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Hu{4tHH^HHH[tHtHX|HH[]rH[]HYff.UHSPHHGuH H{蠅Hu{4tH]HޙHHH[tHtH{HH[]H[]HYff.UHAVSHHGuH H{Hu{4tHH]HHLsAtItLV{LvH[A^]jHXf.UHw0]UHSPHWGG dCuH[]HHH[]EDUHAWAVAUATSPHHIHOHML=Iv(HL9HtH;D(L`Ey:A $AAŀx HIHDIAff.DȃKIcLA8A4$ID$@yHHƀH΄QILIvUAA4$ID$@yHHƀH΄!ILIvAA$ID$HH€HʄILAuzA~4LAF4IFHH+p9Au?A~4pLAF4IFHHppHIF(@EDHEH0@twHHDLH  LIVLIVLxHtWAV HH+s9stWS\H#PH}/HH?H?5Mt ADkPE1LH[A\A]A^A_]Å~ HKHHDI L @UHAWAVSPIHIHGHt}I9vCHw$HfLH HIGCHv܉ɀKHHKHH=@r*Hfffff.KHHH?HwIGHtwI9vCHw$H`LH<HIGCHv܉ɀKHHKHH=@r$HKHHH?HwAG txI9vCw#HbLH辟HAG CvHcȉ€ʀSHKH=@r(Hfff.KHHH?HwAG4tt/Iw@tIUIw(V(HM6HAG4uIw(V HMHIw@uHH[A^A_]HVu H HHVHvIH)LcL9|HLsLLHH[A^A_]ןUHG(]UHG ]UHAVSHHGHttHHL4IƉIHCHtHHHHHIHcC HtHHHHIHIIƋC4tu;H{(E1HCHuH{(BIƃIIIHKu Ds0L[A^]HAuHHAIf.UH]fDUHAWAVAUATSHIHHFHtHCIFHtHCAF tC AF4t#{4uL{(D{4uPH{(HC4HCHH7`jIHC(A~4uTIN(DaEuWHC4HCHHjHHC(A~4Iv(H DaEtQIH}Li IDHMIO DE+GH}HLDoHMEgEgIG D9 }D Hq@tIHHL/Iv@u%H[A\A]A^A_]H5_Iv@tHHHHH[A\A]A^A_]H?H?fffff.H9t?UHAVSHIWGG [AFt I~KLH[A^]ff.UH]UHHGHNHOHFGNOFG N O F 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AAŀx HHHDgHAՐHMDuqA u@LHHHH+s9sS\HHEuAu%H}pLHHE?ff.EtKDt@I4$@t%HHDHHHEH/LYuHHMHtADkPHHH;EtF1HHH;Eu4HH[A\A]A^A_]Å~ HKHHDHHHH;EtUHAWAVAUATSPIIH_DoD;ouEeHDrIGN4 IGN4EeD#H[A\A]A^A_]UHAWAVAUATSPHIIDg EL93A IFAID MgIcGM,fIM9sdL93vOI $IAHv AHHAFIH@rHANHAIH=?HwHLYIIw@uLH[A\A]A^A_]HVuNH HMHLIA IFA!DʀʀAH?wLpHVHvHL)LcL9|LLnMjHLH[A\A]A^A_]˔ff.UHSPHHnHHtHcHHHHIHH1K HHSu C$H[]HJuHHJH@UHAWAVSPIHvt3H{Lc{DypAFCIL{IvIcVHLIv@u H[A^A_]HHHHH[A^A_]UHAWAVSPIHvt3H{Lc{DoAFCIL{IvIcVHLIv@u H[A^A_]HHHHH[A^A_]CUHAWAVATSH9teIIGH_GtHAvt4IMcgDYoAFAGIMgIvIcVHLgIv@u [A\A^A_]HHH[A\A^A_]f.UH]UHHGHNHOHFGNOF]f.UHSPH0HCH1HC+]@G@,@+HH[]fff.UHHHHDHGHqHHwWGG(]ffff.UHHHHDHGHlqHHwWGG(]ffff.UHAWAVAUATSPIHHGH%qHWGHG D~EtGLkMf LD_IHK DD+CLHLDcD{D{HC D98}D8C(Iv@tH{HHH[A\A]A^A_]IH_dLoUH]&fDUHSPHHGuH H{poHt:HHHH[tHtHeHH[]H[]H{ tH{H?u+HBHBfUH]ffDUHSPHRHH[]l@UHw(]UHAWAVATSHDwE~5L{ E1M9t K|IGGtHMCCu [A\A^A_]HH[A\A^A_]!UHAWAVAUATSPHIIHGHELgffff.L;+E}IMEyAAǀxYILA tiED HEH0@HHDHHjIHuLD"HAA ufDHIf.IIF Ht$IcN;}QAVHt%fffff.I<$[LHHHLHtbIH9A} tLH+s9stRS\HIHH}I lLH Ht AD{P1HH[A\A]A^A_]Å~ HCHLDLffffff.UHAWAVAUATSPIHIDgEt:E1ffff.IG McJtV$HM!HAE9uIw@uHH[A\A]A^A_]HVu H HHVHvIH)LcL9|HLuLLHH[A\A]A^A_]ՌDUHG$]UHAWAVAUATSPLcwH}HG LxHLDMN$E1K/H{3HtHcHHH HIHHf1ɉC HHSu%K$ȃIIIIM9uHBuHHBHE1HUHJuDr(LH[A\A]A^A_]HAuHHAIUHAWAVAUATSPIHD~EtGLcMn ILDHK DD+CLHLD]D{D{HC D98}D8Iv@uH[A\A]A^A_]HHHHH[A\A]A^A_]UHAWAVAUATSPIHD~EtGLcMn ILD HK DD+CLHLDP]D{D{HC D98}D8Iv@uH[A\A]A^A_]HHHHH[A\A]A^A_]qUHAWAVAUATSPH9HIDE~7Mf E1M9t K|IGGtHAFM~AFtLDcEtOMnL}L{ ILD IN DE+FLHLL}DL\EfEfIF D9 }D Hs@uH[A\A]A^A_]HHLH[A\A]A^A_]qUH]UHHGHNHOHFHFHN HWLG HVLF HGHFHWHVHGHO ]f.UHSPH@HCHAHC6HedShapesHH.0@ @@6HH[]fffff.UHHHHDHGHhHHwWGG(]ffff.UHHHHDHGHhHHwWGG(]ffff.UHAWAVAUATSPIHHGH=hHWGHG D~EtGLkMf LDIHK DD+CLHLD_[D{D{HC D98}D8C(Iv@tH{HHyH[A\A]A^A_]IHZLUH]&fDUHSPHHGuH H{fHt:H\zHHH[tHtHV\HH[]pH[]H{ tH{H?uH9Hy9fUH]ffDUHSPHRHH[] @UHw(]UHAWAVATSHDwE~EL{ E1Wffff.M9t K|IGGtHWCCu [A\A^A_]HH[A\A^A_]UHAWAVAUATSPHIIHGHELgffff.L;+E}IMEyAAǀxYILA tiED HEH0@HHDHHIHuLDHAA ufDHIf.IIF Ht$IcN;}QAVHt%fffff.I<$gPLH,HHLHtbIH9A} tLH+s9stRS\HIHH}IbLH Ht AD{P1HH[A\A]A^A_]Å~ HCHLDLffffff.UHAWAVAUATSPIHIDgEt:E1ffff.IG McJtV HMHAE9uIw@uHH[A\A]A^A_]HVu H HHVHvIH)LcL9|HLLLHH[A\A]A^A_]eDUHHcGHW HJHHDHH1fDL1MHMtsIMO IIMPMtIMOI‰IMMXAu=EH EAEGAIALLHH9u0E1MPMuIESAuIMSM1HWuG(]HJuHHJHȉG(]UHAWAVAUATSPIHD~EtGLcMn ILD<HK DD+CLHLDUD{D{HC D98}D8Iv@uH[A\A]A^A_]HHHHH[A\A]A^A_]UHAWAVAUATSPIHD~EtGLcMn ILDHK DD+CLHLDUD{D{HC D98}D8Iv@uH[A\A]A^A_]HHHHH[A\A]A^A_]UHAWAVAUATSPH9HIDE~GMf E1Wfffff.M9t K|IGGtH`WAFM~AFtL@DcEtOMnL}L{ ILDIN DE+FLHLL}DSEfEfIF D9 }D Hs@uH[A\A]A^A_]HHLH[A\A]A^A_]UH]UHHGHNHOHFHFHN HWLG HVLF HGHFHWHVHGHO ]f.UHSPH@HCHAHC0@ @@0HH[]fff.UHHG(]fDUHHG(]fDUHAWAVATSIHHOHHIu.S<t1uOHuALc(Mt8L &L8S<uuxHuLc(MtLXLC<Mt8IVuHtH1I9tLLWIC<Ls([A\A^A_]HHueHHufff.UHAVSHG<t-uVHKHHuRHuALs(Mt8LC&HKHHu4HuLs(MtL{L;C<[A^]HHuHHuUHAWAVATSIHHOHHIu.S<t1uOHuALc(Mt8L&L8S<uuxHuLc(MtLLC<Mt8IVuHtH1I9tLL*VIC<Ls([A\A^A_]HHueHHufff.UHHHHDHGH<]HHGHwHG HG8G@]fff.UHHHHDHGH\HHGHwHG HG8G@]fff.UHAWAVAUATSHIHHGH\HWGvt%H{ 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LO==============================================.=========V====~=====================,=========T=========|================D========l=================(========^===================================&=========N=========v===========================================================================>=========f==============================================================================================================================================================================================<====d==================================== ====H====~=========================== =========V=====================.====d==========================<====r====================================<====d===============,====b=====================:====p================H====~==========================:====b==============8=====`==============================(====P====x===================================@===================h==============================>====t====================="====J==============================.====V====~==============================F====n========================================6====^====================&=================N================================L=================================================t============================================================AF(C(IL{0Iv0IcV(HLvAv@t3H{@Lc{@DAF@C@IL{HIvHIcV@HLrvAvXt3H{XLc{XDAFXCXIL{`Iv`IcVXHL7vHoI9IFpHt?H{pHu#HCHHR+HHCpIFpHH5nHEmIFxHt?H{xHu#HCHHHHCxIFxHH5nnHE%IHtHIHtHIHtHAtƃAtƃA3t22uHW3uiHHVǃ2HCHH贏HHA2IuIv@t}Hǃ3HCHH跏HA3IQtPHq@tHHHHxIv@u!H[A^A_]H5lRuIv@tHHHHH[A^A_]ZxH? H?HA3cH FlQ_]H?H?fffff.UHSPHxuHH[]H"ǃxHCHHu:HH[]H?UHAWAVAUATSPIHvt3H{Lc{DuAFCIL{IvIcVHLrAv(t3H{(Lc{(D:AF(C(IL{0Iv0IcV(HLrAFCTHCHHHHCHA~T+IvH?sIv@%H$>CT HCHHEEIHCHA~T MnHAut4IMcgD AEAGIMgIuIcUHLuqIu@tsIHHL]H=CTHCHHqHCHA~TINHQtPHq@tHHHHfuIv@u%H[A\A]A^A_]H5>irIv@tHHHHH[A\A]A^A_]uL-nlAuH?fH?H?赋HCHA~TDH hQ=;ffffff.UHSPHuHH[]H5ǃHCHHu藃HH[]H?@UHSPHuHH[]HǃHCHHu藏HH[]H?@UHAVSIHHLjH9IFHt?H{Hu#HCHHiHHCIFHH5gHE諵IFHt?H{Hu#HCHH!HHCIFHH5gHEcIF HtHC IF(HtHC(A~0tC0A~1tC1Iv@u[A^]HHHH[A^]rH?6H?vfff.UHSPHuHH[]HUǃHCHHuwHH[]H?@UHAVSIHHiH9IFHt?H{Hu#HCHHHHCIFHH5tfHE+IFHt;H{HuHCHHuw襉HHCIFHH50fHEIF HtHC IF(HtHC(A~0tC0Iv@u[A^]HHHH[A^]{qH?EH?fffff.UHSPHuHH[]HǃHCHHuǍHH[]H?@UHAVSIHHhH9 IFHt?H{Hu#HCHHXyHHCIFHH5eHE軲IFHt?H{Hu#HCHH1HHCIFHH5dHEsIF Ht?H{ Hu#HCHHHHC IF HH5tdHE+IF(Ht?H{(Hu#HCHH衇HHC(IF(HH5,dHEIF0HtHC0A~8tC8A~9tC9fAnF?fffff.UHSPHuHH[]HǃHCHHuHH[]H?@UHHv@u]HHH]>fffff.UHSPHuHH[]H%ǃHCHHuGwHH[]H?@UHHv@u]HHH]>fffff.UHSPHuHH[]HǃHCHHurHH[]H?@UHHv@u]HHH]=fffff.UHSPHuHH[]HǃHCHHugrHH[]H?@UHHv@u]HHH]<fffff.UHSPHuHH[]HuǃHCHHu'rHH[]H?@UHHv@u]HHH]n<fffff.UHSPHuHH[]HǃHCHHuqHH[]H?@UHHv@u]HHH];fffff.UHSPHuHH[]HUǃHCHHuqHH[]H?@UHHv@u]HHH]N;fffff.UHSPHuHH[]HǃHCHHugqHH[]H?@UHHv@u]HHH]:fffff.UHSPHuHH[]H5ǃHCHHu'qHH[]H?@UHHv@u]HHH].:fffff.UHSPHuHH[]HǃHCHHupHH[]H?@UHHv@u]HHH]9fffff.UHSPHuHH[]HǃHCHHupHH[]H?@UHHv@u]HHH]9fffff.UHSPHuHH[]HǃHCHHugpHH[]H?@UHHv@u]HHH]~8fffff.UHSPHuHH[]HǃHCHHu'pHH[]H?@UHHv@u]HHH]7fffff.UHSPH uHH[]Heǃ HCHHuoHH[]H?@UHHv@u]HHH]^7fffff.UHSPHuHH[]HǃHCHHuHH[]H?@UHHv@u]HHH]6fffff.UHSPHuHH[]HEǃHCHHu觊HH[]H?@UHFtGHv@u]HHH]46@UHSPH/uHH[]Hǃ/HCHHuIHH[]H?@UHfnFf~tf~GHv@u]HHH]5fff.UHSPH4uHH[]Hǃ4HCHHuGIHH[]H?@UHfnFf~tf~GHv@u]HHH]4fff.UHSPH9uHH[]Huǃ9HCHHuGHH[]H?@UHfnFf~tf~GHv@u]HHH]\4fff.UHSPH;uHH[]Hǃ;HCHHugGHH[]H?@UHfnFf~tf~GHv@u]HHH]3fff.UHSPH>uHH[]H5ǃ>HCHHuEHH[]H?@UHfnFf~tf~GHv@u]HHH]3fff.UHSPH@uHH[]Hǃ@HCHHuEHH[]H?@UHfnFf~tf~GHv@u]HHH]|2fff.UHSPHHuHH[]HǃHHCHHuFHH[]H?@UHHv@u]HHH]1fffff.UHSPHMuHH[]HeǃMHCHHuFHH[]H?@UHHv@u]HHH]^1fffff.UHSPHRuHH[]HǃRHCHHuGFHH[]H?@UHHv@u]HHH]0fffff.UHSPHWuHH[]HEǃWHCHHuDHH[]H?@UHHv@u]HHH]>0fffff.UHSPHauHH[]HǃaHCHHulHH[]H?@UHHv@u]HHH]/fffff.UHSPHfuHH[]H%ǃfHCHHukHH[]H?@UHHv@u]HHH]/fffff.UHSPHkuHH[]HǃkHCHHujHH[]H?@UHHv@u]HHH].fffff.UHSPHpuHH[]HǃpHCHHuiHH[]H?@UHHv@u]HHH]-fffff.UHSPHuuHH[]HuǃuHCHHuGfHH[]H?@UHHv@u]HHH]n-fffff.UHSPHzuHH[]HǃzHCHHukHH[]H?@UHHv@u]HHH],fffff.UHSPHuHH[]HUǃHCHHuiHH[]H?@UHHv@u]HHH]N,fffff.UHSPHuHH[]HǃHCHHuiHH[]H?@UHHv@u]HHH]+fffff.UHSPHuHH[]H5ǃHCHHuhHH[]H?@UHHv@u]HHH].+fffff.UHSPHuHH[]HǃHCHHu}HH[]H?@UHAWAVSPIHvt3H{Lc{DAFCIL{IvIcVHL&Iv@u H[A^A_]HHHHH[A^A_]C*UHSPHuHH[]HǃHCHHu7jHH[]H?@UHHFHtHGHv@u]HHH])UHSPHuHH[]H5ǃHCHHugHH[]H?@UHHFHtHGHv@u]HHH]!)UHSPHuHH[]HǃHCHHuwgHH[]H?@UHHFHtHGFtGHv@u]HHH](UHSPHuHH[]HǃHCHHu'gHH[]H?@UHHv@u]HHH]'fffff.UHSPHuHH[]HuǃHCHHufHH[]H?@UHFtGHv@u]HHH]d'@UHSPHuHH[]HǃHCHHuXHH[]H?@UHHFHtHGHv@u]HHH]&UHSPHuHH[]HUǃHCHHufHH[]H?@UHHFHtHGHv@u]HHH]A&UHSPHuHH[]HǃHCHHueHH[]H?@UHHFHtHGFtGHv@u]HHH]%UHSPHuHH[]H%ǃHCHHuWWHH[]H?@UHAWAVSPIHvt+H{Lc{DiiAFCL{IvIcVL Iv@u H[A^A_]HHHHH[A^A_]$ff.UHSPHuHH[]HEǃHCHHuVHH[]H?@UHHFHtHGHFHtHGHv@u]HHH]$$@UHSPHuHH[]HǃHCHHurHH[]H?@UHAWAVSPIHvt3H{Lc{D虺AFCIL{IvIcVHLIF(HtHC(IF0HtHC0Iv@u H[A^A_]HHHHH[A^A_])#fUHSPHuHH[]HǃHCHHuTHH[]H?@UHHFHtHG~tGHv@u]HHH]"UHSPHuHH[]HǃHCHHuRHH[]H?@UHAWAVSPIHvt3H{Lc{DAFCIL{IvIcVHLhIv@u H[A^A_]HHHHH[A^A_]!UHSPHuHH[]H%ǃHCHHurHH[]H?@UHAWAVSPIHvt3H{Lc{DyAFCIL{IvIcVHLAv(t+H{(Lc{(D.eAF(C(L{0Iv0IcV(LUAv8t3H{8Lc{8D AF8C8IL{@Iv@IcV8HLAvPt+H{PLc{PDdAFPCPL{XIvXIcVPLAv`t3H{`Lc{`D蝢AF`C`IL{hIvhIcV`HLAvxt1H{xLc{xDRdAFxCxLIIcVxLsIv@u H[A^A_]HHHHH[A^A_]fffff.UHSPHuHH[]H%ǃHCHHuqHH[]H?@UHAWAVSPIHvt+H{Lc{DicAFCL{IvIcVLAv t3H{ Lc{ DFAF C IL{(Iv(IcV HLUAv8t+H{8Lc{8DbAF8C8L{@Iv@IcV8L"AvHt3H{HLc{HDؠAFHCHIL{PIvPIcVHHLAv`t+H{`Lc{`DbAF`C`L{hIvhIcV`LIv@u H[A^A_]HHHHH[A^A_]ffffff.UHSPHuHH[]HeǃHCHHurHH[]H?@UHHFHtHGHFHtHGHF HtHG Hv@u]HHH]7UHSPHuHH[]HǃHCHHujHH[]H?@UHHFHtHGHFHtHG~ tG Hv@u]HHH]f.UHSPHuHH[]HǃHCHHujHH[]H?@UHHFHtHG~tGHv@u]HHH]UHSPHuHH[]HeǃHCHHuiHH[]H?@UHHFHtHG~tGHv@u]HHH]GUHSPHuHH[]HǃHCHHuG7HH[]H?@UHAVSIHHH9IFHt?H{Hu#HCHHY2HHCIFHH5HE\IFHt;H{HuHCHHuw2HHCIFHH5HEW\IF HtHC IF(HtHC(A~0tC0Iv@u[A^]HHHH[A^]H?EH?fffff.UHSPHuHH[]HUǃHCHHugJHH[]H?@UHAVSIHHlH9IFHt?H{Hu#HCHH0HHCIFHH5t HE+[IFHt?H{Hu#HCHH0HHCIFHH5, HEZIF HtHC IF(HtHC(A~0tC0A~1tC1A~2tC2A~3tC3Iv@u[A^]HHHH[A^]VH? H?`fDUHSPH)uHH[]Hǃ)HCHHukHH[]H?@UHHv@u]HHH]fffff.UHSPH.uHH[]H5ǃ.HCHHu7JHH[]H?@UHAWAVSPIHvt3H{Lc{D)AFCIL{IvIcVHLHI9tDIF(Ht;H{(HuHCHHuV.HHC(IF(HH5 HEXIv@u H[A^A_]HHHHH[A^A_]H?fffff.UHSPH8uHH[]Hǃ8HCHHuIHH[]H?@UHfnFf~tf~GHv@u]HHH]fff.UHSPH=uHH[]HUǃ=HCHHuGIHH[]H?@UHAWAVSPIHvt3H{Lc{DIAFCIL{IvIcVHLfAnF$f~tf~C$Iv@u H[A^A_]HHHHH[A^A_]UHSPHBuHH[]HeǃBHCHHuHHH[]H?@UHfnFf~tf~GHv@u]HHH]Lfff.UHSPHLuHH[]HžǃLHCHHuNHH[]H?@UHHv@u]HHH]fffff.UHSPHQuHH[]H5ǃQHCHHugNHH[]H?@UHAWAVSPIHvt3H{Lc{D)AFCIL{IvIcVHLIv@u H[A^A_]HHHHH[A^A_]UHSPHVuHH[]HUǃVHCHHuMHH[]H?@UHHv@u]HHH]Nfffff.UHSPH`uHH[]Hżǃ`HCHHug_HH[]H?@UHAWAVSPIHvt3H{Lc{DAFCIL{IvIcVHL( A~$tC$Iv@u H[A^A_]HHHHH[A^A_]XUHSPHeuHH[]HջǃeHCHHu\HH[]H?@UHAWAVSPIHvt3H{Lc{D)AFCIL{IvIcVHL8 Iv@u H[A^A_]HHHHH[A^A_]sUHSPHjuHH[]HǃjHCHHu'\HH[]H?@UHHFHtHGHv@u]HHH]UHSPHouHH[]HeǃoHCHHug\HH[]H?@UHHv@u]HHH]^fffff.UHSPHtuHH[]HչǃtHCHHug[HH[]H?@UHAWAVSPIHvt3H{Lc{D)AFCIL{IvIcVHL8 Iv@u H[A^A_]HHHHH[A^A_]sUHSPHyuHH[]HǃyHCHHuG[HH[]H?@UHHv@u]HHH] 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D(L`EyA $AAŀxYHIDtftu|AuvA4$ID$@yHHƀH΄ILIv|HDCIADuAuA$AF?fEt{DtpI7@tZHHDLHQMHTLAIVH=HH+s9stFS\H9?L蕳HMt ADkPE1LH[A\A]A^A_]Å~ HKHHDIfUHAWAVSPIHIHGHt}I9vCHw$HfLHӼHIGCHv܉ɀKHHKHH=@r*Hfffff.KHHH?HwAGtI9vOCHAGtI9vLCHIw@uHH[A^A_]HVu1H H0LHҼHAGLHҼHAGHVHvIH)LcL9|HLd)LLHH[A^A_]ӼUHHGHtHHHHH1HHHDHAHDHWuG ]HJuHHJHȉG ]DUHHFHtHGfnFf~tf~GfnFf~tf~GHv@u]HHH],ffff.UHAWAVSPH9taIIWGH_GtHr,IFHtIOHfAnFf~tfA~GfAnFf~tfA~GIv@u H[A^A_]HHHH[A^A_]K,ff.UH]UHHGHNHOHFGNOF]f.UHSPH@%HCHAHC0tJ@ YJ@>J@0HH[]fff.UHHHHDHGHl HHGHwG WG(G8]fffff.UHHHHDHGH HHGHwG WG(G8]fffff.UHAVSIHHGH HWGvt%H{AFH{CIvIcVH@&C WC(C8Iv@tH{HH~*H(AF([A^]IHL!f.UHAVSIHHGH HWGvt%H{AFH{CIvIcVH%C WC(C8Iv@tH{HH)H(AF([A^]IHHHH{@tLM DUHSPHHGuH H{`Hu H{ýHHHH[tHtH蝤HH[]"H[]HUHAVSHHGuH H{Hu H{ýH2HHLsAtItL+LK"H[A^]?"Hoffffff.UHw8]UHGWG(Gu]H],(fff.UHAWAVAUATSHHHIH >H HMHuLHOHML%1AeAF0HHMHH;D(HHEy: AAŀx HHHDC;HAffff.HMDȃIcLA1HA@yHHƀHքxFHHHEIv(TAAF40A"uULHH=Hj9HEIV(HH+s9sS\H1HEAŃ u'H}ޥLHHEDffff.EtODtDHEH0@t%HHDHH-DHEHp0H}襪HHMHtADkPH!<HH;EtF1H <HH;Eu4HH[A\A]A^A_]Å~ HKHHDHH;HH;Et ^UHAWAVAUATSPIIH_DoD;ouEeHD诼IGN4 IGN4EeD#H[A\A]A^A_]UHAWAVAUATSPHIIHG(Ht{L93vAAFHw&IbHLʼIIG(AAFHvډɀANHHANIH=@r"H@ANHAIH?HwAG0tL93AAFIAG4tL93 AAFIEg EL93A"IFAID MgIcGM,ff.IM9sdL93vOI $IAHv AHHAFIH@rHANHAIH=?HwHLȼIIw@uLH[A\A]A^A_]HVu|H H{HLȼIAG0HLȼIAG4HLiȼIA"IFADʀʀAH?wLpHVHvHL)LcL9|LLM@>f@0ms@2HH[]fDUHHHHDHGHTHWGG ]ÐUHHHHDHGH$HWGG ]ÐUHAVSIHHGHHWGHG Hv@tH{HHgHAF[A^]IHLUHAVSIHHGHHWGHG Hv@tH{HHHAF[A^]IH$HHH{@tLvfffff.UHAVSIH_tI^H聣HHϷHItHtH͙H[A^][A^]Hw@UHAVSHLwAtLsL M6HnHHAtItLkLH[A^]Hvffffff.UHw ]UHWGGu]H]sUHAWAVAUATSPHHIL AA$AFILH; D(L`EyA $AAŀxYHIDtftu|AuvA4$ID$@yHHƀH΄ILIv|HDd0IADuAuA$AF?fEt{DtpI7@tZHHDLH1:HTL.IVH=HH+s9stFS\Hh&?LuHMt ADkPE1LH[A\A]A^A_]Å~ HKHHDIfUHAWAVSPIHIHGHt}I9vCHw$HfLHHIGCHv܉ɀKHHKHH=@r*Hfffff.KHHH?HwAGtI9vOCHAGtI9vLCHIw@uHH[A^A_]HVu1H H0LHпHAGLH輿HAGHVHvIH)LcL9|HLDLLHH[A^A_]UHHGHtHHHHH1HHHDHAHDHWuG ]HJuHHJHȉG ]DUHHFHtHGfnFf~tf~GfnFf~tf~GHv@u]HHH]ffff.UHAWAVSPH9taIIWGH_GtHRIFHtIOHfAnFf~tfA~GfAnFf~tfA~GIv@u H[A^A_]HHHH[A^A_]+ff.UH]UHHGHNHOHFGNOF]f.UHSPH@mHCHAHC37@ 7@7@/rams@3HH[]DUHHHHDHGH<HWGG ]ÐUHHHHDHGH HWGG ]ÐUHAVSIHHGHHWGHG Hv@tH{HHHAF[A^]IH[LkUHAVSIHHGHlHWGHG Hv@tH{HHgHAF[A^]IHHHH{@tL fffff.UHAVSIH_tI^HHH?HItHtH=H[A^]Y[A^]Ho@UHAVSHLwAtLsL萛M6HޯHHAtItLۑLH[A^]Hoffffff.UHw ]UHWGGu]H]UHAWAVAUATSPHHIL AA$AFILH; D(L`EyA $AAŀxYHIDtftu|AuvA4$ID$@yHHƀH΄ILIv|HD(IADuAuA$AF?fEt{DtpI7@tZHHDLH2HTL.'IVH=HH+s9stFS\H?LHMt ADkPE1LH[A\A]A^A_]Å~ HKHHDIfUHAWAVSPIHIHGHt}I9vCHw$HfLHHIGCHv܉ɀKHHKHH=@r*Hfffff.KHHH?HwAGtI9vOCHAGtI9vLCHIw@uHH[A^A_]HVu1H H0LH@HAGLH,HAGHVHvIH)LcL9|HLLLHH[A^A_]UHHGHtHHHHH1HHHDHAHDHWuG ]HJuHHJHȉG ]DUHHFHtHGfnFf~tf~GfnFf~tf~GHv@u]HHH]=ffff.UHAWAVSPH9taIIWGH_GtHIFHtIOHfAnFf~tfA~GfAnFf~tfA~GIv@u H[A^A_]HHHH[A^A_]ff.UH]UHHGHNHOHFGNOF]f.UHSPH@ HCHAHC1\0@ A0@&0f@0sHH[]f.UHHHHDHGH$HHGHwG WG(G8]fffff.UHHHHDHGHHHGHwG WG(G8]fffff.UHAVSIHHGHHWGvt%H{AFH{CIvIcVH C WC(C8Iv@tH{HHH(AF([A^]IHRLbf.UHAVSIHHGHHWGvt%H{jAFH{CIvIcVH C WC(C8Iv@tH{HHH(AF([A^]IHKHHH{@tLDUHSPHHGuH H{谓Hu H{BHHHH[tHtH퉻HH[]H[]H0gUHAVSHHGuH H{?Hu H{ѨHHHLsAtItL{LH[A^]Hfffffff.UHw8]UHGWG(Gu]H]| fff.UHAWAVAUATSHHHIH g#H HMHuLHOHML%1AeAF0HHMHH;D(HHEy: AAŀx HHHD HAffff.HMDȃIcLA1HA@yHHƀHքxFHHHEIv(TAAF40A"uULHH"HHEIV(HH+s9sS\HbHEAŃ u'H}.LHHEDffff.EtODtDHEH0@t%HHDHH})HEHp0H}HHMHtADkPHq!HH;EtF1H]!HH;Eu4HH[A\A]A^A_]Å~ HKHHDHH)!HH;Eta^UHAWAVAUATSPIIH_DoD;ouEeHDIGN4 IGN4EeD#H[A\A]A^A_]UHAWAVAUATSPHIIHG(Ht{L93vAAFHw&IbHLlIIG(AAFHvډɀANHHANIH=@r"H@ANHAIH?HwAG0tL93AAFIAG4tL93 AAFIEg EL93A"IFAID MgIcGM,ff.IM9sdL93vOI $IAHv AHHAFIH@rHANHAIH=?HwHL)IIw@uLH[A\A]A^A_]HVu|H H{HL筼IAG0HLЭIAG4HL蹭IA"IFADʀʀAH?wLpHVHvHL)LcL9|LLMHHAtItL;wL[H[A^]OHTffffff.UHw]UHGHGGu]H];ff.UHAWAVAUATSPHHIL ILIvH; D(L`EyA $AAŀxbHIDt{AA4$ID$@yHHƀH΄yL IVHxHD0IADuffffff.AuA$AFIL*fEtdDtYI7@tCHHDLHH=HH+s9stFS\H??LL~HMt ADkPE1LH[A\A]A^A_]Å~ HKHHDIUHAWAVSPIHIHGHt}I9vCHw$HfLHrHIGCHv܉ɀKHHKHH=@r*Hfffff.KHHH?HwAGtI9v8CHIw@uHH[A^A_]HVuH HLHǝHAGHVHvIH)LcL9|HLOLLHH[A^A_]鳞UHHGHtHHH HH1HAHDHWuG]HJuHHJHȉG]UHHFHtHGfnFf~tf~GHv@u]HHH]ffffff.UHAWAVSPH9tUIIGHGH_GtHzIFHtIOHfAnFf~tfA~GIv@u H[A^A_]HHHH[A^A_]gUH]UHHGHNHOHFHGHNHOHFGNOF]fffff.UHSPH@HCHAHC3@ @@/rams@3HH[]DUHHHHDHGHLHHGHwG WG(]DUHHHHDHGH HHGHwG WG(]DUHAVSIHHGHHWGvt%H{AFH{CIvIcVHpC WC(Iv@tH{HHH(AF0CIF(H[A^]IH3oLCff.UHAVSIHHGHHWGvt%H{JAFH{CIvIcVHC WC(Iv@tH{HHH(AF0CIF(H[A^]IH,HHH{@tL~fDUHSPHHGuH H{yHu H{"HӍHHH[tHtHoHH[]H[]HMUHAVSHHGuH H{yHu H{豎HbHHLsAtItL[oL{H[A^]oHLffffff.UHw4]UHGHG(G0Gu]H]T@UHAWAVAUATSHHHIH G H HMHuLLgHHHEIv(H;D(HHEy5 AAŀx HHHDHAHMDtat|A1HA@vHHƀHքWHHEIV(fff.AugLHHAujAF0HHMHHH+s9sS\HUHEAŃuH}!qLHHE4EtKDt@I4$@t%HHDHHHEHx/LuHHMHtADkPHuHH;EtF1HaHH;Eu4HH[A\A]A^A_]Å~ HKHHDHH-HH;EteUHAWAVAUATSPIIH_DoD;ouEeHDIGN4 IGN4EeD#H[A\A]A^A_]UHAWAVAUATSPHIIHG(Ht{L93vAAFHw&IbHL|IIG(AAFHvډɀANHHANIH=@r"H@ANHAIH?HwAG0tL93AAFIEg EL93AIFAID MgIcGM,IM9sdL93vOI $IAHv AHHAFIH@rHANHAIH=?HwHLYIIw@uLH[A\A]A^A_]HVueH HdHLIAG0HLIAIFA DʀʀAH?wLpHVHvHL)LcL9|LLWMSHLH[A\A]A^A_]鴔@UHSPHHHtHcHHH HIHH1ɉC HHC(HtHHHHHHHA{0HDHSu C4H[]HJuHHJHfUHAWAVSPIHvt3H{Lc{DلAFCIL{IvIcVHLHIF(HtHC(fAnF0f~tf~C0Iv@u H[A^A_]HHHHH[A^A_]cUHAWAVATSH9IIGHG(G0H_GtHAvt4IMcgDAFAGIMgIvIcVHLtIF(HtIO(HfAnF0f~tfA~G0Iv@u [A\A^A_]HHH[A\A^A_]UH]UHHGHNHOHFGNOFHG(HN(HO(HF(G0N0O0F0]fffff.UHSPH@HCHAHC5HerParamsHH- @  @ @5HH[]fffff.UHHHHDHGHHWG]UHHHHDHGHHWG]UHAVSIHHGHtHWGHv@tH{HHHAFCIFH[A^]IHdLDUHAVSIHHGHHWGHv@tH{HHHAFCIFH[A^]IHփHHH{@tL(UHAVSIH_tI^HAoHHHItHtHeH[A^][A^]HB@UHAVSHLwAtLsLnM6H.HHAtItL+eLKH[A^]?HoBffffff.UHw]UHGHGGu]H]+ff.UHAWAVAUATSPHHIL ILIvH; 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AAŀx HHHDoHAՐHMDuqA u@LHH=rHH+s9sS\HeHEuAu%H}LHHE?ff.EtKDt@I4$@t%HHDHH yHEH/LHHMHtADkPHqHH;EtF1HqHH;Eu4HH[A\A]A^A_]Å~ HKHHDHHpHH;EtUUHAWAVAUATSPIIH_DoD;ouEeHDݼIGN4 IGN4EeD#H[A\A]A^A_]UHAWAVAUATSPHIIDg EL93A IFAID MgIcGM,fIM9sdL93vOI $IAHv AHHAFIH@rHANHAIH=?HwHLIIw@uLH[A\A]A^A_]HVuNH HMHLWIA IFA!DʀʀAH?wLpHVHvHL)LcL9|LLTMjHLH[A\A]A^A_] ff.UHSPHHKHHtHcHHHHIHH1K HHSu C$H[]HJuHHJH@UHAWAVSPIHvt3H{Lc{DڼAFCIL{IvIcVHLSIv@u H[A^A_]HHHHH[A^A_]XUHAWAVATSH9teIIGH_GtHWAvt4IMcgDڼAFAGIMgIvIcVHL'SIv@u [A\A^A_]HHH[A\A^A_]jWf.UH]UHHGHNHOHFGNOF]f.UHSPH0PHCH1HC-y@y@ty@-HH[]fff.UHHHHDHGHBHG]UHHHHDHGHBHG]UHAVSHGHBHGHv@tHHHH"V[A^]IHLLf.UHAVSHGH"BHGHv@tHHHHU[A^]IHHHH{@t/LMLDUHAVSIH_tI^HaHHHItHtHкH[A^]N[A^]H@UHAVSHLwAtLsLM6HNHHAtItLKкLkNH[A^]_NH菭ffffff.UHw]UHGu]H]ZTf.UHAWAVATSHHIIH;swD8L`EyA $AAǀx=HIEtxDtmI6@tWHHDLHqHuUHDgIAEu/HH+s9st@S\H]i9L HMt AD{PE1L[A\A^A_]Å~ HKHHDI@UHAVSHHw@uH[A^]HFu H HHFHvH 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מ1H9NHOHD]DUHH]UH]f.UH]fDUHH sH]fDUHH]H]UH]f.UH]^fDUHH]UHH ؞1H9NHOHD]DUHHm]UH]f.UH]fDUHH SH]fDUHH=H]UH]f.UH]fDUHH]2fUHH ٞ1H9NHOHD]DUHHM]UHH H_HHϨuH9HMII)MBMMIIRHs<HHyHHdf(HLH?LHf I|eIM 8fDILWff.BB\f_f_If(f(M9|f(f_L9~ Bf_f(Hf(f_H~AAHsE1Pf(f(sHE1fDB _BD_BL_BD_IL9uf(Mt)JfDUHHH6H]<fff.UHH מ1H9NHOHD]DUHHM]UHAWAVAUATSHHIHHEH@HEHHH H tHHHHy1HHMLHHMLEAtHMH L9LEMLuLL4HMLQHI HMMHpMxxLHHMH]HHHMAH}MIvQHMLIM2HMH}L,IxLfffff.LuIHLI9K 0AHMHH9|LHL;M}5HHMEtHMH Lt1H}ffff.1fff.LuHIHHL9`LK 0ALH LM1@f(A_LL9|HL9uHMHH;U|HXH;]}EHIHMEtHMH Lt1HHL9jfff.1@L$ff.LuIHHLH9MILAIvQHMHYH~DHMM,HMLfILI E1f(B_MI9|IM9uHMHH;UkL@L;EH}HHULt1HLH9SDLuIIHLL9MILAIrQHMHYH~DHMM,HMLfILI E1f(B_MI9|IM9uHMHH;UkL@L;EI}HHMH Lt1HLL9PHH[A\A]A^A_]ffff.LuIHLI9tK 0AHMHH9|LHL;M}H||HMLt1H}1fffff.LuIHLI9cK 0AHMHH9|LHL;M}H|HMH Lt1H}1뮿H aHH5\aHeH7ffff.UHAWAVAUATSPIIIPHL-9L(HxL!<HHL11rHHI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHL6fffff.UHAWAVAUATSPIIIPHL-59L(HxL <HHL11rH HI?IHtHPILH[A\A]A^A_]IL+H{HCHt [IHNLvfffff.UHAWAVAUATSPIIIP/HL-u8L(HxL& <HHL11CqHTHI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHLfffff.UHAWAVAUATSPIIIPoHL-7L(HxLf<HHL11pH HI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHLfffff.UHAWAVAUATSPIIIPHL-6L(HxL<HHL11oHHI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHL6fffff.UHAWAVAUATSPIIIPHL-56L(HxL<HHL11oH HI?IHtHPILH[A\A]A^A_]IL+H{HCHt [IHNLv fffff.UHAWAVAUATSPIIIP/HL-u5L(HxL&<HHL11CnHTHI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHL fffff.UHAWAVAUATSPIIIPoHL-4L(HxLf<HHL11mHHI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHL fffff.UHAWAVAUATSPIIIPHL-3L(HxL<HHL11lH HI?IHtHPILH[A\A]A^A_]IL+H{HCHt IHL6 fffff.UHSPHGt H{ HA3HH{HCHHt[] []ffffff.UHSPHGt H{ H2HH{HCHtp HH[]b f.UHSHHH)HHEHFHNu HHEHMH HHH}HMHrHt8FHNIHuHH:HH4)HH;EuHH[]] ffffff.UHAWAVAUATSH8AIIHH(HHEH6IV1MNHEHDž`HDž(HHD$0HeHD$(HHD$ HHD$H(HD$H`H$D$<ALLMHLLStG(ua`umEt H} H(HH;E[H8[A\A]A^A_]H1gIH`@u2Hh?H0j `tHhU EuHhHr HHH( @L(H1L<IH`HDž`Ht @u6LHH@HLS.HL HDž(HDž`HDžWHDžHu@uHUHUHr HHH?@H(uL0&HLIBL0Hr MHLKL,$HLLH HDž Ht u!u-`u9(CH tH `tHhs (H0Z u H(H(ttHH HDž Ht HH(m_(t6H(t%rHH`HDž`Hu (uO`u[EugH(t(H(tH(t H(tH0[`tHhFEtH}7H_UHAWAVAUATSHIHIHN$HHE1ILpL@H HW)HDž ILLP8MuSHHbHx4HH HH8߫HH HH^iHHH)HH9sHH)H v HHMhHt=HLHH1J4*H9H6H4HH9uIHsI|$ u HbHۋ HDžHLP E1Huffffff.HHH9t"ffff.L90t HH9uPH9tKHEHHtHXHHH9LtHPHODM97ID$ J HMHHtHXHHH9t@LtHPH HHHII9HHHuHH쮯HuHH;E1Hu$fH HHHII9HHH9tfL90t HH9uH9uM97ID$ J HMHHtHXHHH9tLxHPlHH HufHH@uHPLH,HPHHrHLu LHH}+@HUH1<H}$@HH}HEHtHt HPHHt HLHH  H H;Mu H[A\A]A^A_]HL(H 1PHH5OH}HHL0L蟍HLH-)HH5s H"HCIHH_H5BHH UH}LHZ#bH5JH00AHJHuH0LfE1H5xHML H0tH@iHAH}PMHHMEuHHHMHLzH0uH}HEHumHur|H@H}HEHtCH aHPHKHFHHu.8H3H.HH}HEHtHt HP}HHt HeHDUHAWAVAUATSHLLIH|HHEHwHIIHHW Hu HH}}@Ls$IH HH}@IMt IuL9uKLBL@HA6HH HHH!i1HMIUHu H;H}LHIj)u5LLHH]H1/HHEHH]Hu*HCpHHHH8HH@HHLPHHXH* H?HH@WH*H(0HH`H8HhL`LuH HLL|Z^H}H`H9tHu HH@fnff8HpHHHljH`s 1foH1foff.AoT6f8f8Hff.UH]f.UH]NfDUHSPH MH HC@HKHHH[]fff.UHHHGFHGHF]UH]f.UH]fDUHAWAVAUATSHHGHLLLL)LIMK4'HJHvH s?K HH5s>H|kHN@UHH g1H9NHOHD]DUHH]UH]f.UH]fDUHSPH(H HCK@HH[]fff.UHHHGOFN]UH]f.UH]fDUHAVSH HH HH)HpA IL;G LWL_MItO H_E4ID84}D4IH9tMIDIL9tL_C\ C8 |L_C C8\ }DC L_C C8\ }C\ [A^]UHH (1H9NHOHD]DUHH]UH]f.UH]fDUHSPH8H HCKS(@HP(H[]@UHHHGOW(FNV(]UH]f.UH]^fDUHHH6H]<fff.UHH 1H9NHOHD]DUHHU]UHAWAVAUATSHHUH9ffo dH}%ffff.HxHH;uH}HHGLHMH@HHEHLwLJ1HELo HW(LHxHHHEMHuwH}HyHWH IAIIL`ILUHUIIIRHPHWHhHHADEHHpHHULHMLHHLHHEN<1ILHILXJ<N$HuE1L@0D@HEBIHIHEL@M9HuwF,}kH}!s 1DAfnf8Hh s1foJHM11foLEAod7f8LHLHMP0ILk0L;{ |HHH;EuH([A\A]A^A_]ᐣ*fff.UHH f1H9NHOHD]DUHH%ީ]UH]f.UH]>߂fDUHB߂H ީH]fDUHHݩH]UH]f.UH]ނfDUHH]@UHH l1H9NHOHD]DUHHީ]UH]f.UH]ނfDUHނH ݩH]fDUHHݩH]UH]f.UH]>ނfDUHHHH DHCH HPH!sE1/Afnff8L@MIIDσI`s E1foIE1fofff.BoTf8ۂfDUHHH6H]<fff.UHH 1H9NHOHD]DUHHU۩]UHAWAVAUATSHHpH@HL@IHH H tHHHHHy 1H IAHEtHEHH;py IHHHHE1ŬILpLH@H HW)HDž ILLP8MuSHHRŬHx4HH HH(HH HHN+HHH)HH9sHH)H荝 v HHMhHt=HLHH1J4*H9H6H4HH9uIHsI|$ u HbHۋ HDžHLP E1Huffffff.HHH9t"ffff.L90t HH9uPH9tKHEHHtHXHHH9LtHPHODM97ID$ J HMHHtHXHHH9t@LtHPH HHHII9HHpHuHHpHuHH;E1Hu$fH HHHII9HHH9tfL90t HH9uH9uM97ID$ J HMHHtHXHHH9tLxHPlHHpHufHH@uHPLH,HPHHrHLu LHH}C@HUH1;H}E@HH}HEHtłHt HPłHHt HłLHH H H;Mu H[A\A]A^A_]HLƂłH !HH5H>HłłHL0LOHL‚HHH5cH H~ł3łIHH^H52 H H}LHZ$H5 H0 cAH HuH0Ls(E1H5 :HrLĂ H0tH@YĂHAH}@HHEuHHHHLjĂH0uH}HEHumHur|H@ÂH}HEHtCH"HPHKHFHHu.8H3H.HH}HEHt|ÂHt HPmÂHHt HUÂH}DUHAWAVAUATSHLLIHlߦHHEHwHIIHHW Hu HH}?@LIHXάHH}A@IMt IuL9uKLLA@HA6HH HHH!i1wH=IUHu H+H}LHIZu5LLHH]H!HHEHH]HeHCpHHHH8HH@HHLPHHXH* H?HH@WH*H(0H}ũH`H8HhL`LuH HLLl^H}H`H9tHu HH@fnff8HpHHHljH`s1fozH1fofffff.AoT6fAoD6fAoL6!Ao\61Aod6AffAoT6QffAoD6afAoL6qfHHuHHHt'fIICofCoTfH Huffvffofqff8Af~@H9utiIIIP@fnf`pfA~L>fHI9ufpUffofrffofqff~ΉL9u%HHDA<@@8GH‰H9uH@8H}HEHt9HۦHH;EuH[A\A]A^A_]Я詿?H HH5 H8H[ HHML9t&IHuH}HEHu@HໂILH}HEHtHH}HEHtmH蛻UHAWAVATSH`HIIHڦHHEEILɬI?IOHQH*H1EH?HMH H IWH*EH پHMLuHUHELuLuHUHL^H}HMH9t Hu HHH٦HH;EuH`[A\A^A_]1 HHML9t IHt ILHuffff.UHAWAVAUATSHXHp٦HHEHHHHIHFL(LpILjȬIMy?LLLѾI*EH?HEKD-H@WH*E(觼H HLxL`LpLh HEHUL}HLL^H}HMH9t Hu HHH}ئHH;EuHX[A\A]A^A_]Ȭ衼HHML9t IHt ILH @UHAWAVAUATSHhHIIHئHHEIL(ǬIHHIVH HxIHJLzH]LHH*EWH*EHH@WH*E8GH HLhA@HX Lx(L`0HEHULuH}HxL&^H}HMH9t Hu HHHצHH;EuHh[A\A]A^A_][4HHML9t IHt 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tLHHHuD1HuLuIFHJ4HH H t'HHHIMx%LLIFHHy/1AMy/k*k1IFHHIN ML!INKII>M>HtHPHH[A\A^A_]HLsHsUHAWAVATSIIHsIH IIL,H?II>M>HtHPHH[A\A^A_]HL8sH`sUHHH HHH@Ht]s]fDUHSPHHP HHHCHtsHH[]sfUHAWAVAUATSHIHHHEHH1ZߝHH H8LsI Lk Mu M LxHߝIHHIL"b1fWf) HDž0HtI IHHsIH N$L0HHHsL(E1E1LL)HLH)v)HEH HMLjL L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}L^1HUH1,HHH H(H)IIM9LIOHHHXH` HH=HH 1HHH HsIHH)sLhLHsIHHsLpLHsHIHsLHHHHXHhFMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8 HxHHxHHDžL9HHfHLtHL H HIIMfWf)HDžLtUHI9JHsHHLL5.7L@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0sL0@HHHH(HEfInfb\f\df(fXfWf)@PHHLL/5OH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt H,sHHDžHt sHHDžHtsHHDžHtsH}HEHtsH Ht H(sHHH;EuuHHĈ[A\A]A^A_]8ɓsH %HH5$HQHs}sH $HH5$HQHswsH 蜅d>sH $HH5$HQHZs8Hb*sH w$HH5E$HNQH s HEYH}sKtHHML9tIHIML}xBt4HHDžHHDžHH4HJ-HHMH`H9t HHt HHHHt HsHHDžHtsHHDžHpHt HpsHHDžHhHt HhVsH}HEHt@sH Ht H(.sHVsfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~|IOH J HH H 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H(sH&sfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~|IOH J HH H u1E1J<IG(H8IG0HH0IG8HsMx&IG@HN,II9;H[A\A]A^A_]ÿsH HH5Q HZ:H,sfUHAWAVAUATSHHUH9H}!fff.HuHH;uH}^HGLM]HHHuL$HGHJ LH H tLHHHuD1HuLuIFHJ4HH H t#HHHIMx!LH=uJ1AMyBB1IFHHIN IL!INKM>HtHPHH[A\A^A_]HLHsHpsUHAWAVATSIIH2sIHxIIL),HII>M>HtHPHH[A\A^A_]HLȻsHsUHHH HHH@Ht]鐻s]fDUHSPHHHHHCHt_sHH[]QsfUHAWAVAUATSHIHzחHHEHH1궝HHH8LsI Lk Mu M LxH蠶IH腒HIL91fWf) HDž0HtI IHH荺sIH N$L0HHHXsL(E1E1LL)HLH)v)HEH HMLL L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}L61HUH1,HĝHH H(H)IIM9LIOHHHXH` HH=HH 1HHH H裸sIHH蹸sLhLHsIHH蕸sLpLH[sHIHqsLHHHHXHhGMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8 HxHHxHHDžL8HHeHLtHL H HIIMfWf)HDžLtUHI9JHxsHHLL5L@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0sL0@HHHH(HEfInfbuf\uf(fXfWf)@PHHLL OH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt H輰sHHDžHt蚰sHHDžHt~sHHDžHtbsH}HEHtLsH Ht H(:sH̗HH;EuuHHĈ[A\A]A^A_]Ƞs@UH]f.UH]鮪sfDUHSPHP譪sH HCKS([8@HP(X8HKHHHHH[]@UHHHGOW(_8FNV(^8HGHHFH]UH]f.UH]sfDUHHH6H]M>HtHPHH[A\A^A_]HL8sH`sUHAWAVATSIIH"sIHh͗IIL,HZII>M>HtHPHH[A\A^A_]HL踧sHsUHHH ͗HHH@Ht]逧s]fDUHSPHH̗HHHCHtOsHH[]AsfUHAWAVAUATSHIHj×HHEHH1ڢHHΝH8LsI Lk Mu M LxH萢IHu~HIL%1fWf) HDž0HtI IHH}sIH N$L0HHHHsL(E1E1LL)HLH)v)HEH HMLvL L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}L"1HUH1},H腳HH H(H)IIM9LIOHHHXH` HH=HH 1HHH H蓤sIHH詤sLhLHosIHH腤sLpLHKsHIHasLHHHHXHhHMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8 fHxHHxHHDžL7HHdHLtHL H HIIMfWf)HDžLtUHI9JHgsHHLL5L@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0sL0@HHHH(HEfInfbaf\af(fXfWf)@PHHLLNH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt H謜sHHDžHt芜sHHDžHtnsHHDžHtRsH}HEHtHGHsw6sH Id辛sH 1HH5HHڛs8HH*脛sH HH5HH蠛s HEYH}sKtHHML9tIHIML}xBt4HHDžHHDžHH4HJ-HHMH`H9t HHt HHHHt HLsHHDžHt*sHHDžHpHt HpsHHDžHhHt Hh֙sH}HEHtsH Ht H(讙sH֖sfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~yIOH J HH H u1E1LLIG(H8IG0HH0IG8H̚sMx&IG@HN,II9>H[A\A]A^A_]ÿØsH 6HH5H HߘsDUHAWAVAUATSHHUH9uH}!fff.HuHH;uH}NHGLMMHHHuL$HGHJ LH H tLHHHuD1HuLuIFHJ4HH H t'HHHIMx%LLIFHHy/1AMyz1IFHHIN ML!INKM>HtHPHH[A\A^A_]HLsH0sUHAWAVATSIIHsIH8IIL,HII>M>HtHPHH[A\A^A_]HL舓sH谐sUHHH ҸHHH@Ht]Ps]fDUHSPHHHHHCHtsHH[]sfUHAWAVAUATSHIH:HHEHH1誎HHϽH8LsI Lk Mu M LxH`IHEjHILr1fWf) HDž0HtI IHHMsIH N$L0HHHsL(E1E1LL)HLH)v)HEH HMLbL L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}L 1HUH1M,HUHH H(H)IIM9LIOHHHXH` HH=HH 1HHH HcsIHHysLhLH?sIHHUsLpLHsHIH1sLHHHHXHhFMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8 HxHHxHHDžL9HHfHLtHL H HIIMfWf)HDžLtUHI9JH9sHHLL5L@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0щsL0@HHHH(HEfInfbMf\Mf(fXfWf)@PHHLLNH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt H|sHHDžHtZsHHDžHt>sHHDžHt"sH}HEHt sH Ht H(sHCHH;EuuHHĈ[A\A]A^A_]xsH oԗHH5=ԗHFHs͇sH @ԗHH5ԗHHswsH 4d莇sH ԗHH5ӗHH誇s8H4*TsH ӗHH5ӗHHps HEYH}sKtHHML9tIHIML}xBt4HHDžHHDžHH4HJ-HHMH`H9t HHt HHHHt HsHHDžHtsHHDžHpHt HpЅsHHDžHhHt Hh覅sH}HEHt萅sH Ht H(~sH覂sfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~|IOH J HH H u1E1J<IG(H8IG0HH0IG8H虆sMx&IG@HN,II9;H[A\A]A^A_]ÿ萄sH їHH5ЗHH謄sfUHAWAVAUATSHHUH9uH}!fff.HuHH;uH}NHGLMMHHHuL$HGHJ LH H tLHHHuD1HuLuIFHJ4HH H t#HHHIMx!LH>uJ1AMySN1IFHHIN IL!INKM>HtHPHH[A\A^A_]HLsH}sUHAWAVATSIIHsIHIIL蹌,HII>M>HtHPHH[A\A^A_]HLXsH|sUHHH HHH@Ht] s]fDUHSPHHpHHHCHt~sHH[]~sfUHAWAVAUATSHIH HHEHH1zzHHH8LsI Lk Mu M LxH0zIHVHILB0fWf) HDž0HtI IHH~sIH N$L0HHHsL(E1E1LL)HLH)v)HEH HMLNL L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}L0HUH1,H%HH H(H)IIM9LIOHHHXH` HH=HH 1HHH H3|sIHHI|sLhLH|sIHH%|sLpLH{sHIH|sLHHHHXHhHMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8H HHxHHxHHDžL7HHdHLtHL H HIIMfWf)HDžLtUHI9JHzsHHLL5VٚL@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0usL0@HHHH(HEfInfb|9f\9f(fXfWf)@PHHLLONH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt HLtsHHDžHt*tsHHDžHttsHHDžHtssH}HEHtssH Ht H(ssHHH;EuuHHĈ[A\A]A^A_]XdssH ?HH5 HHssssH HH5޿HHsswssH d^ssH ѿHH5HHzss8H *$ssH HH5eHnH@ss HEYH}rsKtHHML9tIHIML}xBt4HHDžHHDžHH4HJ-HHMH`H9t HHt HHHHt HqsHHDžHtqsHHDžHpHt HpqsHHDžHhHt HhvqsH}HEHt`qsH Ht H(NqsHvnsfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~|IOH J HH H u1E1J<IG(H8IG0HH0IG8HirsMx&IG@HN,II9;H[A\A]A^A_]ÿ`psH ӼHH5HH|psfUHAWAVAUATSHHUH9H}!fff.HuHH;uH}^HGLM]HHHuL$HGHJ LH H tLHHHuD1HuLuIFHJ4HH H t#HHHIMx!LH=uJ1AMy#1IFHHIN IL!INKM>HtHPHH[A\A^A_]HLksHhsUHAWAVATSIIHksIHȐIILyx,HΚII>M>HtHPHH[A\A^A_]HLksH@hsUHHH bHHH@Ht]js]fDUHSPHH0HHHCHtjsHH[]jsfUHAWAVAUATSHIHʆHHEHH1:fHH_H8LsI Lk Mu M LxHeIHAHIL0fWf) HDž0HtI IHHisIH N$L0HHHksL(E1E1LL)HLH)v)HEH HMLJ:L L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}Lo0HUH1x,HpHH H(H)IIM9LIOHHHXH` HH=HH 1HHH HgsIHH hsLhLHgsIHHgsLpLHgsHIHgsLHHHHXHhGMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8 HxHHxHHDžL8HHeHLtHL H HIIMfWf)HDžLtUHI9JHesHHLL5~ƚL@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0aasL0@HHHH(HEfInfb<%f\D%f(fXfWf)@PHHLLNH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt H `sHHDžHt_sHHDžHt_sHHDžHt_sH}HEHt_sH Ht H(_sH{HH;EuuHHĈ[A\A]A^A_]P_sH HH5ͫH؛H_s]_sH ЫHH5H؛Hy_sw_sH | d_sH HH5_Hh؛H:_s8HB *^sH WHH5%H.؛H_s HEYH}v^sKtHHML9tIHIML}xBt4HHDžHHDžHH4HJ-HHMH`H9t HHt HHHHt H]sHHDžHt]sHHDžHpHt Hp`]sHHDžHhHt Hh6]sH}HEHt ]sH Ht H(]sH6Zsfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~sIOH J HH H u1E1IG(IO0H8LH1IG8H2^sMx&IG@HN,II9DH[A\A]A^A_]ÿ)\sH HH5jHs՛HE\sff.UHAWAVAUATSHHUH9UH}!fff.HuHH;uH}.HGLM-HHL$HGHJ LH H HutLHHHuD1HuLuIFHJM>HtHPHH[A\A^A_]HLWsHTsUHAWAVATSIIHrWsIH|IILid,HJII>M>HtHPHH[A\A^A_]HLWsH0TsUHHH R|HHH@Ht]Vs]fDUHSPHH |HHHCHtVsHH[]VsfUHAWAVAUATSHIHrHHEHH1*RHHυH8LsI Lk Mu M LxHQIH-HIL0fWf) HDž0HtI IHHUsIH N$L0HHHWsL(E1E1LL)HLH)v)HEH HML:&L L(MM9M JLJ4J LH)It$fDHyI9 I9 H9 H9LL9t IuH>L @HCHtHHH~L9HuLL)H0 Mt M" HH}L_0HUH1d,HnHH H(H)IIM9LIOHHHXH` HH=HH 1HHH HSsIHHSsLhLHSsIHHSsLpLHSsHIHSsLHHHHXHhHMIǹHֿAL`Lp$HIIDLLIIIM~KDHHKTHHHH„uIuLNLH]H|ILHH H tHHHHL H W1HL H ?1A:H8 fHxHHxHHDžL7HHdHLtHL H HIIMfWf)HDžLtUHI9JHQsHHLL5ֳL@L`3fDLHHHHHH;yHHHHHHHHHHHHXL0QMsL0@HHHH(HEfInfb,f\4f(fXfWf)@PHHLLNH}L9HLLLLHfff.HHLLLLH1HHHHHH;HxHHHt HKsHHDžHtKsHHDžHtKsHHDžHtKsH}HEHtKsH Ht H(zKsHgHH;EuuHHĈ[A\A]A^A_]<|KsH HH5HěHKsMKsH HH5HěHiKswKsH ldKsH HH5OHXěH*Ks8H2*JsH GHH5HěHJs HEYH}fJsKtHHML9tIHIML}xBt4HHDžHHDžHH4HJ-HHMH`H9t HHt HHHHt HIsHHDžHtzIsHHDžHpHt HpPIsHHDžHhHt Hh&IsH}HEHtIsH Ht H(HsH&Fsfffff.UHAWAVAUATSPH9HIIIffff.IIOH0IH9IGLLOM9}_HE1)HHHHIW HJ IIM9t1H~yIOH J HH H u1E1LLIG(H8IG0HH0IG8HJsMx&IG@HN,II9>H[A\A]A^A_]ÿHsH HH5TH]H/HsDUHAWAVAUATSHHUH9uH}!fff.HuHH;uH}NHGLMMHHHuL$HGHJ LH H tLHHHuD1HuLuIFHJ4HH H t'HHHIMx%LLIFHHy/1AMyɛɛ1IFHHIN ML!INKM>HtHPHH[A\A^A_]HLXCsH@sUHAWAVAUATSHHHz_HHEп1CsW@@p@ @0@@@P@`HǀHHHP@p?@xƀHHHHE &HX&Iu&I議&I襦&IW)EHE(9BsHEHH(HMHXHLxL`LhLp HMH5tH}HU蘽*HX|&HPp&I&I &I&IW)`HDžp(AsH`HH(HpHPHL`LhLpLx HhH5L-uH`HX*H5vH臹*H5?}Hx*HA H5PSsH_*HHHv͘HEH HUHuHuH HKHK0HCHSHC@H`Ht Hh@sH}Ht H}@sLuHEMtL9RLy@sH\HH;EuHHĈ[A\A]A^A_]@sHH`HuH}HuHH}HV=sHh@sH}Ht HH}۔H+=sHH}HtH}?sH}赔H=sffff.UHAWAVAUATSHxIIIH[HHEH1@Hxu!IHPL1FIAtvEgy0|?sHEHE1HE)v@v@v@)HML^NE9 H}?s+ ID$XD`DeHEI_ ALpL}DeLx_LTIvH IN HH H LqM IH]LuHULMAuEAAAM9MMMM)I]MIIHHMHM~J4LW>sHMLeN$9I|1HEJsHMM9}HHJHLxlIF H9XHEH)HhH9HMHIHLx/HHIN H9HhHHEH9HMHHHH?H!1HHNHt`HHH)LUIHULMIILII1I?f.OM\HMH9ut#HH)HHEHH?HUH HMHHMI_ A HMLoNHEIFH%II^ L3MHL{MLIH~ HH}9sMx(L9cMDeLL)L9IMH1LHH9MDeMM9MMLHLH?L!1MLNHtVHHH)IHUNMIIMLII1KIDHMH9utHH)IILHMHHMHHMHEHX LpAAHMLKHEIFHII^ L3MHL{MLIH~ HH}W8sMx(L9cMDeLL)L9IMH1LHH9MDeMM9MMLHLH?L!1MLNHtZHHH)IHUNMIIMLII1KIDHMH9utHH)IILHMHHMHHMHEHX LpA3HMLGIvHMIN HHBH3LqM+IH]LuHULMAuEAAAM9MMMM)I\$MIIHHMHM~J4L6sHML}N<)I|1HEJHLxH]0HA L9XHL)HMH9HMHCHLxH]LHHI H9HELH9HMHLH?L!1MLNHtVHHH)HOL ILII1f.CADHMH9utHH)IHLAHEHHEH}cIEHQHH;EuALHx[A\A]A^A_]d%4sH KHH5H"H4s5sHEt H}k4sH1sff.UHHH YHHH@Ht]04s]fDUHSPHHYHHHCHt3sHH[]3sfUHAWAVATSIIH(3sIHLDI>M>HtHPHH[A\A^A_]HL3sH0sffff.UHAWAVAUATSH(IHHOHHEL-XL/Hh@,HHfEkEL{ H}HULLn,LeHEMtAD$t I|$2sL2sEt H}2sMtIE EdtypfEeHKH}HUL2n,LuMCHEtAFt I~2sL}2sEt H}n2sHNHH;EuH([A\A]A^A_]2sIEt H}62sL+H{HCHt2sLE/sffff.UHAWAVATSHHHIIFW)pHEH H[LHL@L9 H<$sIH4HpHxIML8Lp1M&I$ILxHH@LxL9iHXH9X7 H@ HI9rMM)MIICL9( HL)III9LFHH9MCMtlHPLHHhH`M9- LMJ<#sIIHhMLHHPN,JM9uB1N,JM9t3IL$HL)H(sWfIL$IIMIM9uJ4N$ILxMtLI?#sLIMHHJ48HII)I9wHH)H9wIL)HH yHHOHHHHHHHs1L@usMIIN I1ffff.ADALA ADADALALADHIuHML@t.HLH)MMMI)AAAIMMNFM)H9M)LpH HAHu5E11MML8.MH HAHLi Mu HHMH LLpH`!DI$ILxII9MI9IUI9uLL)IIICL9VIII9LFHH9MCMtYHHLhM9lLPJ< sILhLPHHN^LsfDUHAWAVAUATSHHL}@Lu LmH1HHEHhH`D_LPH>HHMkHM8L99YHIJ H@M~.HH1fHc<.HHEHhH`D_LPHHHMlHM8L99ZHIJ H@M~/HH1fHHH`HuH}Hu&H}NTHrHhbrH}HtH}PrH}THorUHAWAVATSHHHlHHEؿ#rW@@p@ @0@@@P@`HǀHHHP@p?@xƀHHHHER'IW)EHEHxHII)H) p8rIHEHEJ HMIIVH9II)II8HH)H IIpILHHIsE1GIIE1DBBLBBLBD BL0BD BL0I@IuII@tBBLBBLJL9tJ H1H0HHH9uHEH5"sH}HUx*Ima&Iek&IW)`HDžprH`HHHpL8L`HhH5|vH`LZx*H5ڰvHt*H5,|Hu*HA H5sHv*HHHڈHEH HUHuHuH HKHK0HCHSHC@H`Ht HhrH}Ht H}rLuHEMtL LrH&HH;EuHHĀ[A\A^A_]KrH} 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;HH5Z;HchH5ppIH}MH5tH ɱtH`LEmMH5(tH(ڌAHqH`H(L*RE1H5cH)9Lpp[pH :HH5:HgHwpH`聛M#pH :HH5d:HmgH?p!H@GH@9O H(tH8pHAH`t8H}8EueHEtH}WpHAH`;8H@8Eu6HH}8HH@8EHLUpMHLHpHp#!H'HL`Mt LhHHMtLpH@Ht HHpHpf.UHAWAVATSIIHpIHIILy)HBeII>M>HtHPHH[A\A^A_]HLpH@pUHHH bHHH@Ht]p]fDUHSPHH0HHHCHtpHH[]pfUHAWAVAUATSHIHHHHEH1>HILHIIGHr HH H IW)@HDžPLj.HHynHL IqmL HL@HPL)HH9nHH= LHH<pMM)LHHLHIL)LVIsIHHLH)H sIIIrHHIMIIIs1QIMIN I1ADALA ADADALALADHIuHL At$HII)MI)ABAJAHA@IM)H9tM)@IFII@IM9uL@HHHPML tLJpL HIHtHuCID$ H8u8H;KHHHPH9oHHHHW)`HDžpLH=wN4LpIH`HLpLg.IHL EJLHIFHE1LDIM9H;t6HHH@LHMILxILMLHL`HfDHHH~I9ILIT$ HHHqH9HEI|H@t]L`ML LL@IM)MIIVHH=dHL)III9LFHH9LCMtI98J<Bpא1JJ JJ4HIM)tIIsL L IHL)H s L IIzIIIMIIIs1JMINI1DLA ADDLALADHIuHL NAt$HII)II)ACAKAIAAL)L9tL)@HCHHBHL9uH@HHHPMtLpH@HHHH)vHHL H t 1II1AALuL}HEH`HDžhH`H1)HGHUHM'MJ4IILHHIWHHHHHHHHLP0MIHLCMIJ HMI1H)@HHIIHHH; ]HHHHH)HIH @@ t 1Hs1JfLM1@AFAADALCDC ADA HMIut1L4H HHLHBBLHHI9IIAt'HH HHH4HH4LM9HVLIHLIE1fK .H J *HLHMI9u~HHH@Ht HHpH9HH;EXHH[A\A]A^A_]ÿpIH@LH5tH $tH`L@9AH5tH}AHqH`HML3FE1H5WH2-LpCipH .HH5.H[Hp:pIH}LH5tH ntH`LEmAH5xtH(*AH/qH`H(LzEE1H5WHy,LpppH .HH5-HZHpH`юMspH -HH5-HZHp!H@藎H@艎C H(tH8pHAH`+H}K,EueHEtH}pHAH`+H@,Eu6HH}+HH@+EHLpMHLpHlp#!H'HL`Mt LhHHMtLpH@Ht HHpHpf.UHAWAVATSIIHpIHIIL)HXII>M>HtHPHH[A\A^A_]HLhpHpUHHH HHH@Ht]0p]fDUHSPHHHHHCHtpHH[]pfUHAWAVAUATSHIHHHHEH1ښHILHIIGHr HH H IW)@HDžPL^].HHyQaHL Iq=aL HL@HPL)HH9nHH= LHH<pMM)LHHLHIL)LVIsIHHLH)H sIIIrHHIMIIIs1QIMIN I1ADALA ADADALALADHIuHL At$HII)MI)ABAJAHA@IM)H9tM)@IFII@IM9uL@HHHPML tLpLڸHIHtHuKID$ H8u@W.ucHHHPH9HHHH5W)`HDžpLH=N4LpIH`HLpLZ.IHL ]JLHIFHE1Lffff.IM9.+u{6HHH@LHMILILMLHL`Hffff.HHH~I9ILIT$ HHHqH9HEI|H@tML`ML LL@IM)MIIVHH=dHL)III9LFHH9LCMtI98J<rp ː1JJ JJ4HIM)tIIsL L IHL)H s L IIzIIIMIIIs1JMINI1DLA ADDLALADHIuHL NAt$HII)II)ACAKAIAAL)L9tL)@HCHHBHL9uH@HHHPMtLpH@HHHH)vHHL H t 1II1AALuL}HEH`HDžhH`H1)HGHHM'MJ4IILHHIWHHHHHHHHLP0MIHLCMIJ HMI1H)@HHIIHHH; ]HHHHH)HIH @@ t 1Hs1JfLM1@AFAADALCDC ADA HMIut1L4H HHLHBBLHHI9IIAt'HH HHH4HH4LM9HVLIHLIE1fK .H J *HLHMI9u~HHH@Ht HH 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LI9u[A^]fDUHAVSHLwGt H LI9u[A^]fDUHAVSHLwGt H LI9u[A^]fDUHHH?H Ht] ]DUHAWAVATSHIL'HI9t!ILI@t HxS I9uI?I[A\A^A_]; UHAVSHLwGt H LI9u[A^]fDUHAVSHLwGt H LI9u[A^]UHAVSHLwGt H LI9u[A^]fDUHAWAVATSHIL'HI9t!ILI@t Hxs I9uI?I[A\A^A_][ UHAVSHLwGt H; LI9u[A^]fDUHAVSHLwGt H LI9u[A^]fDUHHH?H Ht] ]DUHAWAVATSHIL'HI9t!ILI@t Hx I9uI?I[A\A^A_] UHAVSHLwGt Hk LI9u[A^]fDUHAVSHLwGt H; LI9u[A^]fDUHHH?H Ht ]fDUHHH?H Ht ]UHAVSHLwGt H LI9u[A^]fDUHAWAVATSHIL'HI9t!ILI@t Hx I9uI?I[A\A^A_]k UHAWAVATSHIL'HI9t!ILI@t Hx3 I9uI?I[A\A^A_] UHAWAVATSHIL'HI9t!ILI@t HxI9uI?I[A\A^A_]UHHH?H Ht]]UHAWAVATSHIL'HI9t!ILI@t HxsI9uI?I[A\A^A_][UHHH?H Ht]?]DUHAVSHIt H{@At H{(t H{[A^]UHAVSHIt H{@At H{(t H{[A^]UHAWAVATSHIL'HI9t!ILI@t HxI9uI?I[A\A^A_]kUHAWAVATSHIL'HI9t!ILI@t Hx3I9uI?I[A\A^A_]UHHH?H Ht]]DUHAWAVATSHIL'HI9t!ILI@t HxI9uI?I[A\A^A_]UHAWAVATSHIL'HI9tIILI9uI?I[A\A^A_]eUHSPHHtHHH[]3H[]@UHSPHHtHHH[]H[]@UHSPHHtH蹇HH[]H[]UH f.UH f.UH f.UH f.UH f.UH f.UH f.UH f.UHH?Hu H1]UHH?Hu H]UHH?Hu H]UHH?Hu H]UHH?Hu H]UHH?Hu H]UHH?Hu Hq]UH f.UH f.UH f.UH f.UHSPHt H{XuH[]H{H[]fffff.UHSPHt H{XuH[]H{H[]fffff.UHSPHt H{XuH[]H{H[]vfffff.UHSPHt H{XQuH[]H{H[]6UH f.UH f.PH= H58 H D 5%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)% )% )%)%)%)%)%)%)%)%)%)% )%")%$)%&)%()%*)%,)%.)%0)%2)%4)%6)%8)%:)%<)%>)%@)%B)%D)%F)%H)%J)%L)%N)%P)%R)%T)%V)%X)%Z)%\)%^)%`)%b)%d)%f)%h)%j)%l)%n)%p)%r)%t)%v)%x)%z)%|)%~)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%$%$%$%$%$%$%$%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)% )% )%)%)%)%)%)%)%)%)%)% )%")%$)%&)%()%*)%,)%.)%0)%2)%4)%6)%8)%:)%<)%>)%@)%B)%D)%F)%H)%J)%L)%N)%P)%R)%T)%V)%X)%Z)%\)%^)%`)%b)%d)%f)%h)%j)%l)%n)%p)%r)%t)%v)%x)%z)%|)%~)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)%)L)AS%$hh h)hEhbhxhhhChhhxhnh7dhZhPhFh4h4h?*h{ hh h4hhhhbhhhhIhhhMhlhhhvh8lhobhXhNh Dh7:h0h&hhh,hIhjhhh#hRhhhhh>h\hzh|hrhhh^hThsJh@h6h,h"hhh/h?h`huhhhhhhh-hChWhghwxhnhdhZhPhFhh-!4h:!*hG! hU!hp! h!h!h!h!h!h!h!h!h"h"h"h/"hD"hW"ho"vh"lh"bh"Xh"Nh#Dh)#:hD#0hc#&h#h#h#h#h#h#h$h$h@$hW$hs$h$h$h$h$h$h%|h8%rhM%hhi%^h%Th%Jh%@h%6h%,h%"h%h&h&h&h-&h=&hL&ha&hp&h&h&h&h&h&h&h&h&xh'nh'dh!'Zh0'Ph@'FhP' &)ops.size() > 0No kernels to registers.custom op registered at runtime1 == undefinedThere must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.IsCompatibleinput count does not matchcustom op schemas mismatch, expecting stndth input to be of optional type input to be of variadic type input to keep same homogeneity input to keep same arity input to be of single typeoutput count does not match output to be of optional type output to be of variadic type output to keep same homogeneity output to keep same arity output to be of single typeCreateCustomRegistry`InlinedVector::at(size_type) const` failed bounds checkSetOutputTypeShapeInvalid shape infosymbolic and integer dims mismatch![TypeInferenceError] Output expected to have tensor or sparse type expected to have tensor typeauto onnxruntime::CreateSchema(const std::string &, const std::vector &)::(anonymous class)::operator()(const OrtCustomOp *, int, int, bool) consti == count - 1Only the last inputoutput to a custom op may be marked variadic.static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o))Another version of operator ''has a different number of inputsoutputs. onnxruntime allows the overloading of an operator if all versions have the same number of declared .defined list of typesall typesonnxruntime::CustomOpKernel::CustomOpKernel(const OpKernelInfo &, const OrtCustomOp &)Unsupported version '' in custom op 'ComputeAsync is not implementedOrtShapeInferContext::OrtShapeInferContext(onnx::InferenceContext &)value_case == ONNX_NAMESPACE::TypeProto::kTensorTypeshape inference not yet supported for non-tensor typesAn allocator for this device has already been registered for sharing.Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.No allocator for this device has been registered for sharing.intra-opinter-opMemcpyFromHost/Users/runner/work/1/s/onnxruntime/core/session/environment.ccXTYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.MemcpyToHost is not implemented in CreateAndRegisterAllocatorV2()seq(tensor(bfloat16))tensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)tensor(complex64)tensor(complex128)tensor(float8e4m3fn)tensor(float8e4m3fnuz)tensor(float8e5m2)tensor(float8e5m2fnuz)seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))string[ShapeInferenceError] Mismatch between inferred and declared type. Inferred= Declared=Unsupported Source/Target type=Trying to register schema with name (domain: version: ) from file line , but it is already registered from file unknown, but its domain is not known by the checker., but its version is not in the inclusive range [, ] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: Exception caught: Cpucom.microsoftcom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwcorg.pytorch.aten/Users/runner/work/1/s/onnxruntime/core/session/inference_session.cclogging::Severity onnxruntime::GetSeverity(const SessionOptions &)session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::ConstructorCommon(const SessionOptions &, const Environment &)status.IsOK()Could not finalize session options while constructing the inference session. Error Message: graph_transformer_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()session.set_denormal_as_zero1session.force_spinning_stopConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is truesession.intra_op.allow_spinning-session--intra-opsession.dynamic_block_baseDynamic block base set to session.intra_op_thread_affinities!to.affinity_str.empty()Affinity string must not be emptyto.custom_join_thread_fncustom join thread function not set for intra op thread poolsession.inter_op.allow_spinning-inter-opcustom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falsesession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.TraceSessionOptionsonnxruntime::InferenceSession::InferenceSession(const SessionOptions &, const Environment &, const PathString &)Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const SessionOptions &, const Environment &, std::istream &)st.IsOK()Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const SessionOptions &, const Environment &, const void *, int)result~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.DmlExecutionProviderHaving memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Can't register custom op domains with ORT for AddCustomOpDomainsReceived nullptr for custom registryReceived nullptr for graph transformerRegisterGraphTransformerGraph transformers must be registered before the session is initialized.SaveToOrtFormatFailed to save ORT format model to file: LoadWithLoaderThis session already contains a loaded model.Unknown exceptionEncountered unknown exception in LoadWithLoader()model_loading_uriLoad model from failed:session.load_model_formatORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().model_loading_arraymodel_loading_protomodel_loading_istreamModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protosession.disable_aot_function_inliningTransformGraphsession.disable_quant_qdqsession.debug_layout_transformationCastFloat16TransformerLoadOrtModelWithLoaderThis session has already been initialized.ORT model verification failed.Serialized version info is null. Invalid ORT format model.The old ORT format model (version ) has saved runtime optimizations. They will be ignored.The ORT format model version [] is not supported in this build 1.17.1Missing Model. Invalid ORT format model.session.use_ort_model_bytes_for_initializersThe provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instanceInitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.session.use_env_allocatorsThis session will use the allocator registered with the environment.optimization.minimal_build_optimizationsTensorrtExecutionProviderCUDAExecutionProviderThis session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs.This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs. as all compute graph nodes have not been partitioned to the CUDA EP.This session cannot use the CUDA Graph feature as requested by the user as all compute graph nodes have not been partitioned to the CUDA EP.This model has shape massaging nodes that will execute on CPU. Use the CUDA Graph feature with caution. As long as the intermediate shapes produced in the model using the representative input used to capture the CUDA graph, will match the shapes produced in the model for other inputs of the same shape as the representative input (common case), it is safe to use the CUDA Graph feature.as all the graph nodes have not been assigned to This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to This session will use the CUDA Graph feature as requested by the user.session.disable_cpu_ep_fallbackConflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.NchwcTransformerSerializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.session.optimized_model_external_initializers_file_namesession.optimized_model_external_initializers_min_size_in_bytes1024Session successfully initialized.Encountered unknown exception in Initialize()session_initializationInvalid rank for Got: Expected: Please fix either the inputs/outputs or the model.Got invalid dimensions for for the following indices index: common::Status onnxruntime::InferenceSession::ValidateInputsOutputs(gsl::span, gsl::span, const InputOutputDefMetaMap &, ArgType) constarg_type == ArgType::kInput || arg_type == ArgType::kOutputValid values kInput, kOutputfeedfetch names has elements, but has elements.Invalid name: with name: '' expected to be of type: but received a tensortensorValidateInputsOutputssparse_tensor but received a sparse tensor but received a tensor sequenceseqAt least one output should be requested.Invalid output name:RunReplaying the captured CUDA Graph for this model with tag: Session was not initializedSession not initialized.memory.enable_memory_arena_shrinkageRunning with tag: disable_synchronize_execution_providersEncountered unknown exception in Run()model_runStart another run for necessary memory allocation or graph capture.input name cannot be emptyNULL input supplied for input output name cannot be emptyStatus onnxruntime::InferenceSession::Run(const RunOptions &, gsl::span, gsl::span, gsl::span, gsl::span)fetch_unique_ptrs[i] != nullptrintra op thread pool must have at least one thread for RunAsyncGetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.Cannot find execution provider SetTuningResultsInvalid TuningResults (index=). does not support TunableOp.Failed to load TuningResults (index=). Reason: Correctly set TuningResults for , enable TunableOp for usingcpugpuUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: The registered allocator for device-id combination is not an arena based allocator: ShrinkMemoryArenasUnable to shrink arena: error message: const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions &, std::unique_ptr &)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: AddPredefinedTransformersWaitForNotificationtimeout_in_ms >0 is not supportedonnxruntime::SessionIOBinding::SessionIOBinding(InferenceSession *)session->NewIOBinding(&binding_).IsOK()ORT_LOAD_CONFIG_FROM_MODELThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configStatus onnxruntime::FinalizeSessionOptions(const SessionOptions &, const onnx::ModelProto &, bool, SessionOptions &)is_model_proto_parsedModelProto needs to be parsed to check for ORT config within itSession Options { execution_mode: execution_order: enable_profiling: optimized_model_filepath: enable_mem_pattern: enable_mem_reuse: enable_cpu_mem_arena: profile_file_prefix: session_logid: session_log_severity_level: session_log_verbosity_level: max_num_graph_transformation_steps: graph_optimization_level: intra_op_param: inter_op_param: use_per_session_threads: thread_pool_allow_spinning: use_deterministic_compute: config_options: { }DEFAULTPRIORITY_BASEDUNKNOWNoperator()Error during EndProfiling(): Provider has already been registered./Users/runner/work/1/s/onnxruntime/core/framework/execution_providers.hAddORTMException during loading: MemcpyTransformersession.save_model_formatsave value of 'save' is only valid when saving an ORT format model.applyInvalid value for ShapePartitionOrtFormatModelApplyOrtFormatModelRuntimeOptimizationsException during initialization: /Users/runner/work/1/s/onnxruntime/core/framework/mldata_type_utils.hMLDataType onnxruntime::utils::GetElementTypeFromOptionalTensor(MLDataType)IsOptionalTensor(type)Provided type is not an optional tensor/Users/runner/work/1/s/include/onnxruntime/core/framework/ort_value.hconst onnxruntime::Tensor &OrtValue::Get() constIsTensor()Trying to get a Tensor, but got: Unexpected data type. Actual: (()) , expected: ())const onnxruntime::SparseTensor &OrtValue::Get() constIsSparseTensor()Trying to get a SparseTensor, but got: Could not find OrtValue with name ''MLDataType onnxruntime::utils::GetElementTypeFromOptionalSeqTensor(MLDataType)IsOptionalSeqTensor(type)Provided type is not an optional sequence tensorconst onnxruntime::TensorSeq &OrtValue::Get() constIsTensorSequence()Trying to get a TensorSeq, but got: /Users/runner/work/1/s/onnxruntime/core/session/inference_session.hStatus onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraph()IsGraphCaptured()Cached EP instance for graph replay is not set yet before calling ReplayGraph()/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.hFeedsFetchesInfoonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span, gsl::span, const OrtValueNameIdxMap &)OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:]Device:[DeviceType: MemoryType: DeviceId:Flush-to-zero and denormal-as-zero are onoffwarr+w+a+wbabrbr+bw+ba+bsession.strict_shape_type_inferenceFailed to load model because protobuf parsing failed.session.allow_released_opsets_onlypost_layout_transform_step_.onnxauto onnxruntime::InferenceSession::TransformGraph(onnxruntime::Graph &, bool)::(anonymous class)::operator()(const Graph &)LoadOrtModelBytes failed. Only / bytes were able to be read.session.use_ort_model_bytes_directly/Users/runner/work/1/s/include/onnxruntime/core/common/parse_string.hParseStringWithClassicLocaleT onnxruntime::ParseStringWithClassicLocale(std::string_view) [T = unsigned long]Failed to parse value: ""unknown exception_.json%Y-%m-%d_%H-%M-%SepresultsvalidatorsThe Model Proto has already been checked for the ORT config json.ort_config/Users/runner/work/1/s/onnxruntime/core/session/inference_session_utils.ccParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: The Model Proto hasn't been checked for the ORT config json.ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelsession_optionsintra_op_num_threadsintra_op_num_threads option in the model file must be an integerinter_op_num_threadsinter_op_num_threads option in the model file must be an integerexecution_modeexecution_mode option in the model file must be an integergraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: Parsing RunOptions from ModelProto is not supported yettuning_resultsParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modelJson stored in the `ort_config` key cannot be parsed. Error message: SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to type must be number, but is type_error[json.exception.] nullobjectarraybooleanbinarydiscardednumberSetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to SetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to Sequential modeParallel modeSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: SetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to falsetruetype must be array, but is cannot compare iterators of different containersinvalid_iteratorcannot get valueTuning results stored in the model file cannot be parsed. Error message: . Ignoring...key '' not foundcannot use at() with map::at: key not foundout_of_rangetype must be string, but is type must be object, but is invalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literalinvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'invalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signvalueobject keyobject separatornumber overflow parsing 'excessive object size: iterator does not fit current valueiterator out of rangecannot use erase() with excessive array size: parse_errorparse error at line , column syntax error while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokencannot use key() for non-object iteratorstried creating tensor with negative value in shapesize overflownot enough space: expected , got /Users/runner/work/1/s/onnxruntime/core/session/onnxruntime_c_api.ccFillSparseTensorCooOrtStatusPtr OrtApis::FillSparseTensorCoo(OrtValue *, const OrtMemoryInfo *, const int64_t *, size_t, const void *, const int64_t *, size_t)FillSparseTensorCsrOrtStatusPtr OrtApis::FillSparseTensorCsr(OrtValue *, const OrtMemoryInfo *, const int64_t *, size_t, const void *, const int64_t *, size_t, const int64_t *, size_t)OrtStatusPtr OrtApis::FillSparseTensorBlockSparse(OrtValue *, const OrtMemoryInfo *, const int64_t *, size_t, const void *, const int64_t *, size_t, const int32_t *)tried Filling sparse tensor with negative value in block sparse indices shapeFillSparseTensorBlockSparseCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyUseCooIndicesOrtStatusPtr OrtApis::UseCooIndices(OrtValue *, int64_t *, size_t)UseCsrIndicesOrtStatusPtr OrtApis::UseCsrIndices(OrtValue *, int64_t *, size_t, int64_t *, size_t)UseBlockSparseIndicesOrtStatusPtr OrtApis::UseBlockSparseIndices(OrtValue *, const int64_t *, size_t, int32_t *)the ort_value must contain a constructed tensorUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsUsingFunction: Registration function was not foundEnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledlengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsindex is out of boundsoffsets buffer is not equal to tensor sizeoutput buffer is too small. Use GetStringTensorDataLength.buffer size is too small for string elementinput array is too shortopaque(,)OrtStatusPtr OrtApis::CreateOpaqueValue(const char *, const char *, const void *, size_t, OrtValue **)ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typenon_tensor_base != nullptrOpaque type is not a non_tensor type!!!OrtStatusPtr OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)Invalid build with no providers availableSpecified provider is not supported.this API does not support stringslocation dimensions do not match shape sizeinvalid location rangemax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkinitial_growth_chunk_size_bytesmax_power_of_two_extend_bytesInvalid key found: The requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s ORT Build Info: git-branch=HEAD, git-commit-id=8f5c79cb63, build type=Release, cmake cxx flags: -DNDEBUG -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fstack-protector-strong -O3 -pipe -ffunction-sections -fdata-sections -fvisibility=hidden -fvisibility-inlines-hidden -DCPUINFO_SUPPORTEDstatic void onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity(Severity)narrowing_errorSparseTensor &(anonymous namespace)::ValidateFillInputArgs(OrtValue *, const TensorShape &, const OrtMemoryInfo *)Strings can only reside in CPU memorytried Filling sparse tensor with negative value in values shapestd::unique_ptr (anonymous namespace)::GetDataTransfer(const OrtDevice &, const OrtDevice &)Not able to find appropriate IDataTransfer to copy sparse dataonnxruntime::Tensor *OrtValue::GetMutable()OrtValue should contain a Tensor or a Sparse TensorSparse Tensor does not contain sparse dataThis API supports Tensors or SparseTensorsshape is invalid/Users/runner/work/1/s/include/onnxruntime/core/framework/tensor.hgsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::string]utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. T out of index/Users/runner/work/1/s/onnxruntime/core/common/safeint.hstatic void SafeIntExceptionHandler::SafeIntOnOverflow()Integer overflowinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map./Users/runner/work/1/s/include/onnxruntime/core/framework/data_types_internal.hstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType>>::check(const Cont &, size_t) [T = std::vector>]++index < c.size()Sequence is missing type entry for its elementstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]Map is missing type entry for its valueconst T &OrtValue::Get() const [T = std::vector>]onnxruntime::DataTypeImpl::GetType() == type_ != static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>>::check(const Cont &, size_t) [T = std::vector>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::vector>]Input is not of one of the supported map types.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]Invalid index requested for map type.const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]/Users/runner/work/1/s/onnxruntime/core/framework/TensorSeq.hconst OrtValue &onnxruntime::TensorSeq::GetAt(size_t) consti < tensors_.size()Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.OrtStatus *OrtCreateValueImplMapHelper(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string]value_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = std::string]len >= 0 && static_cast(len) < std::numeric_limits::max()const T *onnxruntime::Tensor::Data() const [T = std::string]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = long long]const T *onnxruntime::Tensor::Data() const [T = long long]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = float]const T *onnxruntime::Tensor::Data() const [T = float]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = double]const T *onnxruntime::Tensor::Data() const [T = double]OrtStatus *OrtCreateValueImplMapHelper(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = std::string]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = long long]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = float]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = double]Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeOrtStatus *OrtCreateValueImplSeqHelper(const OrtValue *const *, size_t, OrtValue **)in[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.void onnxruntime::TensorSeq::SetType(MLDataType)elem_type_ != nullptrTensor sequence must contain only primitive typesvoid onnxruntime::TensorSeq::Add(const OrtValue &)IsSameDataType(tensor.Get())TensorSeq: tensor to be added has a different data type.T *onnxruntime::Tensor::MutableData() [T = std::string]/Users/runner/work/1/s/onnxruntime/core/session/ort_env.ccstatic void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_.get()/Users/runner/work/1/s/onnxruntime/core/session/provider_bridge_ort.ccLoadDynamicLibraryFromProviderlibonnxruntime_providers_cuda.dyliblibonnxruntime_providers_cuda_ut.dyliblibonnxruntime_providers_cann.dyliblibonnxruntime_providers_rocm.dyliblibonnxruntime_providers_dnnl.dyliblibonnxruntime_providers_openvino.dyliblibonnxruntime_providers_tensorrt.dyliblibonnxruntime_providers_migraphx.dylibdevice_typeenable_npu_fast_compiledevice_idnum_of_threadscache_dircontextenable_opencl_throttlingdisable_dynamic_shapesnum_streamsTryGetProviderInfo_TensorRTProviderInfo_TensorRT &onnxruntime::GetProviderInfo_TensorRT()TensorRT Provider not available, can't get interface for itTryGetProviderInfo_CUDAProviderInfo_CUDA &onnxruntime::GetProviderInfo_CUDA()CUDA Provider not available, can't get interface for itTryGetProviderInfo_CUDA_TestProviderInfo_CUDA &onnxruntime::GetProviderInfo_CUDA_Test()TryGetProviderInfo_CANNProviderInfo_CANN &onnxruntime::GetProviderInfo_CANN()CANN Provider not available, can't get interface for itTryGetProviderInfo_DnnlProviderInfo_Dnnl &onnxruntime::GetProviderInfo_Dnnl()oneDNN Provider not available, can't get interface for itTryGetProviderInfo_ROCMProviderInfo_ROCM &onnxruntime::GetProviderInfo_ROCM()ROCM Provider not available, can't get interface for itvoid onnxruntime::CopyGpuToCpu(void *, const void *, const size_t, const OrtMemoryInfo &, const OrtMemoryInfo &)GPU-to-CPU copy is not implemented.void onnxruntime::cudaMemcpy_HostToDevice(void *, const void *, size_t)cudaMemcpy_HostToDevice is not implemented.AddTensorRTCustomOpDomainToSessionOptionThe custom op domain name is already in session option.OrtSessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryCUDA and/or ROCM execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryCANN execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryoneDNN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.libonnxruntime_providers_shared.dylibEnsurevoid onnxruntime::ProviderSharedLibrary::Ensure()Provider_SetHostUnloadGetProvider &onnxruntime::ProviderLibrary::Get()GetProviderunordered_map::at: key not found/Users/runner/work/1/s/include/onnxruntime/core/graph/graph.hNode *onnxruntime::Graph::NodeAtIndexImpl(NodeIndex) constnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:/Users/runner/work/1/s/include/onnxruntime/core/framework/op_kernel_context.hconst T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::Tensor]Missing Input: const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::SparseTensor]const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]const T &onnxruntime::OpKernelContext::RequiredInput(int) const [T = onnxruntime::Tensor]input_ptrRequired input at index is not present.Tensor *onnxruntime::OpKernelContext::Output(int)p_ml_valuePlease fetch output tensor with specified shape.onnxruntime::TensorSeq *OrtValue::GetMutable()Tensor &onnxruntime::OpKernelContext::RequiredOutput(int, const TensorShape &)output_ptrRequired output at index T *onnxruntime::Tensor::MutableData() [T = bool]T *onnxruntime::Tensor::MutableData() [T = signed char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = float]T *onnxruntime::Tensor::MutableData() [T = double]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E4M3FN]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E4M3FNUZ]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E5M2]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E5M2FNUZ]const T *onnxruntime::Tensor::Data() const [T = bool]const T *onnxruntime::Tensor::Data() const [T = signed char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]const T *onnxruntime::Tensor::Data() const [T = unsigned long long]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E4M3FN]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E4M3FNUZ]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E5M2]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E5M2FNUZ]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]void *onnxruntime::Tensor::MutableDataRaw(MLDataType)type == dtype_Tensor type mismatch.const void *onnxruntime::Tensor::DataRaw(MLDataType) constvoid onnxruntime::Tensor::Reshape(const TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (void onnxruntime::TensorSeq::Add(OrtValue &&)void onnxruntime::TensorSeq::Add(Tensor &&)IsSameDataType(tensor)/Users/runner/work/1/s/onnxruntime/core/session/provider_registration.ccSessionOptionsAppendExecutionProviderOrtStatusPtr OrtApis::SessionOptionsAppendExecutionProvider(OrtSessionOptions *, const char *, const char *const *, const char *const *, size_t)DMLQNNOpenVINOSNPEXNNPACKWEBNNAZUREJSVitisAIUnknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'NNAPITvm execution provider is not supported in this build. execution provider is not enabled in this build. Attribute type not supported yet.standalone_/Users/runner/work/1/s/onnxruntime/core/session/standalone_op_invoker.ccCreateOpKernel definition was not found for node Domain:'' op_type:InvokeOpCompute_Create_State_Release_State_RegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'kernel already mapped to existing nodematching node is missinginvalid node input count: , expect: invalid node output countvirtual int onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputs(size_t) constarg_num < static_cast(input_count_)invalid arg_num.CoreMLExecutionProvider/Users/runner/work/1/s/onnxruntime/core/providers/coreml/coreml_execution_provider.ccGetCapabilityThe current system does not have Apple Neural EngineCoreMLCoreMLExecutionProvider::GetCapability, number of partitions supported by CoreML: number of nodes in the graph: number of nodes supported by CoreML: CompileInconsistent input sizesInconsistent output sizesInput () has a dynamic shape () but the runtime shape () has zero elements. This is not supported by the CoreML EP.auto onnxruntime::CoreMLExecutionProvider::Compile(const std::vector &, std::vector &)::(anonymous class)::operator()(FunctionState, const OrtApi *, OrtKernelContext *)::(anonymous class)::operator()(const std::string &, int32_t, gsl::span) constmodel_output_it != model_outputs.end()Failed to find CoreML model output name: utils::CApiElementTypeFromProtoType(requested_onnx_tensor_element_type) == actual_element_typeRequested and actual output tensor element types do not match. Requested: , actual: /Users/runner/work/1/s/onnxruntime/core/providers/coreml/shape_utils.ccGetShapeImplNodeArg [] has no shape infobool onnxruntime::coreml::(anonymous namespace)::GetShapeImpl(const NodeArg &, std::vector &, const logging::Logger &, bool)dim_value >= 0] has a negative dimension value] has shape with dynamic dimension/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/helper.ccIsInputSupportedInput [] of [] has no shapeCoreML EP is set to only allow static input shapes. Input has a dynamic shape. Input: , shape: CoreML does not support input dim > 16384. Input:CoreML EP doesn't allow input shapes with rank greater than 5. Input: GetSupportedNodesAll ops will fallback to CPU EP, because we do not have supported OSOperator type: [] index: [] name: [] supported: [CheckIsConstantInitializer (NodeArg name: '') is not a constant initializer tensorHasNeuralEngineCurrent Apple hardware info: alphabetabiassize/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/LRN_op_builder.ccIsOpSupportedImplLRN does not support empty input shapeLRN only supports input rank equals to 3 or 4, input rank is PReluSigmoidTanhRelu/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/activation_op_builder.ccAddToModelBuilderImplLeakyReluActivationOpBuilder::AddToModelBuilderImpl, unknown op: AddPReluWeightslope initializer has unsupported data type: Failed to get shape of X.gsl::span onnxruntime::Tensor::DataAsSpan() const [T = float]IsPReluOpSupportedPRelu 'X' input must have 3 or 4 dimensions, it has dimensionsPRelu 'X' input must have a known third from last dimension.PRelu 'slope' input must be a constant initializer tensorPRelu 'slope' input must either have shape [C, 1, 1] or have a single valuePRelu single 'slope' value in CoreML weight is not supportedaxiskeepdimsCastselect_last_index/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/argmax_op_builder.ccselect_last_index for ArgMax is not supportedtoArgmax has both cast and other downstream nodes./Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/base_op_builder.ccHasExternalInitializerInitializer [] with external data location are not currently supportedAddToModelBuilderUnsupported operator Operator name: [] type: [] was addedNode__type_Node [] type [HasSupportedInputsImpl[] Input type: [] is not supported for nowHasSupportedOpSetis only supported for opset [epsilon/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/batch_norm_op_builder.ccYour onnx model may be in training mode, please export it in test mode.BN only support 4d shape for now, input is d shapespatialNon-spatial BN is not supportedScale of BN must be a constant initializerB of BN must be a constant initializerMean of BN must be a constant initializerVar of BN must be a constant initializerMulSubDivPowBinaryOpBuilder::AddToModelBuilderImpl, unknown op: /Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/binary_op_builder.ccPow only supports fp32 inputs, actual input type, Input type 1: , Input type 2: CheckIfBothInputShapesMatch] Input shape is missing/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/builder_utils.ccComputeConvPadsHandleAutoPadThe initializer of graph has unsupported type, name: type: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.static void SafeIntExceptionHandler::SafeIntOnDivZero()Divide by zerogsl::span onnxruntime::Tensor::DataAsSpan() const [T = int]/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/cast_op_builder.ccCast has no preceding nodes.Multiple nodes producing Cast's input.ArgMaxCast's producing node is not ArgMax is not supported.Current producing node: [Cast's producing node [] is not a supported op.] Output type: [] is not supported./Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/clip_op_builder.ccGetClipMinMax failedmin_output_Clip_minthreshold_output_Clip_max_threshold_Clip_max_linear/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/concat_op_builder.ccinput name Concat only support 2+ inputs, actual number of inputs: Concat only support 4d shape for now, input is Concat only support axis to be -3, actual axis: , actual rank: /Users/runner/work/1/s/onnxruntime/core/providers/common.hint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)IsAxisInRange(axis, tensor_rank)axis is not in valid range [-stridesdilationspads/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/conv_op_builder.ccstrides size does not equal 1 for Conv 1ddilations size does not equal 1 for Conv 1donnx_pads size does not equal 2 for Conv 1dgroup_expandDims_Conv_expandCannot get shapeauto_padNOTSET_conv_output_Conv_squeezeConv [] dimension: Only conv 2d and conv 1d are supported.The weight of Conv [] must be knownThe bias of Conv [] must be a constant initializerVALIDSAME_UPPERSAME_LOWERAutoPadType onnxruntime::StringToAutoPadType(const std::string &)Unknown AutoPadType Stringblocksize/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/depthtospace_op_builder.ccDepthToSpace does not support input shape of d shape.modeDCRThe mode: of DepthToSpace is not supported in CoreML EP for now./Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/flatten_op_builder.ccFlatten does not support empty input shape/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/gather_op_builder.ccFailed to get 'data' shapeFailed to get 'indices' shapeGather does not support scalar 'indices'Gather does not support output with rank greater than 5GemmMatMul/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/gemm_op_builder.cctransBB of Gemm/Matmul must be an initializer tensorA must be 2DA must be non-emptyB must be 2DB must be non-emptytransAOnly transA == 0, alpha == 1.0 and beta == 1.0 is supported. transA alpha beta C of Gemm must be an initializer tensorC of Gemm cannot be a scalarC of Gemm must be a vector or a tensor with only last dimension != 1C of Gemm must be a vector of b_shape[ b_shape: [ c_size: /Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/pad_op_builder.ccPad requires input shape to be at least 2d, input is Cases that input data being empty due to a dimension with value of 0 is not supportedconstantOnly `constant` mode Pad is currently supported for now, mode: `constant_value` input is required for constant mode Pad op.constant_value must be a constant initializer.pads must be a constant initializer.Negative pad value is not supported: pads[] = if provided, `axes` input is required to a constant initializerCoreML only supports padding on last two dimensions.GlobalAveragePoolGlobalMaxPoolAveragePoolMaxPoolPoolOpBuilder, unknown op: kernel_shapecount_include_pad/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/pool_op_builder.cc only supports rank-4 tensor, input [] has actual dim count storage_orderstorage_order == 1 is not supportedOnly pooling 2d is supportedceil_modeceil_mode == 1 is not supported for poolingDilations of pooling is not supportedArgmax in maxpooling is not supportedaxesnoop_with_empty_axesReduceSumReduceMeanReductionOpBuilder::AddToModelBuilderImpl, unknown op: /Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/reduction_op_builder.ccAxes of reduction must be a constant initializerCoreML doesn't support noop on empty axes for reduction layers/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/reshape_op_builder.ccNew shape of reshape must be a constant initializerNew shape of reshape cannot be emptyReshape does not support empty input shapeReshape does not support new shape with rank greater than 5. Input shape: , new shape: allowzero Reshape does not support new shape with 0 as dimension when allowzero is enabled. Input shape: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const TensorShape &, TensorShapeVector &, bool)input_shape_size != -1The input tensor must not have any dynamic (-1) dimensions. Input shape:requested_shape[i] >= -1A dimension cannot be less than -1, got unknown_dim == -1At most one dimension can be -1.i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.size != 0 && (input_shape_size % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:input_shape_size == sizenearestlinear/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/resize_op_builder.ccError getting resize scalesError getting input shapeError getting resize output_sizesResize only support 4d shape, input is Resize unsupported input mode, exclude_outsideResize does not support exclude_outside for nowcoordinate_transformation_modehalf_pixelasymmetricResize bilinear, unsupported coord_trans_mode, Resize nearest neighbor, unsupported coord_trans_mode, nearest_moderound_prefer_floorfloorResize nearest neighbor, unsupported nearest_mode, Input scales or sizes of Resize must be knownInput scales of Resize must be knownInput sizes of Resize must be knownScales of N/C channel should be 1Resize of N/C channels are not supported, scale_n, , scale_c, Resize: scale_h: is not a whole numberResize: scale_w: Input shape with dynamic dimensions is not supported.Output sizes of N/C channel should match the input sizes, , input_size_n, , output_size_n, . input_size_c, , output_size_c, Resize: output_size_h: is not a mutliple of input_size_h: Resize: output_size_w: is not a mutliple of input_size_w: start/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/shape_op_builder.ccShape does not support 'start' attribute with value other than 0endShape does not support 'end' attribute/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/slice_op_builder.ccFailed to get input shape.'starts''ends''axes''steps'virtual bool onnxruntime::coreml::SliceOpBuilder::IsOpSupportedImpl(const Node &, const OpBuilderInputParams &, const logging::Logger &) constPrepareSliceComputeMetadataFromConstantInitializersFailed to get constant initializer.Data type for starts and ends inputs' is not supported in this build. Got 'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0ValidateSliceComputeMetadataForCoreMLEmpty range is not supported: [) with step /Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/softmax_op_builder.ccreshape1_output_Softmax_reshape1softmax_output_Softmax_reshape2Empty input data is not supported./Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/split_op_builder.ccnum_outputs'split'CoreML SplitND requires to produce at least 2 outputs.Mismatch between the sum of 'split'. Expected: Actual: Invalid value in 'splits' input.Dim at the splitting axis is not allowed to be dynamic.No 'num_outputs' provided. For split 18+, num_outputs is a required attribute.Invalid num_outputs. The value cannot be lower than 2. CoreML SplitND requires at least 2 outputs. num_outputs: Invalid num_outputs provided. .The value should be smaller or equal to the size of dimension being split. num_outputs: gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/squeeze_op_builder.ccInput axes of Squeeze must be knownperm/Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/impl/transpose_op_builder.ccPerm and input should have same dimensionSqrtReciprocalUnaryOpBuilder::AddToModelBuilderImpl, unknown op: /Users/runner/work/1/s/onnxruntime/core/providers/coreml/builders/model_builder.ccinitializer_RegisterInitializersRegisterModelInputOutputUnable to get shape for The of graph doesn't have elem_type: of graph doesn't have valid type, name: RegisterModelInputsAddOperations], type [] is not supportedRegisterModelOutputsSaveCoreMLModelSave the CoreML model failedORT_COREML_EP_CONVERTED_MODEL_PATH_token_TransposeConvBatchNormalizationReshapeDepthToSpaceConcatResizeClipSqueezeFlattenLRNPadGatherSliceSoftmaxSplit/Users/runner/work/1/s/onnxruntime/core/providers/shared/utils/utils.ccGetType] has no input typeminmaxGetClipMinMaxInput min of Clip must be knownGetClipMinMax() only support Clip node with float inputs for now. The node [] has input 0 type: Input max of Clip must be knowngsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]/Users/runner/work/1/s/onnxruntime/core/providers/coreml/model/model.mm-[CoreMLExecution cleanup]Failed cleaning up the compiled model: , error message: Failed cleaning up the coreml model: Failed to create model URL from pathError compiling model: Failed to create MLModel, error: Model is not loaded-[CoreMLExecution predict:outputs:getOutputTensorDataFn:]Error executing model: output_features has no value for CoreML MLMultiArray count () and shape size () do not matchNon-contiguous output MLMultiArray is not currently supportedv24@?0r^v8q16PredictExecution::Predict requires Execution::LoadModelconst OnnxTensorInfo &onnxruntime::coreml::Model::GetInputOutputInfo(const std::string &) constinfo != nullptrFailed to get info for input/output: Output data type is not supported, actual type: v16@?0^v8CreateInputFeatureProviderFailed to create MLMultiArray for feature: Failed to create MLDictionaryFeatureProviderNSString * _Nonnull (anonymous namespace)::Utf8StringToNSString(const char *)result != nilNSString conversion failed.InlinedVector (anonymous namespace)::GetStaticOutputShape(gsl::span, gsl::span, const logging::Logger &)IsStaticShape(coreml_static_shape)CoreML output shape () is not static.inferred_shape.size() == coreml_static_shape.size()CoreML static output shape () and inferred shape () have different ranks.auto (anonymous namespace)::GetStaticOutputShape(gsl::span, gsl::span, const logging::Logger &)::(anonymous class)::operator()(int64_t, int64_t) constinferred_dim == -1 || inferred_dim == coreml_static_dim) have an inconsistent static dimensions ( vs. ).mlmultiarray_buffer has no dataCopyMLMultiArrayBufferCoreML output buffer size and expected output size differCoreML output data type is not MLMultiArrayDataTypeInt32Exception: CoreML.Specification.ArrayFeatureExtractorCoreML.Specification.BayesianProbitRegressor.GaussianCoreML.Specification.BayesianProbitRegressor.FeatureValueWeightCoreML.Specification.BayesianProbitRegressor.FeatureWeightCoreML.Specification.BayesianProbitRegressor.regressionInputFeatureNameCoreML.Specification.BayesianProbitRegressor.optimismInputFeatureNameCoreML.Specification.BayesianProbitRegressor.samplingScaleInputFeatureNameCoreML.Specification.BayesianProbitRegressor.samplingTruncationInputFeatureNameCoreML.Specification.BayesianProbitRegressor.meanOutputFeatureNameCoreML.Specification.BayesianProbitRegressor.varianceOutputFeatureNameCoreML.Specification.BayesianProbitRegressor.pessimisticProbabilityOutputFeatureNameCoreML.Specification.BayesianProbitRegressor.sampledProbabilityOutputFeatureNameCoreML.Specification.BayesianProbitRegressorstring length exceeds max sizeCoreML.Specification.CategoricalMapping.strValueCoreML.Specification.CategoricalMappingCoreML.Specification.CustomModel.CustomModelParamValue.stringValueCoreML.Specification.CustomModel.CustomModelParamValueCoreML.Specification.CustomModel.classNameCoreML.Specification.CustomModel.descriptionCoreML.Specification.CustomModelCoreML.Specification.CustomModel.ParametersEntry.keyCoreML.Specification.StringToInt64MapCoreML.Specification.Int64ToStringMapCoreML.Specification.StringToDoubleMapCoreML.Specification.Int64ToDoubleMapCoreML.Specification.StringVector.vectorCoreML.Specification.StringVectorCoreML.Specification.Int64VectorCoreML.Specification.FloatVectorCoreML.Specification.DoubleVectorCoreML.Specification.Int64RangeCoreML.Specification.Int64SetCoreML.Specification.DoubleRangeCoreML.Specification.StringToInt64Map.MapEntry.keyCoreML.Specification.Int64ToStringMap.MapEntry.valueCoreML.Specification.StringToDoubleMap.MapEntry.keyCoreML.Specification.DictVectorizerCoreML.Specification.Int64FeatureTypeCoreML.Specification.DoubleFeatureTypeCoreML.Specification.StringFeatureTypeCoreML.Specification.SizeRangeCoreML.Specification.ImageFeatureType.ImageSizeCoreML.Specification.ImageFeatureType.EnumeratedImageSizesCoreML.Specification.ImageFeatureType.ImageSizeRangeCoreML.Specification.ImageFeatureTypeCoreML.Specification.ArrayFeatureType.ShapeCoreML.Specification.ArrayFeatureType.EnumeratedShapesCoreML.Specification.ArrayFeatureType.ShapeRangeCoreML.Specification.ArrayFeatureTypeCoreML.Specification.DictionaryFeatureTypeCoreML.Specification.SequenceFeatureTypeCoreML.Specification.FeatureTypeCoreML.Specification.FeatureVectorizer.InputColumn.inputColumnCoreML.Specification.FeatureVectorizer.InputColumnCoreML.Specification.FeatureVectorizerCoreML.Specification.GLMClassifier.DoubleArrayCoreML.Specification.GLMClassifierCoreML.Specification.GLMRegressor.DoubleArrayCoreML.Specification.GLMRegressorCoreML.Specification.CoreMLModels.Gazetteer.languageCoreML.Specification.CoreMLModels.GazetteerCoreML.Specification.IdentityCoreML.Specification.Imputer.imputedStringValueCoreML.Specification.Imputer.replaceStringValueCoreML.Specification.ImputerCoreML.Specification.ItemSimilarityRecommender.ConnectedItemCoreML.Specification.ItemSimilarityRecommender.SimilarItemsCoreML.Specification.ItemSimilarityRecommender.itemInputFeatureNameCoreML.Specification.ItemSimilarityRecommender.numRecommendationsInputFeatureNameCoreML.Specification.ItemSimilarityRecommender.itemRestrictionInputFeatureNameCoreML.Specification.ItemSimilarityRecommender.itemExclusionInputFeatureNameCoreML.Specification.ItemSimilarityRecommender.recommendedItemListOutputFeatureNameCoreML.Specification.ItemSimilarityRecommender.recommendedItemScoreOutputFeatureNameCoreML.Specification.ItemSimilarityRecommenderCoreML.Specification.LinkedModelCoreML.Specification.LinkedModelFileCoreML.Specification.Pipeline.namesCoreML.Specification.PipelineCoreML.Specification.PipelineClassifierCoreML.Specification.PipelineRegressorCoreML.Specification.FeatureDescription.nameCoreML.Specification.FeatureDescription.shortDescriptionCoreML.Specification.FeatureDescriptionCoreML.Specification.Metadata.shortDescriptionCoreML.Specification.Metadata.versionStringCoreML.Specification.Metadata.authorCoreML.Specification.Metadata.licenseCoreML.Specification.MetadataCoreML.Specification.ModelDescription.predictedFeatureNameCoreML.Specification.ModelDescription.predictedProbabilitiesNameCoreML.Specification.ModelDescriptionCoreML.Specification.SerializedModel.identifierCoreML.Specification.SerializedModelCoreML.Specification.ModelCoreML.Specification.Metadata.UserDefinedEntry.keyCoreML.Specification.Metadata.UserDefinedEntry.valueCoreML.Specification.KNearestNeighborsClassifier.defaultStringLabelCoreML.Specification.KNearestNeighborsClassifierCoreML.Specification.NearestNeighborsIndexCoreML.Specification.UniformWeightingCoreML.Specification.InverseDistanceWeightingCoreML.Specification.LinearIndexCoreML.Specification.SingleKdTreeIndexCoreML.Specification.SquaredEuclideanDistanceCoreML.Specification.NeuralNetworkCoreML.Specification.NeuralNetworkImageScalerCoreML.Specification.NeuralNetworkMeanImageCoreML.Specification.NeuralNetworkPreprocessing.featureNameCoreML.Specification.NeuralNetworkPreprocessingCoreML.Specification.ActivationReLUCoreML.Specification.ActivationLeakyReLUCoreML.Specification.ActivationTanhCoreML.Specification.ActivationScaledTanhCoreML.Specification.ActivationSigmoidCoreML.Specification.ActivationLinearCoreML.Specification.ActivationSigmoidHardCoreML.Specification.ActivationPReLUCoreML.Specification.ActivationELUCoreML.Specification.ActivationThresholdedReLUCoreML.Specification.ActivationSoftsignCoreML.Specification.ActivationSoftplusCoreML.Specification.ActivationParametricSoftplusCoreML.Specification.ActivationParamsCoreML.Specification.TensorCoreML.Specification.NeuralNetworkLayer.nameCoreML.Specification.NeuralNetworkLayer.inputCoreML.Specification.NeuralNetworkLayer.outputCoreML.Specification.NeuralNetworkLayerCoreML.Specification.BranchLayerParamsCoreML.Specification.LoopLayerParams.conditionVarCoreML.Specification.LoopLayerParamsCoreML.Specification.LoopBreakLayerParamsCoreML.Specification.LoopContinueLayerParamsCoreML.Specification.CopyLayerParamsCoreML.Specification.GreaterThanLayerParamsCoreML.Specification.GreaterEqualLayerParamsCoreML.Specification.LessThanLayerParamsCoreML.Specification.LessEqualLayerParamsCoreML.Specification.EqualLayerParamsCoreML.Specification.NotEqualLayerParamsCoreML.Specification.LogicalAndLayerParamsCoreML.Specification.LogicalOrLayerParamsCoreML.Specification.LogicalXorLayerParamsCoreML.Specification.LogicalNotLayerParamsCoreML.Specification.BorderAmounts.EdgeSizesCoreML.Specification.BorderAmountsCoreML.Specification.ValidPaddingCoreML.Specification.SamePaddingCoreML.Specification.SamplingModeCoreML.Specification.BoxCoordinatesModeCoreML.Specification.WeightParamsCoreML.Specification.QuantizationParamsCoreML.Specification.LinearQuantizationParamsCoreML.Specification.LookUpTableQuantizationParamsCoreML.Specification.ConvolutionLayerParamsCoreML.Specification.Convolution3DLayerParamsCoreML.Specification.InnerProductLayerParamsCoreML.Specification.EmbeddingLayerParamsCoreML.Specification.EmbeddingNDLayerParamsCoreML.Specification.BatchnormLayerParamsCoreML.Specification.PoolingLayerParams.ValidCompletePaddingCoreML.Specification.PoolingLayerParamsCoreML.Specification.Pooling3DLayerParamsCoreML.Specification.GlobalPooling3DLayerParamsCoreML.Specification.PaddingLayerParams.PaddingConstantCoreML.Specification.PaddingLayerParams.PaddingReflectionCoreML.Specification.PaddingLayerParams.PaddingReplicationCoreML.Specification.PaddingLayerParamsCoreML.Specification.ConcatLayerParamsCoreML.Specification.LRNLayerParamsCoreML.Specification.SoftmaxLayerParamsCoreML.Specification.SplitLayerParamsCoreML.Specification.AddLayerParamsCoreML.Specification.MultiplyLayerParamsCoreML.Specification.UnaryFunctionLayerParamsCoreML.Specification.UpsampleLayerParamsCoreML.Specification.ResizeBilinearLayerParamsCoreML.Specification.CropResizeLayerParamsCoreML.Specification.BiasLayerParamsCoreML.Specification.ScaleLayerParamsCoreML.Specification.LoadConstantLayerParamsCoreML.Specification.L2NormalizeLayerParamsCoreML.Specification.FlattenLayerParamsCoreML.Specification.ReshapeLayerParamsCoreML.Specification.PermuteLayerParamsCoreML.Specification.ReorganizeDataLayerParamsCoreML.Specification.SliceLayerParamsCoreML.Specification.ReduceLayerParamsCoreML.Specification.CropLayerParamsCoreML.Specification.AverageLayerParamsCoreML.Specification.MaxLayerParamsCoreML.Specification.MinLayerParamsCoreML.Specification.DotProductLayerParamsCoreML.Specification.MeanVarianceNormalizeLayerParamsCoreML.Specification.SequenceRepeatLayerParamsCoreML.Specification.SimpleRecurrentLayerParamsCoreML.Specification.GRULayerParamsCoreML.Specification.LSTMParamsCoreML.Specification.LSTMWeightParamsCoreML.Specification.UniDirectionalLSTMLayerParamsCoreML.Specification.BiDirectionalLSTMLayerParamsCoreML.Specification.CustomLayerParams.CustomLayerParamValue.stringValueCoreML.Specification.CustomLayerParams.CustomLayerParamValueCoreML.Specification.CustomLayerParams.classNameCoreML.Specification.CustomLayerParams.descriptionCoreML.Specification.CustomLayerParamsCoreML.Specification.TransposeLayerParamsCoreML.Specification.BatchedMatMulLayerParamsCoreML.Specification.ConcatNDLayerParamsCoreML.Specification.SoftmaxNDLayerParamsCoreML.Specification.ReverseLayerParamsCoreML.Specification.ReverseSeqLayerParamsCoreML.Specification.LoadConstantNDLayerParamsCoreML.Specification.FillLikeLayerParamsCoreML.Specification.FillStaticLayerParamsCoreML.Specification.FillDynamicLayerParamsCoreML.Specification.WhereBroadcastableLayerParamsCoreML.Specification.SinLayerParamsCoreML.Specification.CosLayerParamsCoreML.Specification.TanLayerParamsCoreML.Specification.AsinLayerParamsCoreML.Specification.AcosLayerParamsCoreML.Specification.AtanLayerParamsCoreML.Specification.SinhLayerParamsCoreML.Specification.CoshLayerParamsCoreML.Specification.TanhLayerParamsCoreML.Specification.AsinhLayerParamsCoreML.Specification.AcoshLayerParamsCoreML.Specification.AtanhLayerParamsCoreML.Specification.PowBroadcastableLayerParamsCoreML.Specification.Exp2LayerParamsCoreML.Specification.WhereNonZeroLayerParamsCoreML.Specification.MatrixBandPartLayerParamsCoreML.Specification.UpperTriangularLayerParamsCoreML.Specification.LowerTriangularLayerParamsCoreML.Specification.BroadcastToLikeLayerParamsCoreML.Specification.BroadcastToStaticLayerParamsCoreML.Specification.BroadcastToDynamicLayerParamsCoreML.Specification.AddBroadcastableLayerParamsCoreML.Specification.MaxBroadcastableLayerParamsCoreML.Specification.MinBroadcastableLayerParamsCoreML.Specification.ModBroadcastableLayerParamsCoreML.Specification.FloorDivBroadcastableLayerParamsCoreML.Specification.SubtractBroadcastableLayerParamsCoreML.Specification.MultiplyBroadcastableLayerParamsCoreML.Specification.DivideBroadcastableLayerParamsCoreML.Specification.GatherLayerParamsCoreML.Specification.ScatterLayerParamsCoreML.Specification.GatherNDLayerParamsCoreML.Specification.ScatterNDLayerParamsCoreML.Specification.GatherAlongAxisLayerParamsCoreML.Specification.ScatterAlongAxisLayerParamsCoreML.Specification.StackLayerParamsCoreML.Specification.RankPreservingReshapeLayerParamsCoreML.Specification.ConstantPaddingLayerParamsCoreML.Specification.RandomNormalLikeLayerParamsCoreML.Specification.RandomNormalStaticLayerParamsCoreML.Specification.RandomNormalDynamicLayerParamsCoreML.Specification.RandomUniformLikeLayerParamsCoreML.Specification.RandomUniformStaticLayerParamsCoreML.Specification.RandomUniformDynamicLayerParamsCoreML.Specification.RandomBernoulliLikeLayerParamsCoreML.Specification.RandomBernoulliStaticLayerParamsCoreML.Specification.RandomBernoulliDynamicLayerParamsCoreML.Specification.CategoricalDistributionLayerParamsCoreML.Specification.ReduceL1LayerParamsCoreML.Specification.ReduceL2LayerParamsCoreML.Specification.ReduceMaxLayerParamsCoreML.Specification.ReduceMinLayerParamsCoreML.Specification.ReduceSumLayerParamsCoreML.Specification.ReduceProdLayerParamsCoreML.Specification.ReduceMeanLayerParamsCoreML.Specification.ReduceLogSumLayerParamsCoreML.Specification.ReduceSumSquareLayerParamsCoreML.Specification.ReduceLogSumExpLayerParamsCoreML.Specification.ExpandDimsLayerParamsCoreML.Specification.FlattenTo2DLayerParamsCoreML.Specification.ReshapeStaticLayerParamsCoreML.Specification.ReshapeLikeLayerParamsCoreML.Specification.ReshapeDynamicLayerParamsCoreML.Specification.SqueezeLayerParamsCoreML.Specification.TopKLayerParamsCoreML.Specification.ArgMaxLayerParamsCoreML.Specification.ArgMinLayerParamsCoreML.Specification.SplitNDLayerParamsCoreML.Specification.CeilLayerParamsCoreML.Specification.RoundLayerParamsCoreML.Specification.FloorLayerParamsCoreML.Specification.SignLayerParamsCoreML.Specification.ClipLayerParamsCoreML.Specification.SliceStaticLayerParamsCoreML.Specification.SliceDynamicLayerParamsCoreML.Specification.TileLayerParamsCoreML.Specification.GetShapeLayerParamsCoreML.Specification.ErfLayerParamsCoreML.Specification.GeluLayerParamsCoreML.Specification.RangeStaticLayerParamsCoreML.Specification.RangeDynamicLayerParamsCoreML.Specification.SlidingWindowsLayerParamsCoreML.Specification.LayerNormalizationLayerParamsCoreML.Specification.NonMaximumSuppressionLayerParamsCoreML.Specification.ClampedReLULayerParamsCoreML.Specification.ArgSortLayerParamsCoreML.Specification.SliceBySizeLayerParamsCoreML.Specification.NeuralNetworkClassifier.labelProbabilityLayerNameCoreML.Specification.NeuralNetworkClassifierCoreML.Specification.OneHotLayerParamsCoreML.Specification.CumSumLayerParamsCoreML.Specification.NeuralNetworkRegressorCoreML.Specification.NetworkUpdateParametersCoreML.Specification.LossLayer.nameCoreML.Specification.LossLayerCoreML.Specification.CategoricalCrossEntropyLossLayer.inputCoreML.Specification.CategoricalCrossEntropyLossLayer.targetCoreML.Specification.CategoricalCrossEntropyLossLayerCoreML.Specification.MeanSquaredErrorLossLayer.inputCoreML.Specification.MeanSquaredErrorLossLayer.targetCoreML.Specification.MeanSquaredErrorLossLayerCoreML.Specification.OptimizerCoreML.Specification.SGDOptimizerCoreML.Specification.AdamOptimizerCoreML.Specification.CustomLayerParams.ParametersEntry.keyCoreML.Specification.NonMaximumSuppression.PickTopCoreML.Specification.NonMaximumSuppression.confidenceInputFeatureNameCoreML.Specification.NonMaximumSuppression.coordinatesInputFeatureNameCoreML.Specification.NonMaximumSuppression.iouThresholdInputFeatureNameCoreML.Specification.NonMaximumSuppression.confidenceThresholdInputFeatureNameCoreML.Specification.NonMaximumSuppression.confidenceOutputFeatureNameCoreML.Specification.NonMaximumSuppression.coordinatesOutputFeatureNameCoreML.Specification.NonMaximumSuppressionCoreML.Specification.NormalizerCoreML.Specification.OneHotEncoderCoreML.Specification.Int64ParameterCoreML.Specification.DoubleParameterCoreML.Specification.StringParameter.defaultValueCoreML.Specification.StringParameterCoreML.Specification.BoolParameterCoreML.Specification.LinearKernelCoreML.Specification.RBFKernelCoreML.Specification.PolyKernelCoreML.Specification.SigmoidKernelCoreML.Specification.KernelCoreML.Specification.SparseNodeCoreML.Specification.SparseVectorCoreML.Specification.SparseSupportVectorsCoreML.Specification.DenseVectorCoreML.Specification.DenseSupportVectorsCoreML.Specification.CoefficientsCoreML.Specification.SupportVectorRegressorCoreML.Specification.SupportVectorClassifierCoreML.Specification.ScalerCoreML.Specification.CoreMLModels.SoundAnalysisPreprocessing.VggishCoreML.Specification.CoreMLModels.SoundAnalysisPreprocessingCoreML.Specification.CoreMLModels.TextClassifier.languageCoreML.Specification.CoreMLModels.TextClassifierCoreML.Specification.TreeEnsembleParameters.TreeNode.EvaluationInfoCoreML.Specification.TreeEnsembleParameters.TreeNodeCoreML.Specification.TreeEnsembleParametersCoreML.Specification.TreeEnsembleClassifierCoreML.Specification.TreeEnsembleRegressorCoreML.Specification.CoreMLModels.VisionFeaturePrint.SceneCoreML.Specification.CoreMLModels.VisionFeaturePrint.Objects.outputCoreML.Specification.CoreMLModels.VisionFeaturePrint.ObjectsCoreML.Specification.CoreMLModels.VisionFeaturePrintCoreML.Specification.CoreMLModels.WordEmbedding.languageCoreML.Specification.CoreMLModels.WordEmbeddingCoreML.Specification.CoreMLModels.WordTagger.languageCoreML.Specification.CoreMLModels.WordTagger.tokensOutputFeatureNameCoreML.Specification.CoreMLModels.WordTagger.tokenTagsOutputFeatureNameCoreML.Specification.CoreMLModels.WordTagger.tokenLocationsOutputFeatureNameCoreML.Specification.CoreMLModels.WordTagger.tokenLengthsOutputFeatureNameCoreML.Specification.CoreMLModels.WordTagger/Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion_helper.hbool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const Graph &, const Node &, const std::vector &, const std::vector &, const logging::Logger &)input_indices.size() == expected_values.size() && input_indices.size() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:MatchGemmSubgraphStart MatchGemmSubgraphUnsqueezeFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphValidateGemmInitializerStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerValidateUnidirMaskunidir mask is not constantunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatMatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphWhereFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphMatchInputMaskSubgraphStart MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertExpandEqualFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphCheckDistilBertReshapeShapeCheckNodesInPathVStart CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedCheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQCheckNodesInPathKStart CheckNodesInPathKk_transpose has not perm attributek_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKMask_Int32MaskCastCast mask from int64 to int32FuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseAttentionFused Attention subgraphs num_headsunidirectionalmask_filter_valueFused an attention node for GPT./Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccApplyImplLayerNormalizationshape of layer norm bias tensor not expectedTotal fused Attention node count: FuseSubGraphFaild to find path vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraph/Users/runner/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.hRecurseFuseSubGraphQKFused an attention node.FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32qkv_weightsqkv_biasFuseSubGraphQKDistilBert/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_dropout_fusion.ccDropoutBitmaskDropoutBiasDropoutBitmaskBiasDropoutfused Add-Dropout-(Add) for /Users/runner/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.ccGeluFastGeluBiasGelufused Add and Gelu/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_softmax_fusion.ccROCMExecutionProviderfused and into softmax(input + bias)BiasSoftmaxis_inner_broadcast/Users/runner/work/1/s/onnxruntime/core/optimizer/common_subexpression_elimination.ccvirtual Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(Graph &, bool &, int, const logging::Logger &) constrepresentative.output_index != kInvalidOutputIndexNot eliminating output of node ] because it's the graph's output.DequantizeLinearConstantFolding/Users/runner/work/1/s/onnxruntime/core/optimizer/constant_folding.ccIfQuantizeLinearCould not find a CPU kernel and hence can't constant fold node 'virtual Status onnxruntime::ConstantFolding::ApplyImpl(Graph &, bool &, int, const logging::Logger &) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold ConstantFoldIfNodeIf node condition initializer: `' is expected to have a single boolean elementUnable to constant fold. InlineIfSubgraph failed ': /Users/runner/work/1/s/onnxruntime/core/optimizer/constant_sharing.ccTotal shared scalar initializer count: ConvActivationFusionConvActNhwcConvFusedConvNhwcFusedConv/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_activation_fusion.ccvirtual std::string onnxruntime::(anonymous namespace)::actions::FuseConvActivationAction::OpType(const RuntimeState &) constUnsupported operator: and domain: /Users/runner/work/1/s/onnxruntime/core/optimizer/selectors_actions/helpers.hNode *onnxruntime::NodesToOptimize::GetNode(size_t, bool) constindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)virtual NodeAttributes onnxruntime::(anonymous namespace)::actions::FuseConvActivationAction::ExtraAttributes(const RuntimeState &) constactivation != nullptrExpected activation node.activationoptimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)Failed to get Clip min/max constants.HardSigmoidactivation_paramsConvAddReluvirtual std::vector onnxruntime::(anonymous namespace)::actions::FuseConvAddRelu::ValueMoves(const RuntimeState &) constconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"Expected Conv then Add.ConvAddActivationFusionConvAddAct/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_add_act_fusion.ccvirtual NodeAttributes onnxruntime::(anonymous namespace)::actions::FuseConvAddActivationAction::ExtraAttributes(const RuntimeState &) constvirtual std::vector onnxruntime::(anonymous namespace)::actions::FuseConvAddActivationAction::ValueMoves(const RuntimeState &) const/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual Status onnxruntime::ConvAddFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual Status onnxruntime::ConvBNFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual Status onnxruntime::ConvMulFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_DoubleQDQRemoved_/Users/runner/work/1/s/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.ccMatMulIntegerToFloatDynamicQuantizeLinearDynamicQuantizeMatMul/Users/runner/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccFuseSubGraphDistilBertWord embedding shape not expected.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). NonZeroConstantOfShapeRangeMatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.MatchInputToConcatSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeCheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64mask_indexEmbedLayerNormalizationfused EmbedLayerNorm subgraphs _Int32_CastCast Input from int64 to int32Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Segment id is not valid. Input_ids and segment id should have the same shape. position_embeddings/Users/runner/work/1/s/onnxruntime/core/optimizer/fast_gelu_fusion.ccfast_gelu_outputGPT2Gelufused GPT2Gelu subgraphs FreeDimensionOverrideTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)Invalid free dimension override.Conflicting free dimension overrides.The model has input '' with a fixed dimension size which does not equal the specified override of /Users/runner/work/1/s/onnxruntime/core/optimizer/gather_fusion.ccsplitSplit for Fused Gather nodesSqueeze for Fused Gather nodesSqueezeAxesInitializervirtual Status onnxruntime::GatherToSliceFusion::ApplyImpl(Graph &, bool &, int, const logging::Logger &) constrange_input_defs.size() == 3unsqueeze_output_Unsqueeze_Unsqueeze for Fused Gather nodesUnsqueezeAxesInitializerSliceAxesInitializerSlice for Fused Gather nodes/Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccGelu approximationTotal Gelu Approximation (FastGelu) node count: /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_fusion.ccErffused Gelu subgraphs /Users/runner/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccFusedGemmfused Gemm with activation activation_EluSeluSoftplusSoftsignScaledTanhParametricSoftplusThresholdedRelu/Users/runner/work/1/s/onnxruntime/core/optimizer/gemm_sum_fusion.ccvirtual Status onnxruntime::GemmSumFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constnew_gemm_input_defs.size() == 3new_gemm_output_defs.size() == 1_sum_transformedFused Gemm with Sumgemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0!sum_input_movedgraph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())Sumvirtual bool onnxruntime::GemmSumFusion::SatisfyCondition(const Graph &, const Node &, const logging::Logger &) constother_sum_input != nullptr_transformedFused Gemm with Transpose/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApplyGraphTransformer modified: with status: /Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_utils.ccInlinedVector> onnxruntime::optimizer_utils::GenerateRewriteRules(TransformerLevel, const InlinedHashSet &)Unsupported optimization level: GenerateRuleBasedGraphTransformerstd::unique_ptr onnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer(TransformerLevel, const InlinedHashSet &, const InlinedHashSet &)session.disable_double_qdq_removersession.enable_quant_qdq_cleanupsession.qdqisint8allowedoptimization.enable_gelu_approximationACLExecutionProviderArmNNExecutionProviderJsExecutionProvidersession.x64quantprecisionInlinedVector> onnxruntime::optimizer_utils::GenerateTransformers(TransformerLevel, const SessionOptions &, const IExecutionProvider &, const InlinedHashSet &)InlinedVector> onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild(TransformerLevel, const SessionOptions &, const SatApplyContextVariant &, const IExecutionProvider &, const InlinedHashSet &)EliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutExpandEliminationCastEliminationPreShapeNodeEliminationNoopEliminationDivMulFusionFuseReluClipGemmSumFusionGemmTransposeFusionNotWhereFusionConvAddFusionConvMulFusionConvBNFusionPad_FusionMatMul_BatchNormalization_FusionClipQuantRewriteReluQuantRewriteDoubleQDQPairsRemoverConstantSharingCommonSubexpressionEliminationMatMulAddFusionReshapeFusionQDQPropagationTransformerRocmBlasAltImplTransposeOptimizerTransposeOptimizer_QDQS8ToU8TransformerGemmActivationFusionMatMulIntegerToFloatFusionDynamicQuantizeMatMulFusionGeluFusionLayerNormFusionSimplifiedLayerNormFusionAttentionFusionEmbedLayerNormFusionGatherToSplitFusionGatherToSliceFusionMatmulTransposeFusionBiasGeluFusionSkipLayerNormFusionFastGeluFusionQuickGeluFusionGeluApproximationBiasSoftmaxFusionBiasDropoutFusionMatMulScaleFusionMatMulActivationFusionAvx2WeightS8ToU8TransformerQDQFinalCleanupTransformerYieldOpIdentity/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.cconnxruntime::Initializer::Initializer(const onnx::TensorProto &, const Path &)utils::HasDataType(tensor_proto)Initializer must have a datatype!model_path.IsEmpty()model_path must not be empty. Ensure that a path is provided when the model is created or loaded.InitializerInitializer &onnxruntime::Initializer::add(const Initializer &)data_type() == other.data_type()Expecting the same data typesize() == other.size()Expecting the same sizeInitializer &onnxruntime::Initializer::sub(const Initializer &)Initializer &onnxruntime::Initializer::mul(const Initializer &)Initializer &onnxruntime::Initializer::div(const Initializer &)void onnxruntime::Initializer::scale_by_axis(const Initializer &, int, bool)axis >= 0Axis must be non-negativescalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)Invalid other(scalers) sizegsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1Unsupported data type: gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = float]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]InsertedPrecisionFreeCast_cast node to cast from float16 to float32 on cpu/Users/runner/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.ccdtypevirtual Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constdtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.bool onnxruntime::IsIsolatedFp16NodeOnCpu(const onnxruntime::Node &, onnxruntime::Graph &, const KernelRegistry &)idx < input_defs.size()RemoveDuplicateCastTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.ccfused LayerNorm subgraphs stash_typeSimplifiedLayerNormalizationchannels_lastLayout/Transpose optimization for failed: QLinearAveragePoolQLinearGlobalAveragePool/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_activation_fusion.ccFusedMatMul_FusedActivationFusedMatMulActivation with activation /Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccgemmfused Matmul and Add /Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_bn_fusion.ccvirtual Status onnxruntime::MatmulBNFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constscale_tensorbias_tensormean_tensorvar_tensormatmul_b_tensorMatMulBnFusion_GemmB_MatMulBnFusion_GemmBiasMatMulBnFusion_GemmGenerated from Matmul BatchNormalization fusion/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_integer_to_float.ccMatMulInteger/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_scale_fusion.cc_FusedMatMulAndScaleFused MatMul and Scalestd::vector onnxruntime::(anonymous namespace)::GetInputNodeMerges(Graph &, Node &, const InlinedHashSet &)input_node.InputDefs().size() == 2 && scale_and_index->second < 2std::optional> onnxruntime::(anonymous namespace)::GetScaleFromNode(const Graph &, const Node &, const InlinedHashSet &)div_inputs.size() == 2mul_inputs.size() == 2std::optional onnxruntime::(anonymous namespace)::GetScalarConstantInitializer(const Graph &, const NodeArg &)shapeConstant initializer NodeArg shape should not be null. NodeArg: GetScalarConstantInitializervoid onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret &) const [Ret = onnxruntime::common::Status]std::vector onnxruntime::(anonymous namespace)::GetOutputNodeMerges(Graph &, Node &, const InlinedHashSet &)output_node.OutputDefs().size() == 1/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_transpose_fusion.cctransBatchAtransBatchBMatMul_With_Transposefused MatMul and Transpose bool onnxruntime::GetTransposePerms(const Node &, std::vector &)transpose_node.InputDefs().size() == 1Created a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodessize_t onnxruntime::UpdateConsumerCount(Graph &, NodeArg *, InlinedHashMap &)!node_consumers.empty()reorderReorderInput/Users/runner/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.ccvoid onnxruntime::NchwcTransformerImpl::TransformConv(Node &)output_channels <= nchwc_output_channelsBuffer overflow_nchwcreshapebn_scalebn_B_bn_nchwcReorderOutputchannelsalign_cornersUpsamplescalesNhwcTransformerQLinearConvT1/Users/runner/work/1/s/onnxruntime/core/optimizer/nhwc_transformer.ccNot/Users/runner/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const InitializedTensorSet &, const Path &, const IExecutionProvider &, const std::function &)Infoonnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const std::unordered_map &, const Path &, const IExecutionProvider &, const std::function &)Tried to allocate without valid type information, ort_value index=ForEachWithIndexTryCreateKernelQAttentionQGemmQLinearMatMul/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/avx2_weight_s8_to_u8.ccDynamicQuantizeLSTMTryConvertDynamicQuantizeLSTMUnable transforming DynamicQuantizeLSTM operator, cannot locate recurrence tensor of const int8 type, int8 overflow might impact precision ! unable to locate recurrence tensor or its zero point value,_s8_2_u8weight_zp_s8_2_u8/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/clip_quantizelinear.ccbool onnxruntime::GetQConstantLowerUpper(const Graph &, const Node &, float &, float &)Unexpected data type for QuantizeLinear input y_zero_point of /Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/ensure_unique_dq_for_node_unit.ccEnsureUniqueDQForNodeUnit/duplicatedAdded by /Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_final_cleanup.ccCleanUpNodeSequenceCleaning up back-to-back nodes: with name "" and /Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccPropagateQBackward/Users/runner/work/1/s/onnxruntime/core/graph/extended_graph_edge.hstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNode(const Graph &, const Node &, int)node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()std::optional onnxruntime::(anonymous namespace)::GetPreviousPropagationEdge(const Graph &, const ExtendedGraphEdge &)src_node != nullptrconst Node *onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEnd(const Graph &, End) constnode != nullptrInvalid node index Status onnxruntime::(anonymous namespace)::InsertQDQPair(Graph &, const ExtendedGraphEdge &, NodeArg &, NodeArg *, const std::string &, const logging::Logger &)src_node || dst_nodeAt least one graph node must be specified in the propagation edge.InsertQDQPairInserting Q/DQ pair between node ("", index: at NodeArg ""._pre_q_q_to_dq_post_dq_qInserted by QDQPropagationTransformerFailed to set op schema for added Q node._dqFailed to set op schema for added DQ node.Node *onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEnd(Graph &, End) constPropagateDQForwardstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput(const Graph &, const Node &, int)node_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()std::optional onnxruntime::(anonymous namespace)::GetNextPropagationEdge(const Graph &, const ExtendedGraphEdge &)dst_node != nullptr/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.ccqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quant/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_util.ccbool onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPoint(const Node &, const GetConstantInitializerFn &, bool &)q_or_dq_input_defs.size() >= 2opsetinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14agenerated at runtimeQLinearQDQSelectorActionTransformerdropSplitQDQdropdrop_no_int16_supportdropDQ1DQ2DQ*DQ/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/shared/utils.ccvoid onnxruntime::QDQ::SelectorManager::InitializeSelectorsMap()insertedMultiple entries for operator is not supported. OpType=GetQDQSelectionsOp version is not supported forValidateNodeGroupDQNodesQDQ node group cannot have DQ node that produces a graph output. DQ node: , target node: QDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: TileArgMinReduceMinReduceMaxReduceProdHardSwishLogSoftmaxAtanAsinSinCosSignExpLogCeilFloorRoundAbsNegSpaceToDepthLpNormalizationGridSampleMaxMinConvTransposeInstanceNormalizationGreaterGreaterOrEqualLessLessOrEqualTopK/Users/runner/work/1/s/onnxruntime/core/optimizer/quick_gelu_fusion.ccQuickGelu/Users/runner/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual Status onnxruntime::FuseReluClip::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constant/Users/runner/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccallowzeroFused reshape node: Total fused reshape node count: startsendsConcatTrainingFuse_SubgraphCannot replace concat node with initializer:/Users/runner/work/1/s/onnxruntime/core/optimizer/rocm_blas_alt_impl.cc__backwardpass/Users/runner/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/work/1/s/onnxruntime/core/optimizer/selectors_actions/actions.ccRunForSaveFailed to set node op schema.Failed to remove node.CreateReplacementNode/Users/runner/work/1/s/onnxruntime/core/optimizer/selectors_actions/helpers.ccNodesToOptimizeIndices onnxruntime::NodesToOptimizeIndicesBuilder::Build() consttarget_node != NodesToOptimizeIndices::kEmptyNodeIndexA target node must be set.MoveInputOutputauto onnxruntime::GetNodesToOptimizeIndices(gsl::span, NodeIndex, gsl::span, int, int)::(anonymous class)::operator()(NodeIndex) constnode_idx <= NodesToOptimizeIndices::kEmptyNodeIndexNode index value is too large to save to ORT format model: auto onnxruntime::NodesToOptimize::ToIndices()::(anonymous class)::operator()(const Node *) constMoveInputOutputImplIndex out of range/Users/runner/work/1/s/onnxruntime/core/optimizer/selectors_actions/selector_action_transformer.ccvoid onnxruntime::SelectorActionRegistry::RegisterSelectorAndAction(const std::string &, const OpVersionsMap &, std::unique_ptr, std::unique_ptr)inserted_in_name_to_entryExisting registration with name ApplySelectorsAndActionsApplySavedRuntimeOptimizationsApplying runtime optimization action for transformer Missing action Nodes to optimize are not valid, skipping action.MatchAndProcessMatched SetOpSinceVersionForProducedNodesNot enough produced nodes in the runtime optimization record.New node op () does not match produced node op in runtime optimization record (Too many produced nodes in the runtime optimization record./Users/runner/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccSkipLayerNormalizationfused SkipLayerNorm subgraphs _FloatCast Input to float/Users/runner/work/1/s/onnxruntime/core/optimizer/transformer_memcpy.cc Memcpy nodes are added to the graph for . It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.MIGraphXExecutionProvidervoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const KernelRegistryManager &, InitializedTensorSet &)VitisAIExecutionProviderExecution type '' doesn't support memcpy MemcpyAddCopyNodeAdd after before Copy from/to host memorybool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const KernelRegistryManager &, const InitializedTensorSet &)ProcessInitializersauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const KernelRegistryManager &, const InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constdup_replacements.find(&arg) == dup_replacements.end()IsInfIsNaNShrinkCeluTanSinhCoshAcosAsinhAcoshAtanhAndOrXorModBitShiftMeanCastLikeReduceLogSumReduceLogSumExpReduceSumSquareReduceL1ReduceL2Hardmaxai.onnxUnsupported ONNX opset: LpPoolMaxUnpoolGlobalLpPool/Users/runner/work/1/s/onnxruntime/core/optimizer/transpose_optimization/ort_optimizer_api_impl.ccvirtual void onnxruntime::ApiValueInfo::PermuteDims(const std::vector &)perm.size() == gsl::narrow_cast(shape_proto->dim_size())Permutation length does not match rank 0 <= p && p_int < shape_proto->dim_size()Permutation entry out of bounds for shape virtual size_t onnxruntime::ApiTensor::NumElements() constsize >= 0Failed to get size of TensorProtoDatavirtual std::vector onnxruntime::ApiTensor::Data() constvirtual std::unique_ptr onnxruntime::ApiGraph::GetValueInfo(std::string_view) constnode_arg_ != nullptrNo NodeArg found for name virtual void onnxruntime::ApiGraph::TransposeInitializer(std::string_view, const std::vector &)successFailed to find initializer for name: TransposeInitializervirtual void onnxruntime::ApiGraph::ReshapeInitializer(std::string_view, const std::vector &)Failed to find initializer to reshape with name new_num_elts == old_num_eltsCannot reshape initializer to have different number of elementsconst_transpose_optimizervirtual void onnxruntime::ApiGraph::CopyValueInfo(std::string_view, std::string_view)dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)Existing destination type is not compatible with source type.int onnxruntime::GetSinceVersionForNewOp(std::string_view, std::string_view, const std::unordered_map &)opset_import_iter != domain_to_version_map.end() domain not found in opset imports.since_version.has_value()Transpose Optimizer is adding an unexpected node: An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.NhwcMaxPool_outAdded in transpose optimizercom.microsoft.QuantizeLinearcom.microsoft.DequantizeLinearcom.microsoft.QLinearAddcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearConcatcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearMulcom.microsoft.QLinearReduceMeancom.microsoft.QLinearSigmoid/Users/runner/work/1/s/onnxruntime/core/optimizer/transpose_optimizer.ccTranspose optimizer failed: UnsqueezeElimination_/Users/runner/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomial/Users/runner/work/1/s/onnxruntime/core/optimizer/utils.ccauto onnxruntime::optimizer_utils::GetClipConstantMinMax(const Graph &, const Node &, float &, float &)::(anonymous class)::operator()(const Node &, size_t, float &) constUnexpected data type for Clip input of /Users/runner/work/1/s/onnxruntime/core/providers/get_execution_providers.ccauto onnxruntime::GetAllExecutionProviderNames()::(anonymous class)::operator()() constprovider.name.size() <= kMaxExecutionProviderNameLenMake the EP: name shorterOpenVINOExecutionProviderDnnlExecutionProviderTvmExecutionProviderQNNExecutionProviderNnapiExecutionProviderRknpuExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderCANNExecutionProviderAzureExecutionProviderauto onnxruntime::GetAvailableExecutionProviderNames()::(anonymous class)::operator()() const/Users/runner/work/1/s/onnxruntime/core/providers/partitioning_utils.ccstd::vector> onnxruntime::utils::(anonymous namespace)::CreateSupportedPartitionNodeGroups(const GraphViewer &, const IsNodeSupportedFn &, const OnGroupClosedFn &, const std::string &, bool)is_node_supported_fnNode support test is required.Unsupported type/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/activations.ccCreateunknown kernel type/Users/runner/work/1/s/onnxruntime/core/providers/cpu/element_wise_ranged_transform.hElementWiseKernelonnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Elu]input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Selu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Softsign]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Tanh]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Tanh]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Celu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::ThresholdedRelu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::ThresholdedRelu]No attribute with name:''is defined.Attribute name and type don't match for '/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/activations.hInitgamma/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/activations.hBV/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/if.cconnxruntime::If::Info::Info(const onnxruntime::Node &, const GraphViewer &)num_subgraph_outputs == static_cast(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.then_branchvoid onnxruntime::If::Init(const OpKernelInfo &)info.GetAttr("then_branch", &proto).IsOK()else_branchinfo.GetAttr("else_branch", &proto).IsOK()virtual common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.SetupSubgraphExecutionInfovirtual Status onnxruntime::If::Compute(OpKernelContext *) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.ComputeIf nodes condition input must have exactly one elementsession_stateSubgraph SessionState was not found for '' attribute.Failed to create output tensor for Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedExecuteFailed to create output tensor for If output ILoop/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/loop.cconnxruntime::Loop::Info::Info(const onnxruntime::Node &, const GraphViewer &)static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has bodyvoid onnxruntime::Loop::Init(const OpKernelInfo &)info.GetAttr("body", &proto).IsOK()virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)info_ == nullptrvirtual Status onnxruntime::Loop::Compute(OpKernelContext *) constSubgraph SessionState was not found for 'body' attribute.feeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: Loop subgraph input 1 has unknown shape: void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector &, std::vector &)last_outputs[j + 1].IsTensor()All scan outputs MUST be tensorsConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Inconsistent shape in loop output for output. Expected: Got:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.ccvoid onnxruntime::Scan<8>::Init(const OpKernelInfo &) [OpSet = 8]num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()directionsvirtual Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &) [OpSet = 8]virtual Status onnxruntime::Scan<8>::Compute(OpKernelContext *) const [OpSet = 8]feeds_fetches_manager_ && info_Invalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was but has batch size of Scan inputs have inconsistent sequence lengths. Previous value was has length of ValidateInputsequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was Subgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsStatus onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector> &)p_mlvalueOutput OrtValue has not been created for loop state variable output CreateLoopStateVariablesScanScan<8> spec does not support transpose of output. This should never be called./Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.htypename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = const OrtValue]position_ >= 0 && position_ < sequence_length_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_9.ccvoid onnxruntime::Scan<9>::Init(const OpKernelInfo &) [OpSet = 9]scan_input_directionsscan_output_directionsscan_input_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was virtual Status onnxruntime::Scan<9>::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &) [OpSet = 9]virtual Status onnxruntime::Scan<9>::Compute(OpKernelContext *) const [OpSet = 9] but input '' dimension Invalid value in scan_input_axes for input of . Input tensor rank was SetupInputsStatus onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector &)output_mlvalueInvalid value in scan_output_axes for output . Output tensor rank was Status onnxruntime::ScanImpl::TransposeOutput()Outputs from Scan are not optional and should never be null.TransposeOutput/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_Attempt to retrieve final output before it was set./Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.cconnxruntime::scan::detail::Info::Info(const Node &, const GraphViewer &, int, bool)num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given void onnxruntime::scan::detail::ReadDirections(const OpKernelInfo &, const std::string &, TensorShapeVector &, size_t)directions.size() == num_entriesNumber of entries in '' was validInvalid values in ''. 0 == forward. 1 == reverse.Subgraph must have the shape set for all outputs but did not.AllocateOutputCreateFeedsFetchesManagerIterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceFailed to create output tensor for output #AllocateFinalBufferStatus onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const TensorShape &)!is_concrete_shape_If shape was concrete we shouldn't be using a custom allocatorAllocateFinalOutputOrtValue &onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratorMismatch between expected shape and shape from first output is not compatible with typename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = OrtValue]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsRegisterCPUKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constT2/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase>::ConstantOfShapeBase(const OpKernelInfo &) [EnabledOutputTypeList = onnxruntime::TypeList]t_proto_p->dims_size() == 1Must have a single dimensiont_proto_p->dims()[0] == 1Must have a single dimension of 1void onnxruntime::ConstantOfShapeBase>::SetValueFromTensorProto(const onnx::TensorProto &) [EnabledOutputTypeList = onnxruntime::TypeList]utils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())!utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.SetValueFromTensorProtoUnsupported value attribute datatype: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: PrepareComputeMust have a valid input shape.input count mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of mean/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()seedinfo.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()highonnxruntime::RandomUniform::RandomUniform(const OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDOutput type not supported in this build: MultinomialComputestart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Base(const OpKernelInfo &) [T = float]min_ <= max_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = float]min->Shape().IsScalar()min should be a scalar.max->Shape().IsScalar()max should be a scalar.void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = double]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = int]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = unsigned int]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = long long]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = unsigned long long]Axis tensor must be provided to the CumSum opAxis tensor should be 0D or 1DAxis tensor should be of type `int32_t` or `int64_t`CumSumexclusivereverseCannot apply CumSum operator on a scalar/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/cumsum.ccvirtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkips(const TensorShape &, gsl::span, gsl::span)dims.size() == extents.size() && dims.size() >= steps.size()void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = float]dims.size() == starts.size()dims.size()=starts.size()=dims.size() == extents_.size()extents.size()=dims.size() == steps.size()steps.size()=void onnxruntime::SliceIteratorBase::Init(gsl::span, gsl::span, gsl::span)dims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = double]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = double]virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = int]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = int]virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = long long]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = long long]Det/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/det.ccvirtual Status onnxruntime::Det::Compute(OpKernelContext *) const [T = float]X != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedEinsumEinsum op: There must be atleast one inputThere was a problem acquiring temporary memory allocator in Einsum op/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.ccDeviceComputeEinsum op: An implementation for the input type is not supported yetequation/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.honnxruntime::Einsum::Einsum(const OpKernelInfo &)info.GetAttr("equation", &equation_).IsOK()Missing 'equation' attribute->/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccstd::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = float]input_1.DataType() == input_2.DataType()Data types of the inputs must match for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Only 1 batch dimension is allowed for MatMulinput_shape_1_override[0] == input_shape_2_override[0]Batch dimension should match for MatMul;input_shape_1_override[2] == input_shape_2_override[1]Incompatible matrix dimensions for matMulEinsum op: Exception during MatMul operation: std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = long long]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = onnxruntime::MLFloat16]Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const Tensor &, Tensor &, void *)output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const Tensor &, int64_t, int64_t, AllocatorPtr, void *)rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]Cannot parse the diagonal elements along dims for input shape bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const gsl::span &)input_rank == permutation.size()The rank of the input must match permutation size for Transposestd::unique_ptr onnxruntime::EinsumOp::Transpose(const Tensor &, const TensorShape &, const gsl::span &, AllocatorPtr, void *, const DeviceHelpers::Transpose &)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const Tensor &, bool, AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()num_broadcasted_indices < num_of_ellipsis_dims_The broadcasted dimensions of the inputs are incompatibledim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedOutput subscript contains repeated lettersOutput subscript contains letters not seen in the inputsRank of the input must match number of subscript labels corresponding to the input/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccstd::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = float]left.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Left shape: Left shape override: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_rank == right_rankRanks of pair-wise operands must be equal. Right shape: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsEinsum op: Input shapes do not alignvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = float]candidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = long long]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = onnxruntime::MLFloat16]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = onnxruntime::MLFloat16]BitwiseAndBitwiseNotBitwiseOrBitwiseXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual Status onnxruntime::Min_6::Compute(OpKernelContext *) const [T = float]inputCount >= 1Must have 1 or more inputsdata_n.Shape() == shapeAll inputs must have the same shapevirtual Status onnxruntime::Max_6::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::Mean_6::Compute(OpKernelContext *) const [T = float]data_1.Shape() == shapevirtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Floor]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Floor]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Ceil]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Ceil]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sqrt]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sqrt]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Exp]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Exp]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Log]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Log]virtual Status onnxruntime::Sum_6::Compute(OpKernelContext *) const [T = float]input_count >= 1virtual Status onnxruntime::Sum_6::Compute(OpKernelContext *) const [T = double]directiononnxruntime::BitShift::BitShift(const OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.auto onnxruntime::BitShift::Compute(OpKernelContext *)::(anonymous class)::operator()(BroadcastHelper &) const [T = unsigned char]cur1 == end1cur_out == end_outonnxruntime::BitShift::BitShift(const OpKernelInfo &) [T = unsigned int]auto onnxruntime::BitShift::Compute(OpKernelContext *)::(anonymous class)::operator()(BroadcastHelper &) const [T = unsigned int]onnxruntime::BitShift::BitShift(const OpKernelInfo &) [T = unsigned long long]auto onnxruntime::BitShift::Compute(OpKernelContext *)::(anonymous class)::operator()(BroadcastHelper &) const [T = unsigned long long]void onnxruntime::UntypedExpand(OpKernelContext &, const ProcessBroadcastSpanFuncs &)shape_data_tensor.Shape().GetDims().size() == 1Tensor with shape information must be 1 dimensional./Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::Broadcaster::Broadcaster(gsl::span, gsl::span)largest <= 1Can broadcast 0 by 0 or 1. is invalid.void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)void onnxruntime::ExpandBroadcastLooper(BroadcastHelper &, const ProcessBroadcastSpanFuncs &)!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::InputBroadcaster::AdvanceBy(size_t)offset % span_size_ == 0InputBroadcaster can only start at span boundary!fmodonnxruntime::Mod::Mod(const OpKernelInfo &)(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, Tensor &, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) for tensor of length:start_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) are not at boundary of span with size:onnxruntime::TensorAllocator::TensorAllocator(OpKernelContext &)Unsupported Y type: void onnxruntime::mod_internal::CallModImpl::operator()(bool, OpKernelContext *) const [T = float, Enable = void]fmod attribute must be true for floating point typesvoid onnxruntime::mod_internal::CallModImpl::operator()(bool, OpKernelContext *) const [T = double, Enable = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, OpKernelContext *) constvoid onnxruntime::BroadcastLooper(TBroadcastHelper &, const ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::BroadcastHelper]helper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.Prepacking of B is supported by MLAS half gemm API, but not implemented by this kernel yet/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_base.honnxruntime::GemmBase::GemmBase(const OpKernelInfo &)info.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.hvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = double]c_shape != nullptrc_shape is required if c_data is providedonnxruntime::GemmHelper::GemmHelper(const TensorShape &, bool, const TensorShape &, bool, const TensorShape &)left.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2left[i] >= 0right[i] >= 0GEMM: Dimension mismatch, W: K: N:Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0/Users/runner/work/1/s/include/onnxruntime/core/framework/allocator.hstatic void onnxruntime::IAllocator::ValidateAllocator(const T &) [T = std::shared_ptr]allocator != nullptrstatic void onnxruntime::IAllocator::ValidateAllocation(void *, size_t)p != nullptr || size == 0Memory allocation failed. Size=void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = onnxruntime::MLFloat16]void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hleft_num_dims and right_num_dims must be >= 1MatMul dimension mismatchStatus onnxruntime::MatMulComputeHelper::Compute(const TensorShape &, const TensorShape &, bool, bool, bool, bool, bool)left_num_dims > 2 && left_num_dims == right_num_dimsTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dims/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13SoftmaxCPU inputs N, D and N * D must be < k argument [] should not be greater than specified axis dim value [k/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.ccvoid onnxruntime::TopkOpset9ConstructorCommon(const OpKernelInfo &, int &, unsigned int &)op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const OpKernelInfo &, int &)void onnxruntime::TopkOpset11ConstructorCommon(const OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedoutput count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valuek tensor should be a 1D tensor of size 1value of k must not be negativeArrayFeatureExtractorai.onnx.mlInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=BinarizerthresholdInput data with index: is NaNCastMapInvalid input type of value: Expected std::map or std::map0.fUnexpected CAST_TO value of cast_to/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMap(const OpKernelInfo &)info.GetAttr("cast_to", &attr).IsOK()map_forminfo.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_form_ != PACK_MAP::SPARSE || max_map_ > 0max_map must be > 0 if map_form is SPARSETO_FLOATTO_STRINGTO_INT64/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/ml_common.hCAST_TO onnxruntime::ml::MakeCast(const std::string &)Invalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSESPARSEPACK_MAP onnxruntime::ml::MakePack(const std::string &)Invalid PACK_MAP value of Expected DENSE or SPARSE/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.ccStatus onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = float, TTo = float]cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of const T *onnxruntime::OpKernelContext::Input(int) const [T = std::map]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = std::string, TTo = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::map]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = float, TTo = long long]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = std::string, TTo = long long]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = float, TTo = std::string]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = std::string, TTo = std::string]CategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const OpKernelInfo &)cats_int64sdefault_stringdefault_int64num_entries == int_categories.size()DictVectorizerstring_vocabulary/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = std::string, TargetType = long long]info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()const T *onnxruntime::OpKernelContext::Input(int) const [T = std::map]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = std::string, TargetType = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::map]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = std::string, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::map]int64_vocabularyonnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = long long, TargetType = std::string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = long long, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::map]FeatureVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccvirtual Status onnxruntime::ml::FeatureVectorizer::Compute(OpKernelContext *) constinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (input_tensor_ptr != nullptrInvalid input type:inputdimensions/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const OpKernelInfo &)status.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedImputerimputed_value_floatsimputed_value_int64sreplaced_value_float/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/imputer.cconnxruntime::ml::ImputerOp::ImputerOp(const OpKernelInfo &)Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.virtual common::Status onnxruntime::ml::ImputerOp::Compute(OpKernelContext *) constInvalid typeEmpty value of imputed values.Empty input dimensions.LabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsvalues_strings_Unusedkeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sclasses_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const OpKernelInfo &)info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = float, TValue = std::string]num_keys == num_values attributes in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = std::string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = std::string, TValue = std::string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = float, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = long long, TValue = std::string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = std::string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = long long, TValue = long long]keys_tensorvalues_tensoronnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = long long]keys.size() == values.size()Keys and values must have the same length.std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = long long]result.IsOK()LabelEncoder is missing attribute or LabelEncoder could not unpack tensor attribute onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = std::string]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = float]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = float, TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = float, TValue = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = short]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = double, TValue = double]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = double, TValue = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = double, TValue = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = double]default_tensorT onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = long long]LabelEncoder could not unpack default tensor T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = std::string]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = float]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = short]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = double]LinearClassifiermulti_classpost_transformNONEinterceptsclasslabels_stringsclasslabels_intscoefficients/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const OpKernelInfo &)!coefficients_.empty()void onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, ptrdiff_t, ptrdiff_t, ptrdiff_t, const std::vector &, const std::vector &, Tensor &, Tensor &, POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constscores_output_data.size() >= scores_output_sizeScores output is incorrect size. Expected: Found:Input shape needs to be at least a single dimension.Unsupported input element type of LOGISTICSOFTMAXSOFTMAX_ZEROvoid *onnxruntime::IAllocator::AllocArray(size_t, size_t)Invalid size requested for allocation: * void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, POST_EVAL_TRANSFORM, int, bool, concurrency::ThreadPool *) [T = float]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of void onnxruntime::ml::CastInputToFloat(const Tensor &, gsl::span &) [SrcType = int]shape_size == out.size()void onnxruntime::ml::CastInputToFloat(const Tensor &, gsl::span &) [SrcType = long long]void onnxruntime::ml::CastInputToFloat(const Tensor &, gsl::span &) [SrcType = double]LinearRegressortargets/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const OpKernelInfo &)Input shape had more than 2 dimension. Dims=Unsupported data type of Normalizernorm/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizer(const OpKernelInfo &)info.GetAttr("norm", &norm).IsOK()MAXL1L2NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/onehotencoder.cconnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = long long]tmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definednum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = std::string]Scaleroffset/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/scaler.cconnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = float]!scale_.empty()Empty scale in attributesscale_.size() == offset_.size()Scale size: () != (Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = double]onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = long long]onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = int]SVMClassifiervectors_per_classprob_aprob_bsupport_vectorsrho/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const OpKernelInfo &)proba_.size() == probb_.size()info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0kernel_typeLINEARkernel_params/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.hSVMCommononnxruntime::ml::SVMCommon::SVMCommon(const OpKernelInfo &)POLYRBFSVMRegressorn_supports/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const OpKernelInfo &) [T = float]one_classInvalid argumentUnexpected mode:static size_t onnxruntime::IAllocator::ValidatedCalcMemSizeForArray(size_t, size_t)TreeEnsembleClassifier/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = float]base_values_as_tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.hvirtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = float, ThresholdType = float, OutputType = float]nodes_hitrates_as_tensornodes_values_as_tensortarget_weights_as_tensoraggregate_functionSUMbase_valuesn_targetsnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuestarget_idstarget_nodeidstarget_treeidstarget_weightsStatus onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = float, ThresholdType = float, OutputType = float]n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()base_values.empty()nodes_modes.size() < std::numeric_limits::max()Node in tree is already there.(coor.node_id >= 0 && coor.node_id < n_nodes_)Unable to find node (truenode).A node cannot point to itself: (falsenode).Number of nodes in nodes_ () is different from n_nodes ( (weights).AVERAGEMINBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQsize_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = float, ThresholdType = float, OutputType = float]Tree id mismatch. Expected but got at position False node must always be the next node, but it isn't at index with flags virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = float, ThresholdType = float, OutputType = float]Unknown aggregation function in TreeEnsemble.void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]TreeEnsemble only works on 1D, 2D tensors.One path in the graph requests feature but input tensor has features./Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hvoid onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = float, ThresholdType = float, OutputType = float]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = float, ThresholdType = float, OutputType = float]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]predictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = float, ThresholdType = float, OutputType = float]predictions.size() == (size_t)n_targets_or_classes_void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = float, ThresholdType = float, OutputType = float]class_weights_as_tensorclass_idsclass_nodeidsclass_treeidsclass_weightsclasslabels_int64scomputevirtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = float, ThresholdType = float, OutputType = float]predictions.size() == 2int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]classes.size() == 2 || classes.size() == 1X dims is empty.classlabels_int64sbase_values_as_tensoronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = double]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = double, ThresholdType = double, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = double, ThresholdType = double, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = double, ThresholdType = double, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = long long]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = long long, ThresholdType = float, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = long long, ThresholdType = float, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = long long, ThresholdType = float, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = int]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = int, ThresholdType = float, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = int, ThresholdType = float, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = int, ThresholdType = float, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]Attribute:'' is specified but is empty./Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_helper.ccStatus onnxruntime::ml::GetNumberOfElementsAttrsOrDefault(const OpKernelInfo &, const std::string &, onnx::TensorProto_DataType, size_t &, onnx::TensorProto &)n_dims == 1Attribute '' must be a vector.proto.data_type() == proto_typeUnexpected type ((for attribute ''.n_elements > 0' has one dimension but is empty.Status onnxruntime::ml::GetVectorAttrsOrDefault(const OpKernelInfo &, const std::string &, onnx::TensorProto_DataType, std::vector &) [TH = double](std::is_same::value)GetVectorAttrsOrDefault not implemented for type GetVectorAttrsOrDefaultStatus onnxruntime::ml::GetVectorAttrsOrDefault(const OpKernelInfo &, const std::string &, onnx::TensorProto_DataType, std::vector &) [TH = float](std::is_same::value)TreeEnsembleRegressor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const OpKernelInfo &) [T = float]target_weightsbase_values_as_tensoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const OpKernelInfo &) [T = double]ZipMap/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOp(const OpKernelInfo &)classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T *OrtValue::GetMutable() [T = std::vector>]T *OrtValue::GetMutable() [T = std::vector>]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccInput dimension cannot be less than 3.Unsupported pooling size.Index tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const OpKernelInfo &)kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]Pad should be smaller than kernel.strides_.size() == kernel_shape_.size()Utraining_modemomentum/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const OpKernelInfo &) [T = float]is_spatial_Training mode only supports spatial BNvirtual Status onnxruntime::BatchNorm::Compute(OpKernelContext *) const [T = float]!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))Invalid number of outputs for BN trainingInvalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: onnxruntime::BatchNorm::BatchNorm(const OpKernelInfo &) [T = double]virtual Status onnxruntime::BatchNorm::Compute(OpKernelContext *) const [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv.ccoutput and sum shape must matchX num_dims does not match W num_dims. X: W: Input channels C is not equal to kernel channels * group. C: kernel channels: group: Output channels M is not divisible by group. M: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape. channels_last: Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_attributes.hInferPadsAndOutputShapeInvalid input shape: ComputePadAndOutputShapeonnxruntime::ConvAttributes::ConvAttributes(const OpKernelInfo &)auto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposeoutput_paddingoutput_shapegroup count is <= 0filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hPrepareForComputevoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(TensorShape, int64_t, const TensorShapeVector &, const TensorShapeVector &, const TensorShapeVector &, const TensorShapeVector &, const int64_t, ConvPadVector *, TensorShapeVector *, bool) constdim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, AutoPadType, int64_t *, int64_t *, int64_t *) const*out_size >= 0`InlinedVector::at(size_type)` failed bounds check/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/dropout_op.hvirtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = float, T2 = float]!mask || mask->Shape() == X_shapeX and mask should have the same shapefloat onnxruntime::(anonymous namespace)::GetRatioOrDefault(const Tensor *) [T2 = float]ratio_tensor->Shape().Size() == 1ratio input should have a single value.0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = float, T2 = double]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const Tensor *) [T2 = double]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = double, T2 = float]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = double, T2 = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flatten(const OpKernelInfo &)info.GetAttr("axis", &axis_).IsOK()virtual Status onnxruntime::Flatten::Compute(OpKernelContext *) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisThe rank of input tensor must be >= axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNorm(const OpKernelInfo &) [T = float]op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cconnxruntime::LayerNormImpl::LayerNormImpl(const OpKernelInfo &, bool, bool)op_kernel_info.GetAttr("axis", &axis_).IsOK()Size of X.shape()[axis:] == . Size of scale and bias (if provided) must match this. Got scale size of and bias size of ComputeImpl/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNorm(const OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()pop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.ccvirtual Status onnxruntime::LRN::Compute(OpKernelContext *) const [T = float]X->Shape().NumDimensions() == 4/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.honnxruntime::LRN::LRN(const OpKernelInfo &) [T = float]info.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0f/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.cckernel_shape num_dims is not compatible with X num_dims./Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_attributes.hTensorShapeVector onnxruntime::PoolAttributes::SetOutputSize(const TensorShape &, int64_t, TensorShapeVector *, bool) constinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:void onnxruntime::PoolAttributes::InferOutputSize(gsl::span, TensorShapeVector *, TensorShapeVector *, bool) constinput_dims.size() >= 2void onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constUnsupported AutoPad Type.onnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()No kernel shape is set.info.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()dilations.size() == kernel_shape.size()Dilations dimensions should match kernel shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const OpKernelInfo &)info.GetAttr("p", &p_).IsOK()Unsupported kernel dimension : Unsupported pooling size : /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.honnxruntime::LpPoolV18::LpPoolV18(const OpKernelInfo &) [T = float]MaxRoiPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.ccvirtual Status onnxruntime::RoiPool::Compute(OpKernelContext *) const [T = float]R->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizepooled_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.hRoiPoolonnxruntime::RoiPool::RoiPool(const OpKernelInfo &) [T = float]pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrink(const OpKernelInfo &)op_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()TfIdfVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizer(const OpKernelInfo &)mode is requiredTFIDFTFIDFimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFmin_gram_lengthmin_gram_length is requiredimpl_->min_gram_length_ > 0Required min_gram_length must be positive: max_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= max_skip_countmax_skip_count is requiredimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: ngram_countsstatus.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredsize_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: ngram_indexesstatus.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowedweightsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: must be of equal sizepool_strings!pool_strings.empty()pool_strings must not be empty if specifiedpool_int64sstatus.IsOK() && !pool_int64s.empty()non-empty pool_int64s is required if pool_strings not providedend_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -grams(items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = long long, ForwardIter = gsl::details::span_iterator, Map = std::unordered_map>>]p.first->second->id_ == 0Duplicate ngram detected, size: id: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::string, ForwardIter = std::__wrap_iter *>, Map = std::unordered_map, std::unique_ptr>, std::hash, std::equal_to>]NonMaxSuppression/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(OpKernelContext *, PrepareContext &)boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.GetThresholdsFromInputsiou_threshold must be in range [0, 1].virtual Status onnxruntime::NonMaxSuppression::Compute(OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const OpKernelInfo &)0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchavg/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const OpKernelInfo &)Invalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Sampling ratio should be >=0, but it was RoiAlignBaseThe existing summation for max mode and sampling ratios besides 1 is incorrect and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign will be different.OOptionalOptionalHasElementOptionalGetElement/Users/runner/work/1/s/onnxruntime/core/providers/cpu/optional/optional_ops.ccThe TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))Trying to use OptionalGetElement on an optional type OrtValue which contains no datatype/Users/runner/work/1/s/onnxruntime/core/providers/cpu/optional/optional_ops.honnxruntime::Optional::Optional(const OpKernelInfo &)attr->has_tp()Optional op must have a TypeProto in the 'type' attribute if the attribute is presentPropagateInputOrtValueToFirstOutputOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableT3ConvInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/quantization/conv_integer.ccvirtual Status onnxruntime::ConvInteger::Compute(OpKernelContext *) constIsScalarOr1ElementVector(X_Zero_Point)Must be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(W_Zero_Point)Non per-tensor quantization is not supported now./Users/runner/work/1/s/onnxruntime/core/providers/cpu/quantization/dynamicquantizelinear.ccvirtual Status onnxruntime::DynamicQuantizeLinear::Compute(OpKernelContext *) const [T = unsigned char]x_ptr != nullptr/Users/runner/work/1/s/onnxruntime/core/providers/cpu/quantization/matmul_integer.ccvirtual Status onnxruntime::MatMulInteger::Compute(OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)MatmulInteger : B zero point is not validPer-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.T4/Users/runner/work/1/s/onnxruntime/core/providers/cpu/quantization/qlinearconv.ccstatic void onnxruntime::QLinearConv::ComputeOffset(OpKernelContext *, int64_t, ActType &, ActType &, uint8_t &) [ActType = unsigned char]IsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsValidQuantParam(W_zero_point, M)QLinearConv : filter zero point shape invalidW_zero_point_data[i] == W_zero_point_valueQLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.static std::vector onnxruntime::QLinearConv::ComputeOutputScale(OpKernelContext *, int64_t) [ActType = unsigned char]IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsValidQuantParam(W_scale, M)QLinearConv : filter scale shape invalidvirtual Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector &, int, bool &) [ActType = unsigned char]prepacked_buffers[0].get() == nullptrstatic void onnxruntime::QLinearConv::ComputeOffset(OpKernelContext *, int64_t, ActType &, ActType &, uint8_t &) [ActType = signed char]static std::vector onnxruntime::QLinearConv::ComputeOutputScale(OpKernelContext *, int64_t) [ActType = signed char]virtual Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector &, int, bool &) [ActType = signed char]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/quantization/quantize_linear.ccvirtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = signed char]DequantizeLinear into BFLOAT16 is not implemented yet.DequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.void onnxruntime::PrepareForQDQ(const TensorShape &, const Tensor &, const Tensor *, int64_t, int64_t &, int64_t &, int64_t &)zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)x_zero_point must be null or a scalar or 1D tensor or size 1.scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = unsigned char]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = int]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })DequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FN]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FNUZ]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2FNUZ]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = unsigned short]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = short]saturatevirtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = signed char]Unsupported input type.virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = unsigned char]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FN]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FNUZ]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2FNUZ]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = unsigned short]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = short]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/quantization/quantize_linear_matmul.ccvirtual Status onnxruntime::QLinearMatMul::Compute(OpKernelContext *) constIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccvoid onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()last_loop_red_size > 0last_loop_size > 0projected_index.size() > 0void onnxruntime::ValidateNoTransposeReduce(int64_t)count == 1Reduction on all axes, output size should be 1.bool onnxruntime::CommonFastReduceCopy(OpKernelContext *, TensorShapeVector &, bool)axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a vector tensor.bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL1]axes.empty()Axes input and attribute should not both be present for reduction.bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL1]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL1]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_kernel_base.honnxruntime::ReduceKernelBase::ReduceKernelBase(const OpKernelInfo &, optional) [allow_multi_axes = false]info.GetAttr("keepdims", &keepdims).IsOK()bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMin]void onnxruntime::ValidateFastReduceKR(const gsl::span &, const Tensor &)fast_shape.size() == 2Only works on matrices with two dimensions.fast_shape[0] == output.Shape().Size()Output size mismatch.void onnxruntime::ValidateFastReduceRK(const gsl::span &, const Tensor &)fast_shape[1] == output.Shape().Size()void onnxruntime::ValidateFastReduceKRK(const gsl::span &, const Tensor &)fast_shape.size() == 3Only works on matrices with three dimensions.fast_shape[0] * fast_shape[2] == output.Shape().Size()void onnxruntime::ValidateFastReduceRKR(const gsl::span &, const Tensor &)void onnxruntime::ValidateMustBeOverloaded()must be overloaded.void onnxruntime::ValidateKeepDims(const TensorShape &, int64_t)Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ReduceKernelBase::ReduceKernelBase(const OpKernelInfo &, optional) [allow_multi_axes = true]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]GRUGRU operator does not support double yet/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccvirtual Status onnxruntime::DeepCpuGruOp::Compute(OpKernelContext *) constInvalid data type for GRU operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOp(const OpKernelInfo &)info.GetAttr("direction", &direction).IsOK()linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclipclip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2layoutlayout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.forwardbidirectional/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hDirection onnxruntime::rnn::detail::MakeDirection(const std::string &)Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.gsl::span onnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan() const [T = float]!is_prepacked_Can not get unpacked span from prepacked weightsvoid onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = gsl::details::span_iterator, TSpanBIter = gsl::details::span_iterator, TSpanCIter = gsl::details::span_iterator]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T *onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]cur + size <= endT *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span, size_t, size_t) [T = float]offset + size <= size_t(span.size())T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = gsl::details::span_iterator, TSpanBIter = gsl::details::span_iterator, TSpanCIter = gsl::details::span_iterator]LSTM/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccPrePackLSTM operator does not support double yetvirtual Status onnxruntime::DeepCpuLstmOp::Compute(OpKernelContext *) constInvalid data type for LSTM operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/lstm_base.honnxruntime::LSTMBase::LSTMBase(const OpKernelInfo &)input_forgetactivation_func_names.size() == static_cast(num_directions_) * 3/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/lstm_base.ccInput X must have 3 dimensions only. Actual:Input B must have shape {*}. Actual:Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {Input initial_c must have shape {Input P must have shape {RNN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffine/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNN(const OpKernelInfo &) [T = float]info.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - Input W must have shape {Input R must have shape {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string &, std::vector::const_iterator &, const std::vector::const_iterator &, std::vector::const_iterator &, const std::vector::const_iterator &, float &, float &)Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)A + (M * K) <= A_endvoid onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)weights.quant_para_alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fActivationFuncPtr onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string &)Invalid activation function of LstmMergeGatesFuncPtr onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string &)Invalid LSTM merge activation function of GruResetGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string &)Invalid GRU reset gate activation function: GruOutputGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string &)Invalid GRU hidden gate activation function: const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = const float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = float]SConcatFromSequence/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual Status onnxruntime::ConcatFromSequence::Compute(OpKernelContext *) constGot nullptr for sequence input./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concatbase.honnxruntime::ConcatBase::ConcatBase(const OpKernelInfo &, bool)Must have valid 'axis' attributenew_axisSequenceLengthSequenceAtInvalid sequence index () specified for sequence of size (/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccSequenceEmptyvirtual Status onnxruntime::SequenceEmpty::Compute(OpKernelContext *) constUnsupported 'dtype' value: SequenceInsertData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceEraseSequenceConstructvirtual Status onnxruntime::SequenceConstruct::Compute(OpKernelContext *) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SplitToSequencesplit_size_sum () != split_dim_size (Status onnxruntime::SplitToSequence::ComputeImpl(OpKernelContext &, const Tensor &, const Tensor *) constsplit_scalar > 0Split should be > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' input. All values must be >= 0int64_t onnxruntime::GetSeqIdx(const Tensor &)CloneTensorTensor onnxruntime::CloneTensor(const Tensor &, OpKernelContext *, const DataTransferManager &)int64_t onnxruntime::GetScalarSplitInput(const Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const Tensor &, InlinedVector &)DFTSTFT/Users/runner/work/1/s/onnxruntime/core/providers/cpu/signal/dft.ccvirtual Status onnxruntime::STFT::Compute(OpKernelContext *) constUnsupported input signal shape. The signal's first dimenstion must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs.Unsupported input data type of onesidedinversediscrete_fourier_transformdft_length must be a scalar value.dft_length must be greater than zero.Status onnxruntime::discrete_fourier_transform(OpKernelContext *, int64_t, bool, bool)Unsupported input signal shape. The signal's first dimension must be the batch dimension and its second dimension must be the signal length dimension. It may optionally include a 3rd dimension of size 2 for complex inputs./Users/runner/work/1/s/onnxruntime/core/providers/cpu/signal/utils.hT onnxruntime::signal::get_scalar_value_from_tensor(const Tensor *) [T = long long]tensor->Shape().Size() == 1T onnxruntime::bit_reverse(T, unsigned int) [T = unsigned long]Unsupported bit size.dft_bluestein_z_chirpStatus onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = float, U = float]signal_components == 1 || signal_components == 2signal shape must end in 1 (real) or 2 (real, imaginary).frame_length == window_lengthIf both frame_length and window are set, then the size of the window must be equal to the frame_length.window_size < signal_sizeEnsure that the dft size is smaller than the signal.short_time_fourier_transformStatus onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = float, U = std::complex]Status onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = double, U = double]Status onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = double, U = std::complex]HannWindowHammingWindowBlackmanWindowMelWeightMatrixperiodicoutput_datatype/Users/runner/work/1/s/onnxruntime/core/providers/cpu/signal/window_functions.ccStatus onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = float]lowest_index >= 0 && lowest_index < num_spectrogram_binslower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.highest_index >= 0 && highest_index < num_spectrogram_binsupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = double]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = signed char]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = short]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = int]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = long long]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned char]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned short]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned int]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned long long]T onnxruntime::signal::get_scalar_value_from_tensor(const Tensor *) [T = float]AffineGridAffineGrid : Input theta tensor dimension is not 3AffineGrid : Invalidate size - length of size should be 4 or 5./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/cast_op.cconnxruntime::(anonymous namespace)::Cast::Cast(const OpKernelInfo &)Attribute to is not set.Attribute saturate is only used for cast to float 8 types.NaNINF%.8gtypename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = float]snprintf_result > 0snprintf() failed with return value: snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1Failed to write value with snprintf().typename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = double]Col2Im/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/col2im.honnxruntime::Col2Im::Col2Im(const OpKernelInfo &) [T = float]strides_.empty()dilations_.empty()pads_.empty()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/col2im.ccvirtual Status onnxruntime::Col2Im::Compute(OpKernelContext *) const [T = float]dilations_.size() == image_dim_numbersize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.pads_.size() == 2 * image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.strides_.size() == image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.Compress/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual Status onnxruntime::Compress::Compute(OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccStatus onnxruntime::ConcatBase::PrepareForCompute(OpKernelContext *, const InlinedTensorsVector &, Prepare &) constinput != nullptrCannot concatenate scalarsinput_rank == reference_rankRanks of input data are different, cannot concatenate them. expected rank: got: Non concat axis dimensions must match: Axis has mismatched dimensions of Data type mismatch/Users/runner/work/1/s/onnxruntime/core/framework/copy.hStatus onnxruntime::DispatchStridedCopy(concurrency::ThreadPool *, Tensor &, std::ptrdiff_t, const TensorShapeVector &, const TensorShape &, const Tensor &, std::ptrdiff_t, const TensorShapeVector &) [EnabledDataTypes = onnxruntime::TypeList]dst.DataType() == src.DataType()src and dst types must matchvoid onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = std::string]dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()src and dst must have same shape and not be rank 0.total_num_elements_to_copy >= 0copy shape must have non-negative sizeauto onnxruntime::StridedCopy(concurrency::ThreadPool *, std::string *, const TensorShapeVector &, const TensorShape &, const std::string *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constlast >= firstcounter.current_offset == lastvoid onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned int]auto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned int *, const TensorShapeVector &, const TensorShape &, const unsigned int *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constvoid onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned long long]auto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned long long *, const TensorShapeVector &, const TensorShape &, const unsigned long long *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constvoid onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned short]auto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned short *, const TensorShapeVector &, const TensorShape &, const unsigned short *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constvoid onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned char]auto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned char *, const TensorShapeVector &, const TensorShape &, const unsigned char *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constinvalid expand shapeEyeLikeEyeLike : Input tensor dimension is not 2Tind/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.ccGather Tind type not supported in this build./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gatherbase.honnxruntime::GatherBase::GatherBase(const OpKernelInfo &)Missing/Invalid 'axis' attribute valueindices element out of data bounds, idx= must be within the inclusive range [GatherElementsGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.ccvoid onnxruntime::core_impl(const Tensor *, const Tensor *, Tensor *, int64_t, concurrency::ThreadPool *) [Tin = int]GatherElements op: Unsupported tensor type, size:GatherElements op: Out of range value in index tensorint64_t onnxruntime::GetIndex(size_t, const T *, int64_t) [T = int]void onnxruntime::core_impl(const Tensor *, const Tensor *, Tensor *, int64_t, concurrency::ThreadPool *) [Tin = long long]int64_t onnxruntime::GetIndex(size_t, const T *, int64_t) [T = long long]indices tensor must has rank larger than 0invalid index found, index = GatherNDindices/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.ccvirtual Status onnxruntime::GatherND::Compute(OpKernelContext *) constinput_tensor != nullptr && indices_tensor != nullptrGatherNDBase PrepareForCompute: Input count mismatchlast dimension of indices must not be larger than rank of input tensorindices tensor data type not supportedbatch_dimscubic/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/grid_sample.honnxruntime::GridSample::GridSample(const OpKernelInfo &) [T = float]mode "" not supported, expect linear, nearest or cubicbilinearbicubic" not supported, expect bilinear, nearest or bicubicpadding_modereflectionborderpadding_mode "" not supported, expect zeros, border or reflection/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/grid_sample.ccvirtual Status onnxruntime::GridSample::Compute(OpKernelContext *) const [T = float]static_cast(grid_dims.NumDimensions()) == data_dims + 2grid dimensions must be for input dimension of grid_dims[grid_dims.NumDimensions() - 1] == data_dimsLast dimension of grid: , expect input_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5Only 4-D or 5-D tensor is supportedgrid_dims[0] == NGrid batch size does not match input batch size mode_ != CubicOnly support GridSample Cubic mode in 4-D cases.onnxruntime::GridSample::GridSample(const OpKernelInfo &) [T = double]virtual Status onnxruntime::GridSample::Compute(OpKernelContext *) const [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/identity_op.hvirtual Status onnxruntime::IdentityOp::Compute(OpKernelContext *) const [is_dropout = true]Unable to get an allocatorvirtual Status onnxruntime::IdentityOp::Compute(OpKernelContext *) const [is_dropout = false]detect_positive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/isinf.cconnxruntime::IsInf::IsInf(const OpKernelInfo &)Failed to obtain detect_positivedetect_negativeFailed to obtain detect_negativeNull input ptrnormalize_variance/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.ccNo valid axes are specified. This is not handled now.MeanVarianceNormalizationacross_channelsComputeMeanVarianceNormalization2DX and Y must both have M * N elements./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccvirtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = bool]X input is required!failed to get first output!virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = unsigned char]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = int]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = long long]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = float]OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual Status onnxruntime::Pad::Compute(OpKernelContext *) constpads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]axes_tensor_dims.size() == 1Axes tensor should be a 1D tensor pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorreflectedgewrap/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/padbase.honnxruntime::PadBase::PadBase(const OpKernelInfo &)Invalid 'mode' attribute valueInvalid 'pads' attribute valuePadValue onnxruntime::PadValueFromFloat(float, MLDataType)Status onnxruntime::PadImpl(OpKernelContext *, const PadsVector &, const PadsVector &, const Mode &, T) [T = unsigned int]data_rank > 0Input tensor has no dimensionsdata_rank * 2 == pads.size()'pads' has wrong number of valuesPadInputWithDimValueOfZeroStatus onnxruntime::PadImpl(OpKernelContext *, const PadsVector &, const PadsVector &, const Mode &, T) [T = unsigned long long]Status onnxruntime::PadImpl(OpKernelContext *, const PadsVector &, const PadsVector &, const Mode &, T) [T = unsigned char]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1(const OpKernelInfo &)Attribute shape is not set.virtual Status onnxruntime::Reshape::Compute(OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1A shape tensor must be a vector tensor./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsamplebase.honnxruntime::UpsampleBase::UpsampleBase(const OpKernelInfo &)info.GetAttr("mode", &mode).IsOK()antialias(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.UpsampleBasekeep_aspect_ratio_policystretchextrapolation_value`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset model uses the deprecated attributecubic_coeff_aexclude_outside can be set to 1 when (1 mode is CUBIC. (2 mode is CUBIC or LINEAR when anti-aliasing is on. Current mode is set to and anti-aliasing is set to UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string &)mode attribute is . It can only be (default) or ScalesValidationScale value should be greater than or equal to 1.Scale value should be greater than 0.'Linear' mode only supports: * 2-D inputs or * 3-D inputs ('Bilinear', 'Trilinear') or * 4-D inputs with the corresponding outermost 2 scale values being 1 or the corresponding outermost and innermost scale values being 1 or * 5-D inputs with the corresponding outermost 2 scale values being 1in the Resize operatorUpsample operator'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the not_largernot_smallerAspectRatioPolicy onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicy(const std::string &)keep_aspect_ratio of [] is not supported!pytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizehalf_pixel_symmetricResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)coordinate_transform_mode:[round_prefer_ceilceilResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string &)nearest_mode:[ParseScalesDatascales size should be greater than 0.all values in axes should be less than rank of the dataReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual Status onnxruntime::ReverseSequenceOp::Compute(OpKernelContext *) constUnknown tensor type of batch_axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOp(const OpKernelInfo &)info.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are Invalid sequence length: . Value must be in range [0,gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]Data type is not supported in this build.ScatterScatterElements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.cconnxruntime::Scatter>::Scatter(const OpKernelInfo &) [EnabledDataTypes = onnxruntime::TypeList]reductiondata type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position= vs Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=Indices type is not supported.addmulScatterDataScatterElements op: input tensor must have at least one dimensionCPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.ScatterNDinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.Size/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccFillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchCannot slice scalars/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBase(const OpKernelInfo &, bool)has_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attribute!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesauto onnxruntime::SliceImpl(OpKernelContext *, const Tensor &, SliceOp::PrepareForComputeMetadata &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccUnsupported input type in SpaceToDepth op: Unsupported input type in DepthToSpace op: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBase(const OpKernelInfo &)info.GetAttr("blocksize", &blocksize_).IsOK()Attribute blocksize is not set.CRDonnxruntime::DepthToSpace::DepthToSpace(const OpKernelInfo &)DepthToSpace op: only 'DCR' and 'CRD' modes are supportedSpaceDepth ops require a 4-D input. Provided rank: SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeDepthToSpace requires input depth to be a multiple of (block_size * blok_size)Invalid num_outputs value of . Size of dimension being split is Input cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.ccvirtual Status onnxruntime::SplitImpl::Compute(OpKernelContext *) constsplit_tensor->Shape().NumDimensions() == 1The split tensor must be a vector tensor./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase(const OpKernelInfo &, uint32_t)std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' attribute. All values must be > 0If 'num_outputs' is specified, the 'split' input should not be provided./Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/squeeze.hvirtual Status onnxruntime::Squeeze::Compute(OpKernelContext *) constaxes_tensor != nullptrAxes input is nullstatic TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const TensorShape &, const TensorShapeVector &)input_shape[i] == 1Dimension of input must be 1 instead of . shape=Input count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/tile.ccvirtual Status onnxruntime::Tile::Compute(OpKernelContext *) constTile doesn't have an implementation yet for the type: Transpose of element size not supported in this build. Size=Mismatched data types between input and output Tensors. /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual Status onnxruntime::Transpose::Compute(OpKernelContext *) constvoid onnxruntime::DoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const std::string *, std::string *)num_axes > 0Transpose not implemented for empty tensors.(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::IncrementIndexAndComputeOffsetSetup(MultiIndex &, size_t, gsl::span, const gsl::span &, size_t)naxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span &, const std::string *, std::string *)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span &, const uint8_t *, uint8_t *, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)perm size: does not match input rank: [ perm: does not align with rank of input data: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBase(const OpKernelInfo &)v >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned long long](local_source >= source) && (local_source < source + sizeof(T) * num_blocks)bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned int]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned short]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned char]Trilu/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/trilu.ccvirtual Status onnxruntime::Trilu::Compute(OpKernelContext *) constIsScalarOr1ElementVector(k)k should be a 1-D or 0-D tensor.upper/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/trilu.honnxruntime::Trilu::Trilu(const OpKernelInfo &)info.GetAttr("upper", &temp).IsOK()UniqueUnsupported tensor type of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.ccStatus onnxruntime::UnsqueezeBase::PrepareCompute(OpKernelContext *, Prepare &) constaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a scalar or a 1-D tensor.'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute value/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccInvalid roi input index.Only one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.BaseComputeRank of input and output tensor should be same.Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.4-D input with innermost scale (usually channel of NHWC) as 1.: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeUpsample: unexpected modeUpsampleNearestResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.ParseSizesDataResize: input tensor's rank does not match the output tensor's rank.axes should be less than output_dims.size()ParseScalesDataAndAdjustOutputSizeInput dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: RegexFullMatchpattern/Users/runner/work/1/s/onnxruntime/core/providers/cpu/text/regex_full_match.cconnxruntime::RegexFullMatch::RegexFullMatch(const OpKernelInfo &)re_.ok()Invalid regex pattern: /Users/runner/work/1/s/include/onnxruntime/core/framework/op_node_proto_helper.hGetAttrT onnxruntime::OpNodeProtoHelper::GetAttr(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext, T = std::string]StringConcatStringNormalizerConversion Erroren_US.UTF-8is_case_sensitive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/text/string_normalizer.cconnxruntime::StringNormalizer::StringNormalizer(const OpKernelInfo &)attribute is_case_sensitive is not setcase_change_actionattribute case_change_action is not setLOWERUPPERattribute case_change_action has invalid valuelocalestopwords!sw.empty()Empty stopwords not allowedp.secondDuplicate stopwords not allowedwstr != wconv_errorStopword contains invalid utf8 charsSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 charsauto onnxruntime::string_normalizer::Locale::Locale(const std::string &)::(anonymous class)::operator()() constFailed to construct locale with name::Please, install necessary language-pack-XX and configure localeswstring_convert: from_bytes errorwstring_convert: to_bytes errorStringSplitmaxsplitdelimiterstring_view::substronnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::ParametricSoftplus]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::ScaledTanh]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::ScaledTanh]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention(AllocatorPtr, const logging::Logger &, int, int, int, int, int, bool, concurrency::ThreadPool *) [T = float]!normalize_not support normalize yet.virtual void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span &, const gsl::span &) [T = float]mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, AttnLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(OpKernelContext *) constAttention mechanism memory shape error! Expected: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:ValidateInputs/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const OpKernelInfo &)static_cast(activation_func_names.size()) == num_directions_ * 3 [seqno= row[ rows[../Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_base.honnxruntime::contrib::AttentionBase::AttentionBase(const OpKernelInfo &, bool)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0do_rotaryrotary_embedding_dimqkv_hidden_sizespast_present_share_buffer/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.ccRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.hApplyAttention4D mask in attention cpu kernel is not supportedAttention cannot have both past and relative_position_biasInput 'input' is expected to have 3 dimensions, got Input 'bias' is expected to have 1 dimension, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads:qkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be sameInput 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes: q_hidden_size= k_hidden_size= v_hidden_size=bias_dims[0]=Input 'past' expect k_hidden_size == v_hidden_sizeInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of past_sequence_length tensor must be of one element when past_present_share_buffer is setwhen past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got Input 'relative_position_bias' dimension 1 should be same as number of heads, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, got Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2Inputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got num_heads should be no larger than /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_base.ccTensor *onnxruntime::contrib::AttentionBase::GetPresent(OpKernelContext *, const Tensor *, int, int, int, int &) constExpect to have present state output when past state input is given/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/bias_gelu.ccvirtual Status onnxruntime::contrib::BiasGelu::Compute(OpKernelContext *) const [T = float, use_approximation = false]use_approximationInput 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0BifurcationDetectormin_ngram_size/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/bifurcation_detector.honnxruntime::contrib::BifurcationDetector::BifurcationDetector(const OpKernelInfo &)info.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_size_ > 0max_ngram_sizeinfo.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ > 0max_ngram_size_ >= min_ngram_size_virtual Status onnxruntime::contrib::BifurcationDetector::Compute(OpKernelContext *) constsrc_tokens_len >= prev_suffix_match_idx_datapred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBase(const OpKernelInfo &)epsilon_ >= 0input index out of rangeinput_ids and position_ids shall have same sequence_lengthposition_ids's first dimension shall be 1 or batch_sizeInput 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1gamma is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have 1 dimensions, got beta is expected to have size of MultiHeadAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/multihead_attention.cconnxruntime::contrib::MultiHeadAttention::MultiHeadAttention(const OpKernelInfo &) [T = float]Packed QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUInput 'query' is expected to have 3 or 5 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' dimension 0 should be batch_size, got Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' dimension 1 should be same as number of heads, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). Input 'past_key' dimension 3 should be same as head_size, got Input 'past_value' dimension 3 should be same as head_size, got Input 'past_key' and 'past_value' shall be both present or both absentInput 'query' is expected to have 3 dimensions when key is given, got Input 'key' is expected to have 3, 4, or 5 dimensions, got Input 'query' and 'key' shall have same dim 0 (batch size)Input 'query' and 'key' shall have same dim 2 (hidden_size)Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvExpect 'value' be none when 'key' has packed kv format.Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size) for past_keyInput 'query' is expected to have 3 or 5 dimensions when key is empty, got Expect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv'bias' is not allowed for packed kv. Input 'key_padding_mask' shape shall be 1D, 2D, or 3DInput 'value' is expected to have 3 or 4 dimensions, got Input 'query' and 'value' shall have same dim 0 (batch_size)Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Input 'relative_position_bias' dimension 0 should be batch_size or 1, got MaybeTransposeToBNSHAndAddBiasAddBiasReshapeAddBiasTransposeTidNGramRepeatBlockngram_size/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/ngram_repeat_block.honnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlock(const OpKernelInfo &)info.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_size_ > 0virtual Status onnxruntime::contrib::NGramRepeatBlock::Compute(OpKernelContext *) constinput_ids_dims.size() == 2scores_dims.size() == 2scores_dims[0] == batch_sizeauto onnxruntime::contrib::NGramRepeatBlock::Compute(OpKernelContext *)::(anonymous class)::operator()(int64_t) consttoken_id < vocab_sizeMRotaryEmbeddinginterleaved/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/rotary_embedding.cconnxruntime::contrib::RotaryEmbedding::RotaryEmbedding(const OpKernelInfo &) [T = float]num_heads > 0num_heads must be provided if rotary_embedding_dim is specifiedUpdating cos_cache and sin_cache in RotaryEmbedding is not currently supportedInput 'x' is expected to have 3 or 4 dimensions, got Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got Input 'cos_cache' is expected to have 2 dimensions, got Input 'sin_cache' is expected to have 2 dimensions, got Inputs 'cos_cache' and 'sin_cache' are expected to have the same shapenum_heads must be provided if rotary_embedding_dim is specifiedrotary_embedding_dim must be less than or equal to head_sizeInput 'position_ids' dimension 0 should be of size batch_size, got Input 'position_ids' dimension 1 should be of size sequence_length, got Input 'cos_cache' dimension 0 should be same as max_sequence_length, got Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got CDistmetric/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cdist.honnxruntime::contrib::CDist::CDist(const OpKernelInfo &) [T = float]info.GetAttr("metric", &metric).IsOK()sqeuclideaneuclideanThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:onnxruntime::contrib::CDist::CDist(const OpKernelInfo &) [T = double]ConvTransposeWithDynamicPads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterQuantizationKernelsRegisterCpuContribKernelsCrop/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop.hAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (CropAndResize/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize(const OpKernelInfo &) [T = float] specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1DynamicSliceScale/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Affine::Affine(const OpKernelInfo &) [T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const OpKernelInfo &) [T = float]info.GetAttr("scale", &scale_).IsOK()ExpandDims/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/expand_dims.hvirtual Status onnxruntime::contrib::ExpandDims::Compute(OpKernelContext *) constaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.axis <= X_NumDims && axis >= -X_NumDimsAxis must be within range []. Axis is unimplemented activation: activation_params count mismatch/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const OpKernelInfo &)GetFusedActivationAttr(info, activation_).IsOK()/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const OpKernelInfo &) [T = float]TransposeMatMulImageScaler/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const OpKernelInfo &) [T = float]Bias size () does not match the number of channels (InverseSparseToDenseMatMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/math/sparse_dense_matmul.ccCurrently supporting only 2-D matricesCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: Expecting COO 2-D indices shapeExpecting 2xValues == indicesExpecting the same number NNZ == size of Inner indicesOuter size must be M + 1Currently support only COO and CSR(x64) formatsCOO indices must be 2-D, got: COO k index: is out of bounds of lhs_right: COO m index: is out of bounds of out_left: /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/matmul_fpq4.ccvirtual Status onnxruntime::contrib::MatMulFpQ4::Compute(OpKernelContext *) constblob_shape.NumDimensions() == 1Second input of MatMulFpQ4 must be a 1D blob!b_shape.NumDimensions() == 2Right hand side of MatMulFpQ4 must be a 2D matrix!buf_size > 0Operator MatMulFpQ4 not yet supported on this hardware platform.(size_t)blob_len == buf_sizeQuantized and packed blob size differ from expected!MatMulFpQ4blk_quant_typeMaxpoolWithMask/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hMurmurHash3/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual Status onnxruntime::contrib::MurmurHash3::Compute(OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytesInvalid assumption of output element sizeinput_num_bytes % 4 == 0positive/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual Status onnxruntime::contrib::ReorderInput::Compute(OpKernelContext *) constX_rank == 4(channels % 4) == 0virtual Status onnxruntime::contrib::ReorderOutput::Compute(OpKernelContext *) constchannels_ <= X_shape[1]virtual Status onnxruntime::contrib::NchwcConv::Compute(OpKernelContext *) constX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(OpKernelContext *, MLAS_POOLING_KIND) const(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual Status onnxruntime::contrib::NchwcUpsample::Compute(OpKernelContext *) constX_shape.size() == 4/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderInput::ReorderInput(const OpKernelInfo &)info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutput(const OpKernelInfo &)info.GetAttr("channels", &channels_).IsOK()channels_ > 0invalid channel countonnxruntime::contrib::NchwcConv::NchwcConv(const OpKernelInfo &)onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const OpKernelInfo &)pool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const OpKernelInfo &)info.GetAttrs("scales", scales_).IsOK()scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode '' for NCHWc Upsampletransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode '/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccinput scale must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccInput W_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:W_scaleDynamicQuantizeLSTM : Weight zero point must be zeroWeight point must be constantRecurrent/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccComputeCommonStatus onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(OpKernelContext *, const uint8_t *, const TensorShape &, float, uint8_t, bool, const Tensor *, const Tensor *, const Tensor *, const Tensor *) constIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)MatmulInteger : b zero point is not validvirtual Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/matmul_bnb4.ccMatMulBnb4Konnxruntime::contrib::MatMulBnb4::MatMulBnb4(const OpKernelInfo &)Status::OK() == info.GetAttr("K", &K_)NStatus::OK() == info.GetAttr("N", &N_)block_sizeStatus::OK() == info.GetAttr("block_size", &block_size_)quant_typeStatus::OK() == info.GetAttr("quant_type", &quant_type_)quant_type_ == FP4 || quant_type_ == NF4Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dequantize_blockwise_bnb4.hvoid onnxruntime::contrib::DequantizeBlockwiseBnb4(T *, const uint8_t *, const T *, int32_t, int32_t, int32_t, int32_t, onnxruntime::concurrency::ThreadPool *) [T = float]quant_type == FP4 || quant_type == NF4only block size 16, 32, 64, 128, 256 are supported.MatMulInteger16/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/matmul_integer16.ccvirtual Status onnxruntime::contrib::MatMulInteger16::Compute(OpKernelContext *) const [T1 = short, T2 = short, T3 = int]A != nullptr && B != nullptr/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.ccMatMulNBitsbitsaccuracy_levelonnxruntime::contrib::MatMulNBits::MatMulNBits(const OpKernelInfo &)nbits_ == 4Only 4b quantization is supported for MatMulNBits op, additional bits support is planned.T onnxruntime::OpNodeProtoHelper::GetAttr(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext, T = long long]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccvirtual Status onnxruntime::contrib::NhwcMaxPool::Compute(OpKernelContext *) const [T8Bits = signed char]input_shape.Size() > 0 || N == 0virtual Status onnxruntime::contrib::NhwcMaxPool::Compute(OpKernelContext *) const [T8Bits = unsigned char]QEmbedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qembed_layer_norm.ccWord embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1QLinearLeakyReluQLinearSigmoidQLinearAddQLinearMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_binary_op.ccvoid onnxruntime::contrib::(anonymous namespace)::QLinearImpl(OpKernelContext &, double, const ProcessBroadcastSpanFuncs &) [T = signed char]IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenvoid onnxruntime::BroadcastLooper(TBroadcastHelper &, const ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::contrib::(anonymous namespace)::QLinearBroadcastHelper]void onnxruntime::contrib::(anonymous namespace)::QLinearImpl(OpKernelContext &, double, const ProcessBroadcastSpanFuncs &) [T = unsigned char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcat(const OpKernelInfo &)input_def_count >= 5 && (input_def_count - 2) % 3 == 0Each input must be (tensor, scale, zero_point) tuple!tensor_x_scale->IsDataType()Input scale is not float for input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @virtual Status onnxruntime::contrib::QLinearConcat::Compute(OpKernelContext *) constinput_count_x3 >= 3 && input_count_x3 % 3 == 0Input scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @QLinearConcat/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_global_average_pool.ccvirtual Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(OpKernelContext *) constIsScalarOr1ElementVector(tensor_x_scale)Input x_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_x_zero_point)input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale)input y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_zero_point)input y_zero_point must be a scalar or 1D tensor of size 1 if given/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_lookup_table.ccvoid onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const Tensor *, const Tensor *, const Tensor *, const Tensor *, const LookupTableArrayTransformer &) [T = unsigned char]QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const Tensor *, const Tensor *, const Tensor *, const Tensor *, const LookupTableArrayTransformer &) [T = signed char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_pool.ccStatus onnxruntime::contrib::QLinearAveragePool::ComputeImpl(OpKernelContext *) const [T8Bits = signed char]QLinear Pooling unsupported pooling size!Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(OpKernelContext *) const [T8Bits = unsigned char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_softmax.cconnxruntime::contrib::QLinearSoftmax::QLinearSoftmax(const OpKernelInfo &)opset must be existed in attributes of QlinearSoftmaxQLinearSoftmaxvoid onnxruntime::contrib::(anonymous namespace)::BuildLookupTableIfFixed(const OpKernelInfo &, std::vector &, size_t, bool)tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/qlinear_where.cconnxruntime::contrib::QLinearWhere::QLinearWhere(const OpKernelInfo &)input_def_count == kExpected_input_countThere must be inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()Wrong input type encountered for zero point input def of x, y, zvirtual Status onnxruntime::contrib::QLinearWhere::Compute(OpKernelContext *) constInput scale is not float for quantized input x @ 2tensor_y_scale->IsDataType()Input scale is not float for quantized input y @ 5tensor_z_scale->IsDataType()Input scale is not float for quantized output z @ 7QLinearWhereTATBTCTYZTY/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/quant_gemm.ccCheckInputsQGemm : scale of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NQGemm : zero point and scale of input b should have same shape sizeQGemm : zero point of y must be null or a scalar or 1D tensor of size 1QGemm : scale of y must be null or a scalar or 1D tensor of size 1void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = int]SampleOpSkipSimplifiedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = float, simplified = false]skip is expected to have 3 or 2 dimensions, got skip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsinput is expected to have 3 or 2 dimensions, got last two dimensions of skip needs to be same as inputgamma is expected to have 1 dimension, got Last dimension of gamma and input does not matchbeta is expected to have 1 dimension, got Last dimension of beta and input does not matchbias is expected to have 1 dimension, got Last dimension of bias and input does not matchonnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = float, simplified = true]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = double, simplified = false]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = double, simplified = true]Tokenizermark/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const OpKernelInfo &)attribute mark is not setpad_valueattribute pad_value is not setmincharnumattribute mincharnum is not setmincharnum_ > 0attribute mincharnum must have a positive valueseparatorstokenexpEither one of the separators OR tokenexp attributes required but none is set!tokenexp.empty()Expecting a non-empty tokenexp!separators.empty()separators must not be empty!char_tokenezation_ || mincharnum_ < 2mincharnum is too big for char level tokenezationCan not digest separators: Can not digest tokenexp: Input string contains invalid utf8 charsInput string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedBeamSearchWhisperBeamSearch/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search.ccvoid onnxruntime::contrib::transformers::BeamSearch::Init(const OpKernelInfo &)parameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperencoderinfo.GetAttr("encoder", &proto).IsOK()init_decoderdecoderinfo.GetAttr("decoder", &proto).IsOK()virtual Status onnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)gpt_subgraph_ == nullptrinit_run_gpt_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availablet5_decoder_subgraph_ == nullptrwhisper_encoder_subgraph_ == nullptrEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)whisper_decoder_subgraph_ == nullptrvirtual Status onnxruntime::contrib::transformers::BeamSearch::Compute(OpKernelContext *) constdecoder_session_stateSubgraph SessionState was not found for 'decoder' attribute.decoder_feeds_fetches_manager_init_run_decoder_session_stateinit_run_decoder_feeds_fetches_manager_init_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_past_present_share_buffer mode must be same for init decoder and decoder subgraphesencoder_session_stateSubgraph SessionState was not found for 'encoder' attribute.encoder_feeds_fetches_manager_Model type is not supported./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_base.hInput 'input_features' is expected to have 3 dimensions, got Input 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_ids' is expected to have 2 dimensions, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'presence_mask' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'presence_mask' shape[1] shall be vocab_size, got decoder_output_cross_qkmin_lengthmax_lengthnum_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'.Node input should be a scalar. Got shape of is required/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_gpt.hGenerateNextToken/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_t5.hStatus onnxruntime::contrib::transformers::BeamSearchT5::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = float]parameters->sequence_length == 1Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = onnxruntime::MLFloat16]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_whisper.hStatus onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = float]parameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizeno_speech_token_id is out of range, it is , vocab_size is decoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with has_decoder_masked_attentiondecoder_subgraph_.past_present_share_buffer_decoder subgraph: output_cross_qk could only work with past_present_share_bufferinput_tensor_cross_qk_layer_head != nullptrMust specify input cross_qk_layer_headStatus onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = onnxruntime::MLFloat16]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search_parameters.ccValidateeos_token_id is invalidpad_token_id is invalidmin_length shall be smaller than max_lengthmodel_typeearly_stoppingeos_token_idpad_token_iddecoder_start_token_idno_repeat_ngram_sizevocab_sizevoid onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputs(OpKernelContext *)context != nullptrdims.size() == 3input_features shall have 3 dimensions. Got decoder_dims.size() == 2decoder_input_ids shall have 2 dimensions. Got dims.size() == 2input_ids shall have 2 dimensions. Got extra_decoder_tensor_dims.size() == 2extra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor_dims[0] == batch_sizeextra_decoder_tensor first dim not same as batch_size. Got , expecting max_length > sequence_lengthmax_length () shall be greater than input sequence length (max_length <= kMaxSequenceLength) shall be no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_beams shall be a positive integer no more than num_return_sequences >= 1num_return_sequences shall be a positive integer, got num_beams >= num_return_sequencesnum_return_sequences () shall be be no more than num_beams (repetition_penalty > 0.0frepetition_penalty shall be greater than 0, got logits_processor >= 0logits_processor shall be a non-negative integer, got virtual void onnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributes(const OpKernelInfo &)model_type == IGenerationParameters::kModelTypeWhispertranslate_token_idtranscribe_token_idstart_of_lm_token_idno_speech_token_idno_timestamps_token_idbeginning_timestamp_token_id/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/beam_search_scorer.ccvoid onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span &, gsl::span &)top_k <= beams_used_virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Process(ISequences &, gsl::span &, gsl::span &, gsl::span &)next_scores.size() == next_tokens.size()next_scores.size() == next_indices.size()beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)Batch can only be done if all beams have been generatedbeam_idx == num_beams_static_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Finalize(ISequences &, gsl::span &, Tensor *, Tensor *)output_sequences != nullptr/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/generation_device_helper.ccStatus onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogits(const OrtValue &, transformers::IBeamSearchState *, transformers::ISequences *, AllocatorPtr &, onnxruntime::concurrency::ThreadPool *, transformers::ILogitsProcessorList *, transformers::IBeamScorer *, const transformers::IGenerationParameters *, int, Stream *, const transformers::IConsoleDumper *) [T = float]logits_shape.NumDimensions() == 3ProcessLogitsStatus onnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogits(const OrtValue &, transformers::IGreedySearchState *, transformers::ISamplingState *, transformers::ISequences *, AllocatorPtr &, onnxruntime::concurrency::ThreadPool *, transformers::ILogitsProcessorList *, const transformers::IGenerationParameters *, bool, int, Stream *, const transformers::IConsoleDumper *) [T = float]GreedySearchProcessLogitsStatus onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(AllocatorPtr, Stream *, const std::vector &, std::vector &, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, transformers::Sequences &, const transformers::IConsoleDumper *) [T = float]last_outputs.size() >= static_cast(1) + num_present_tensorsStatus onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(AllocatorPtr, Stream *, const std::vector &, std::vector &, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, transformers::Sequences &, const transformers::IConsoleDumper *) [T = onnxruntime::MLFloat16]void onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputs(const OrtValue &, int, AllocatorPtr, OrtValue &) [T = int]element_type == DataTypeImpl::GetType()Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(Stream *, const OrtValue &, int, AllocatorPtr, OrtValue &, bool, int) [T = int]is_kv_cacheStatus onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(Stream *, const OrtValue &, int, AllocatorPtr, OrtValue &, bool, int) [T = float]Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(Stream *, const OrtValue &, int, AllocatorPtr, OrtValue &, bool, int) [T = onnxruntime::MLFloat16]Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const Tensor *, const OrtValue *, int, AllocatorPtr, OrtValue &, OrtValue &) [T = float]input_features_shape.NumDimensions() == 3start_token_id >= 0original_decoder_input_ids_shape.NumDimensions() == 2Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const Tensor *, const OrtValue *, int, AllocatorPtr, OrtValue &, OrtValue &) [T = onnxruntime::MLFloat16]BeamSearch op: An implementation for the input type Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputs(const Tensor *, const OrtValue *, int, int, gsl::span &, AllocatorPtr, OrtValue &, OrtValue &, OrtValue &)input_ids_shape.NumDimensions() == 2Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputs(const Tensor *, const OrtValue *, int, int, AllocatorPtr, OrtValue &, OrtValue &, OrtValue &)CPU beam search current not support output cross QK./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/sampling_cpu_helper.hSampleGreedySearch/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/greedy_search.ccvoid onnxruntime::contrib::transformers::GreedySearch::Init(const OpKernelInfo &)parameters_.model_type == IGenerationParameters::kModelTypeGptvirtual Status onnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)Not Implementedvirtual Status onnxruntime::contrib::transformers::GreedySearch::Compute(OpKernelContext *) const/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_base.h/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_gpt.h/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/greedy_search_parameters.ccvoid onnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs(OpKernelContext *)Sampling/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/sampling.ccvoid onnxruntime::contrib::transformers::Sampling::Init(const OpKernelInfo &)virtual Status onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)virtual Status onnxruntime::contrib::transformers::Sampling::Compute(OpKernelContext *) consttemperaturetop_pfilter_valuemin_tokens_to_keeppresence_penaltycustom/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/sampling_parameters.ccvoid onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputs(OpKernelContext *)seed >= 0Seed must be >= 0DecoderMaskedSelfAttentionDecoderMaskedMultiHeadAttentionpast_sequence_lengthbeam_width/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/subgraph_base.ccSetupGetParameterssubgraph past state is expected to have 5 dimension, got subgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 4 shall have a positive value for hidden size per headsubgraph output present_key_self_0 is expected to have 4 dimension, got subgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary size/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/subgraph_gpt.ccStatus onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeeds(const Tensor &, const std::vector &, int, int, gsl::span &, OrtValue &, const OrtValue *, std::vector &, const GenerationDeviceHelper::CreateGptInputsFunc &, const GenerationDeviceHelper::AddToFeedsFunc &, IAllocatorUniquePtr &, Stream *, int, bool)session_state_ != nullptrSetup must be called before CreateInitialFeedsCreateInitialFeedsInvalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).Invalid GPT-2 subgraph: number of inputs shall be number of outputs plus 2 or 3 (if past_present_share_buffer) or 5 (if past_present_share_buffer and use_decoder_masked_self_attention for BeamSearch)input_idssubgraph input 0 shall be named as input_ids, got: position_idssubgraph input 1 shall be named as position_ids, got: attention_masksubgraph input 2 shall be named as attention_mask, got: past_0subgraph input 3 shall be named as past_0, got: logitssubgraph output 0 shall be named as logits, got: present_0subgraph input 1 shall be named as present_0, got: subgraph input 0 (input_ids) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 typesubgraph output 0 (logits) shall be float or float16 data typesubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 1 (present_0) shall shall have same data type of logits outputencoder_hidden_states/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.ccdecoder_masked_attention shall use with past_present_share_buffernumber of inputs expected to be kFirstPastInputIndex + 4 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of outputs expected to be 1 + 2 * layers, got:decoder subgraph input 0 shall be named as input_ids, got: encoder_attention_maskdecoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph output 0 shall be named as logits, got: decoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph output shall have same data type as that of encoder_hidden_statesStatus onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeeds(AllocatorPtr, gsl::span, const std::vector &, const std::vector &, const std::vector &, std::vector &, const GenerationDeviceHelper::DeviceCopyFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, int, Stream *, bool, int, transformers::Sequences &, int, bool)Invalid hidden_states expension: has_hidden_state_ == false/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_encoder.ccexpect 3 inputs, got:expect >=6 outputs, got:number of outputs expected to be 2 + 4 * layers, got:encoder_input_idsencoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder_input_idsencoder subgraph input 2 shall be named as decoder_input_ids, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph output 1 shall be named encoder_hidden_states, got: present_key_self_0encoder subgraph output 2 shall be named as present_key_self_0, got: present_value_self_0encoder subgraph output 3 shall be named as present_value_self_0, got: encoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph output 0 (logits) shall be float or float16 data typeencoder subgraph outputs 1, 2, ... shall have same data typeStatus onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeeds(const Tensor &, const OrtValue *, const std::vector &, int, int, std::vector &, const GenerationDeviceHelper::CreateEncoderInputsFunc &, const GenerationDeviceHelper::AddToFeedsFunc &, IAllocatorUniquePtr &, OrtValue &, Stream *)cpu_allocator shouldn't be nullptr/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_decoder.ccnumber of outputs expected to be first_present_output_index_ + 2 * layers, got:When outputing cross qk, number of outputs expected to be first_present_output_index_ + 3 * layers, got:decoder subgraph input 1 shall be named as encoder_hidden_states, got: decoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_states.Status onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeeds(AllocatorPtr, gsl::span, const std::vector &, const std::vector &, const std::vector &, std::vector &, const GenerationDeviceHelper::DeviceCopyFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, int, Stream *, bool, int, transformers::Sequences &, int, bool)/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_encoder.ccexpect 2 inputs, got:encoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeStatus onnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeeds(const Tensor &, const OrtValue *, int, const std::vector &, std::vector &, const GenerationDeviceHelper::CreateWhisperEncoderInputsFunc &, const GenerationDeviceHelper::AddToFeedsFunc &, IAllocatorUniquePtr &, OrtValue &, Stream *)Input tensor to Unique op should be 1DConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccWordConvEmbeddingembedding_sizeconv_window_sizechar_embedding_sizeAllocateAllocateStaticallyPreExistingReuseShareAllocatedExternallyNotSetAllocation Plan: (ort_value_idx) output_name : : Index out-of-range! Execution Plan: Start logic stream: on device: End logic stream : /Users/runner/work/1/s/onnxruntime/core/framework/allocation_planner.ccCreatePlanvirtual Status onnxruntime::DeviceBasedPartitioner::PartitionGraph(const onnxruntime::GraphViewer &, const ExecutionProviders &, std::vector> &, ExecutionOrder)iter != node_stream_map.end()Failed to find node "" in node-stream mapPartitioner type is not DeviceBasedPartitionerstreamsdevicesvoid onnxruntime::DeviceBasedPartitioner::Initialize()node_names_by_stream_.size() == device_types_.size()Number of streams does not equal to number of device types!DeviceBasedPartitionerSaveConfigCaught exception during saving DeviceBasedPartitioner config: CreateGraphPartitionerCaught exception when reading partition config file: Use DeviceBasedPartition as defaultvoid onnxruntime::PlannerImpl::PartitionIntoStreams(const logging::Logger &, const ExecutionProviders &, const PathString &)Can not find the node Status onnxruntime::PlannerImpl::ComputeValueLocation()p_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the execution provider ComputeValueLocationOrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName &)void onnxruntime::PlannerImpl::ProcessDef(OrtValueIndex, const onnxruntime::NodeArg *)id >= 0 && static_cast(id) < ort_value_info_.size()const KernelCreateInfo &onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap &, NodeIndex)entry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:auto onnxruntime::PlannerImpl::ComputeValueLocation()::(anonymous class)::operator()(const NodeArg &, size_t) constfound_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location mapAllocPlanPerValue &onnxruntime::PlannerImpl::AllocPlan(OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()void onnxruntime::PlannerImpl::GeneratePlanForWeightsHelper(const GraphViewer &, const InitializedTensorSet &, const KernelCreateInfoMap &, const std::string &, size_t, std::vector> &)specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()OrtDevice onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const Node &, const KernelCreateInfoMap &)Status onnxruntime::PlannerImpl::BuildExecutionPlan(const ExecutionProviders &, const IStreamCommandHandleRegistry &)BuildExecutionPlanexecution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()trigger_point_it != node_to_trigger_points.end()ComputeReusePlanComputeReuseCountint &onnxruntime::PlannerImpl::UseCount(OrtValueIndex)n >= 0 && static_cast(n) < ort_value_info_.size()invalid value index: against size Status onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan(size_t)!IsNonTensor(*node_output)Only tensors are supported for external outputs for now.!is_strided_tensorStrided tensor is not supported in non-training build for now.void onnxruntime::PlannerImpl::Reuse(OrtValueIndex, OrtValueIndex, AllocKind)reused != reused_forstd::vector> onnxruntime::PlannerImpl::GetAliasMap(const Node &, const KernelCreateInfo &)kernel_create_info.kernel_def != nullptrKernelDef is null for node: static size_t onnxruntime::PlannerImpl::GetElementSize(const DataType &)nullptr != tensor_type_baseOrtValueIndex &onnxruntime::PlannerImpl::Buffer(OrtValueIndex)OptimizeReusePlanForMultiStream reused by as inputGenerateDeallocationPlancannot use operator[] with a string argument with cannot use insert() with {}{ ": , ":[][ "bytes": [], "subtype": {"bytes":[],"subtype":null}\u%04x\u%04x\u%04xinvalid UTF-8 byte at index : 0xincomplete UTF-8 string; last byte: 0x\ufffd�CudaOpenVINO_GPUHipWebGPU_BufferCudaPinnedHipPinnedSpecified device is not supported./Users/runner/work/1/s/onnxruntime/core/framework/allocator.cc nmemb= size= alignment=/Users/runner/work/1/s/onnxruntime/core/framework/allocator_utils.ccCreateAllocatorReceived invalid value of arena_extend_strategy /Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: max_power_of_two_extend_bytes: memory limit: arena_extend_strategy: Creating bins of max chunk size to onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, ArenaExtendStrategy, int, int, int, int64_t)BinForSize(bin_size * 2) != BinFromIndex(b)BFCArena::Chunk *onnxruntime::BFCArena::ChunkFromHandle(ChunkHandle)h < chunks_.size()Available memory of is smaller than requested bytes of Failed to allocate memory for requested buffer of size ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at ReserveReserving memory in BFCArena for size: virtual void *onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()size_t onnxruntime::BFCArena::RequestedSize(const void *)h != kInvalidChunkHandlesize_t onnxruntime::BFCArena::AllocatedSize(const void *)AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void *onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, Stream *, bool, WaitNotificationFn)BFCArena::Chunk *onnxruntime::BFCArena::FindChunkPtr(BinNum, size_t, size_t, Stream *, bool, WaitNotificationFn)!chunk->in_use()void onnxruntime::BFCArena::SplitChunk(BFCArena::ChunkHandle, size_t)!c->in_use() && (c->bin_num == kInvalidBinNum) BFC Arena shrunk by bytes. The total allocated bytes is now void onnxruntime::BFCArena::DeallocateRawInternal(void *)void onnxruntime::BFCArena::Merge(BFCArena::ChunkHandle, BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use() && c1->stream == c2->streamc2->prev == h1void onnxruntime::BFCArena::InsertFreeChunkIntoBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(BFCArena::Bin::FreeChunkSet *, const BFCArena::Bin::FreeChunkSet::iterator &)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binvoid onnxruntime::BFCArena::FreeAndMaybeCoalesce(BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)BFCArena::ChunkHandle onnxruntime::BFCArena::Coalesce(ChunkHandle)!c->in_use()std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_numDumpMemoryLogAllocator:Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use: Chunks . Bytes . Requested Diff between in-use and requested bytes is Bin for bytes has max bytes of , Chunk State: Overall chunks summary: Chunk Free at of size Summary of in-use chunks by size: chunks of size . Total Sum Total of in-use chunks: Stats: auto onnxruntime::BFCArena::Extend(size_t)::(anonymous class)::operator()(const size_t) constIncorrect arena extend strategy.cudaMallochipMalloc/Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion(void *, size_t, int64_t)0 == memory_size % kMinAllocationSizeRegionForCould not find Region for int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constp_int >= base_intp_int < base_int + memory_size_void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void *)entry != regions_.end()Could not find Region for: Size: | Requested Size: | in_use: , prev: , next: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: Config key is empty or longer than maximum length 128Config value is longer than maximum length: /Users/runner/work/1/s/onnxruntime/core/framework/config_options.ccAddConfigEntryConfig with key [] already exists with value []. It will be overwrittenCopyTensor/Users/runner/work/1/s/onnxruntime/core/framework/data_transfer.ccCopyTensorsCopySparseTensorsvirtual common::Status onnxruntime::CPUDataTransfer::CopyTensor(const Tensor &, Tensor &) constsrc.SizeInBytes() == dst.SizeInBytes()CopyTensorAsyncdata_transfer registered is nullptr.Tensor size mismatchThere's no data transfer registered for copying tensors from /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer_manager.cc/Users/runner/work/1/s/onnxruntime/core/framework/data_types.ccbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())virtual bool onnxruntime::OptionalTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kOptionalTypeutils::HasElemType(thisProto->optional_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kOpaqueTypevirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)floatbooldoubleint8uint8int16uint16int32uint32int64uint64float16bfloat16Float8E4M3FNFloat8E4M3FNUZFloat8E5M2Float8E5M2FNUZtensor type is not supportedsequence tensor type sparse tensor type MLDataType for: is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerChecker(MLDataType)Invalid DataTypeImpl TypeProto definitionbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto &, const onnx::TypeProto &)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(MLDataType)proto != nullptrOnly ONNX MLDataType can be registeredWe do not expect duplicate registration of types for: GetElementTypeGetDeleteFunc/Users/runner/work/1/s/include/onnxruntime/core/framework/data_types.hstatic void onnxruntime::data_types_internal::MapTypeHelper::Set(onnx::TensorProto_DataType, const onnx::TypeProto *, onnx::TypeProto &)value_proto != nullptrexpected a registered ONNX typestatic void onnxruntime::data_types_internal::SequenceTypeHelper::Set(const onnx::TypeProto *, onnx::TypeProto &)elem_proto != nullptrstatic void onnxruntime::data_types_internal::OptionalTypeHelper::Set(const onnx::TypeProto *, onnx::TypeProto &)/Users/runner/work/1/s/onnxruntime/core/framework/device_stream_collection.ccvoid onnxruntime::DeviceStreamCollectionImpl::AddDeviceStream(size_t, std::unique_ptr)idx < num_streams_void onnxruntime::DeviceStreamCollectionImpl::SetDeviceStream(size_t, Stream *)CleanUpStream *onnxruntime::DeviceStreamCollectionImpl::GetStream(size_t) conststream_idx < num_streams_/Users/runner/work/1/s/onnxruntime/core/framework/endian_utils.ccCopyLittleEndiansource and destination buffer size mismatch/Users/runner/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLibCaught exception while destructing CustomOpsLoader with message: Caught exception while loading custom ops with message: /Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const OrtValueNameIdxMap &, const NodeIndexInfo &, gsl::span)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedStatus onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(const int, int, const TensorShape *, OrtValue *&, const Node &)shape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:shape && sp_tensor.DenseShape() == *shapeinvalid index void onnxruntime::IExecutionFrame::Init(gsl::span, gsl::span, const std::unordered_map &, const std::function &, gsl::span)feeds.size() == feed_mlvalue_idxs.size()Get feed size: but expected feed size: fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of onnxruntime::ExecutionFrame::ExecutionFrame(gsl::span, gsl::span, gsl::span, gsl::span, const std::unordered_map &, const DeviceStreamCollection *, const SessionState &)buffers_.find(location) == buffers_.end()ExecutionFrameAllocation of memory pattern buffer for returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueAllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: but the actual size is: , fall back to default allocation behaviorStatus onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue &, int, MLDataType, const OrtDevice &, const TensorShape &)alloc && alloc.get() != nullptrFailed to get allocator for Status onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBuffer(OrtValue &, int, MLDataType, const OrtDevice &, const TensorShape &, bool)Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.AllocateMLValueTensorPreAllocateBufferAllocateReusedOrtValueIfNotAllocatedHelperStatus onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue &, int, const TensorShape *)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Allocation of tensor types requires a shape.AllocateAsPerAllocationPlanInvalid allocation kind: VerifyOutputSizesExpected shape from model of does not match actual shape of for output ReleaseMLValueImplTraceAllocateTraceAllocation for ort_value_idx=void onnxruntime::ExecutionFrame::TraceFree(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework./Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constoffset >= 0 && static_cast(offset) < node_values_size_Could not find OrtValue with idx 'onnxruntime::SparseTensor *OrtValue::GetMutable()/Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.hconst OrtValue &onnxruntime::IExecutionFrame::GetMLValue(int) constort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_StreamAwareArena *onnxruntime::AsStreamBasedAllocator(AllocatorPtr)allocator.get() != nullptrallocator is nullptr failed. Error:IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by /Users/runner/work/1/s/onnxruntime/core/framework/execution_provider.ccvirtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer &, HashValue &) constmetadef_id_generator_IExecutionProvider constructor must be called with true for use_metadef_id_creatorSet a barrier with id: , count: /Users/runner/work/1/s/onnxruntime/core/framework/execution_steps.ccvirtual Status onnxruntime::WaitOnEPStep::Execute(StreamExecutionContext &, size_t, SessionScope &, const bool &, bool &)wait_handle_WaitOnEPStep.wait_handle is nullstream wait on Notification with id: WaitOnEPStep: wait on notification with id: Launch kernel with node id: activate notification with index ActivateNotificationStep: activate notification with id: TriggerDownstreamStep: trigger downstream of trigger point: /Users/runner/work/1/s/onnxruntime/core/framework/fallback_cpu_capability.ccstd::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer &, const IExecutionProvider::IKernelLookup &, gsl::span)kernel_info != nullptrGetCpuPreferredNodesfloat8e4m3fnfloat8e4m3fnuzfloat8e5m2float8e5m2fnuzORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeCandidate for fallback CPU execution: /Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(DeviceCopyCheck, DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span, gsl::span, const OrtValueNameIdxMap &)func info for node: already exist.Can't use func with null ptr not found./Users/runner/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncs/Users/runner/work/1/s/onnxruntime/core/framework/graph_partitioner.ccInlineFunctionsAOTThis model does not have any local functions defined. AOT Inlining is not performedAOT inlining completed. () functions of () pruned.No provider specified.Partitionep.context_enableep.context_file_pathInlineFunctionsAOTImpl/Users/runner/work/1/s/onnxruntime/core/framework/kernel_lookup.honnxruntime::KernelLookup::KernelLookup(ProviderType, gsl::span>, const IKernelTypeStrResolver &)!provider_type_.empty()provider_type must be specified.PartitionOnnxFormatModelPartitionOnnxFormatModelImplThe Function Style fusion is deprecated. did not return correct number of compiled functionsGetCapabilityForEP cannot be used with this model due to its ONNX opset not being supported by the layout transformer.Node '' OpType: with domain: was inserted using the NHWC format as requested by , but was not selected by that EP. This means the graph is now invalid as there will not be an EP able to run the node. This could be a bug in layout transformer, or in the GetCapability implementation of the EP./Users/runner/work/1/s/onnxruntime/core/framework/func_kernel.hCreate state function failed. Return value:API VERSION InlineNodes_ctx.onnxCreateEpContextModelFailed to generate EP context model since the file exist already.PartitionOrtFormatModelImplsingle_node_compute_func should have 1 element./Users/runner/work/1/s/onnxruntime/core/framework/kernel_def_builder.ccKernelDefBuilder &onnxruntime::KernelDefBuilder::VariadicAlias(int, int)input_offset >= 0 && output_offset >= 0Kernel found kernel in the supported version range (node_version: However the types are incompatible. Op with name ( domain ( and type ( kernel is not supported in Encountered following errors: (Kernel not foundOp type (kernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions. Version mismatch. node_version: kernel start version: kernel_end_version: /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.ccMatchKernelDefTypesbool onnxruntime::(anonymous namespace)::MatchKernelDefTypes(const Node &, const std::unordered_map> &, const IKernelTypeStrResolver &, std::string &)static_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptrThis op has been implemented only for the following types (), but the node in the model has the following type ( However the types are incompatible.found duplicated provider in KernelRegistryManagerThe node is not placed on any Execution Provider. Failed to find kernel for (node:'' ep:''). /Users/runner/work/1/s/onnxruntime/core/framework/kernel_type_str_resolver.ccResolveKernelTypeStrFailed to find op_id: Failed to find args for kernel type string '' for node type ''. If type constraint names are available, ensure that they are used in the kernel def type constraints instead of op input or output names. Not doing so will result in this error.RegisterOpSchemaRegisterNodeOpSchemaOp schema must be available.RegisterGraphNodeOpSchemasKernel type string already exists for formal parameter name '', but the existing argument with that formal parameter name has a different formal parameter type string.op_kernel_type_str_args is null.op_id is null.kernel_type_str_args is null.kernel_type_str is null.args is null.Duplicate entry for kernel type str: Duplicate entry for op id: Invalid ORT format model./Users/runner/work/1/s/onnxruntime/core/framework/kernel_type_str_resolver_utils.ccSaveKernelTypeStrResolverToBufferktsrLoadKernelTypeStrResolverFromBufferFailed to verify KernelTypeStrResolver flatbuffers data.AddLayoutTransformationRequiredOpsToKernelTypeStrResolver/Users/runner/work/1/s/onnxruntime/core/framework/library_handles.ccUnloadLibraries/Users/runner/work/1/s/onnxruntime/core/framework/mldata_type_utils.ccMLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.ccauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes &, NodeIndex, const OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::GraphNodes &, NodeIndex, const OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ValidNodes> &, NodeIndex, const OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) const/Users/runner/work/1/s/onnxruntime/core/framework/onnxruntime_map_type_info.ccstatic std::unique_ptr OrtMapTypeInfo::FromTypeProto(const onnx::TypeProto &)value_case == ONNX_NAMESPACE::TypeProto::kMapTypetype_proto is not of type map!/Users/runner/work/1/s/onnxruntime/core/framework/onnxruntime_optional_type_info.ccstatic std::unique_ptr OrtOptionalTypeInfo::FromTypeProto(const onnx::TypeProto &)value_case == ONNX_NAMESPACE::TypeProto::kOptionalTypetype_proto is not of optional type/Users/runner/work/1/s/onnxruntime/core/framework/onnxruntime_sequence_type_info.ccstatic std::unique_ptr OrtSequenceTypeInfo::FromTypeProto(const onnx::TypeProto &)value_case == ONNX_NAMESPACE::TypeProto::kSequenceTypetype_proto is not of type sequence!/Users/runner/work/1/s/onnxruntime/core/framework/onnxruntime_typeinfo.ccstatic std::unique_ptr OrtTypeInfo::FromOrtValue(const OrtValue &)tensor_data_type != nullptrOrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled alreadyThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typestatic std::unique_ptr OrtTypeInfo::FromTypeProto(const onnx::TypeProto &)This TypeProto does not have ValueCase setThe type is not tensor, sparse tensor, sequence, map or optional type/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel.cconnxruntime::OpKernelContext::OpKernelContext(IExecutionFrame *, const OpKernel *, Stream *, concurrency::ThreadPool *, const logging::Logger &)frame != nullptrExecution frame was nullkernel != nullptrOpKernel was nullvirtual OrtValue *onnxruntime::OpKernelContext::OutputMLValue(int, const TensorShape &)virtual int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constarg_num < arg_counts.size()Invalid arg_num of . Num args is TempSpace allocator not foundCPU allocator not foundvirtual OrtValue *onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)int onnxruntime::NodeIndexInfo::GetNodeOffset(NodeIndex) constnode_offsets_index < node_offsets_size_No attribute with name: is defined.Requested attribute: is expected to have type: but is of type: const onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext]attr != nullptrconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnx::InferenceContext]Attribute name and type don't matchNo attribute with this name is defined./Users/runner/work/1/s/onnxruntime/core/framework/op_node_proto_helper.ccGetAttrsGetAttrs failed. Expect values.size()=Attribute: expected to be of type: /Users/runner/work/1/s/onnxruntime/core/framework/mem_pattern_planner.hvoid onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)!using_counters_current <= buffer_size_/Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.ccstatic OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = OrtValue]ort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:dim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, Direction) [T = OrtValue]const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [T = OrtValue]static OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = const OrtValue]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, Direction) [T = const OrtValue]const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [T = const OrtValue]/Users/runner/work/1/s/onnxruntime/core/framework/prepacked_weights.ccHashValue onnxruntime::PrePackedWeights::GetHash() constbuffers_.size() == buffer_sizes_.size()/Users/runner/work/1/s/onnxruntime/core/framework/prepacked_weights_container.ccAllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const std::string &)Unsupported device allocator in the context of pre-packed weights caching: /Users/runner/work/1/s/onnxruntime/core/framework/sequential_executor.cconnxruntime::Status onnxruntime::ExecuteKernel(StreamExecutionContext &, NodeIndex, size_t, const bool &, SessionScope &)Async Kernel Support is not implemented yet.Non-zero status code returned while running node. Name:'' Status Message: ExecuteKernelExecuteThePlan/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternal(const SessionState &, IExecutionFrame &, const OpKernel &, const logging::Logger &, const bool &, Stream *)entry != nullptrAll implicit inputs should have OrtValue instances by now. does not._fence_beforeop_name{"":[]}_kernel_timeprovidernode_indexactivation_sizeparameter_sizeoutput_sizeinput_type_shapeoutput_type_shapethread_scheduling_stats_fence_afterSequentialExecutor::Execute/Users/runner/work/1/s/onnxruntime/core/framework/session_options.ccAddInitializerAn OrtValue for this name has already been added: Status onnxruntime::SessionOptions::AddExternalInitializers(gsl::span, gsl::span)init_num == values.size()Expecting same size spansAddExternalInitializersReceived nullptr for nameReceived nullptr for OrtValueReceived OrtValue is not a tensor. Only tensors are supported.Buffer containing the initializer must be owned by the user./Users/runner/work/1/s/onnxruntime/core/framework/session_state.ccCreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.PopulateKernelCreateInfoconst KernelCreateInfo &onnxruntime::SessionState::GetNodeKernelCreateInfo(NodeIndex) constentry != kernel_create_info_map_.cend()CreateKernelsPruneRemovableAttributesfailed at retrieving the removable attributesfor node '' ('').removed removable attributes '), among attributes: duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?Using an input in multiple nodes on different devices is not supported currently. Input: is used by node () and node Failed to find input name in the mapping: void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const NodeInfo &)output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find output name in the mapping: void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string &, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute const NodeIndexInfo &onnxruntime::SessionState::GetNodeIndexInfo() constnode_index_info_.has_value()SetGraphAndCreateKernels must be called prior to GetExecutionInfo.CreateSubgraphSessionStateFinalizeSessionStatesession.node_partition_config_fileFinalizeSessionStateImplsession.disable_prepackingStatus onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string &, const KernelRegistryManager &, const Node *, const SessionOptions &, bool, InlinedHashMap &, const InlinedHashMap &, bool)entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:' Index: Attribute:p_op_kernelauto onnxruntime::SessionState::PrepackConstantInitializedTensors(InlinedHashMap &, const std::unordered_map &)::(anonymous class)::operator()(bool) constallocator_for_caching.get() != nullptrweights_to_be_filled_in.buffers_.size() > 0The kernel corresponding to the node doesn't have an implementation that can cache computed pre-packed weights!op_type.empty()The op type of a node cannot be emptyUsing cached version of pre-packed weight for constant initializer: used in the node: which is of op type: Unable to write the provided PrePackedWeights instance into the container+KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsVerifyEachNodeIsAssignedToAnEpNode placements All nodes placed on []. Number of nodes: Node(s) placed on [Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.Rerunning with verbose output on a non-minimal build will show node assignments.Could not find an implementation for ) node with name 'VerifyEachNodeIsAssignedToAnEpImplvoid onnxruntime::AccumulateAllNestedSubgraphsInfo(const SessionState &, const std::string &, size_t, SubgraphsKernelCreateInfoMaps &)subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()OuterScopeNodeArgLocationAccumulator/Users/runner/work/1/s/onnxruntime/core/framework/session_state_utils.ccSaveInitializedTensorsSaving initialized tensors.common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const Env &, const std::basic_string &, const GraphViewer &, const AllocatorPtr &, const OrtValueNameIdxMap &, const std::vector &, ITensorAllocator &, const SaveTensorFunction &, const logging::Logger &, const DataTransferManager &, const ExecutionPlanBase &, const SessionOptions &, const MemoryProfileFunction &)ort_value_name_idx_map.MaxIdx() > -1OrtValue indexes should have been populated.entry != initialized_tensors_to_allocate.end()OrtValue index: from initializer_allocation_order not found among initialized tensorsentry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGCan not trace string tensor[Memory] SessionStateInitializer statically allocates bytes for Skipping entry for missing optional value at idx Using user supplied initializer with name (session.use_device_allocator_for_initializersDeserialize tensor failed.Done saving initialized tensorsSaveInputOutputNamesToNodeMappingSubgraphGraph input with name is not used by any node.Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires , Got DeserializeTensorProtostring tensor is not supported for copying between allocatorsFailed to copy tensor to shape.Size() must >=0Failed memory size calculationcommon::Status onnxruntime::session_state_utils::ExtDataTensorProtoToTensor(const Env &, const std::basic_string &, const onnx::TensorProto &, Tensor &, OrtCallback &)utils::HasExternalData(tensor_proto)ExtDataTensorProtoToTensor/Users/runner/work/1/s/onnxruntime/core/framework/sparse_tensor.ccstatic const SparseTensor &onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue &)the ort_value must contain a constructed sparse tensorstatic SparseTensor &onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue &)this tensor already has populated sparse_indicesAllocateBufferValues size must be less than total buffer size: SparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constFormat() == SparseFormat::kCooMust contain Coo format. Got: format_data_.size() == 1UExpecting to contain one index, got: std::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constvalues_count == index_sizeIndex size: must be equal to or twice the values size: Sparse format must not be set. Already contains format: Not expecting an allocator setMakeCooDataUse MakeCooStringsMakeCooStringsExpecting data type to be set as stringSparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)Format() == SparseFormat::kUndefinedallocator_ != nullptrThis method should follow a call to constructor that supplies the allocatorSparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constFormat() == SparseFormat::kCsrcMust contain Csr format. Contains: format_data_.size() == 2UExpecting two indices. Got: ValidateCsrIndicesdense shape must 2-D. Got: Inner and Outer indices must either be both zero or non-zeroExpecting inner index size: the same as values size: Outer index count must be rows + 1 or zero. Got: rows: This method does not expect allocator to be setMakeCsrDataUse MakeCsrStringsMakeCsrStringsSparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)SparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constFormat() == SparseFormat::kBlockSparseMust contain BlockSparse format. Got: Expecting one index. Got: ValidateBlockSparseShapesExpecting to have at lest 3-D shape. Got:Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2Expecting index blocks: to be equal to values blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}MakeBlockSparseDataUse MakeBlockSparseStringsMakeBlockSparseStringsSparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const TensorShape &, const TensorShape &)CopyUnable to find a data transfer for copying from device type: to device type: This instance should not be emptyDestination should be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeMust have the same shapeCopyDataMust have the same size. Got src_size: dst_size: Currently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPU/Users/runner/work/1/s/onnxruntime/core/framework/sparse_utils.ccDenseTensorToSparseCsrUnsupported element size: Support 2-D matrices onlyInput must be of CSR formatUnable to convert strings tensor to a sparse tensor that is not on CPUStatus onnxruntime::sparse_utils::SparseCsrToDenseTensor(const DataTransferManager &, const SparseTensor &, const AllocatorPtr &, const AllocatorPtr &, Tensor &)inner_num == src.Values().Shape().Size()Expecting inner indices to be same as nnz. Got: outer_num == (rows + 1)Outer indices must be M + 1. Got: SparseCsrToDenseTensorInput must be of COO formatSparseCooToDenseTensorExpecting indices to be equal the number of values or be twice as manyInvalid index: > dense_size: DenseTensorToSparseCoo/Users/runner/work/1/s/onnxruntime/core/framework/stream_execution_context.ccStream *onnxruntime::StreamExecutionContext::GetDeviceStream(size_t)idx < device_stream_map_->NumStreams()void onnxruntime::StreamExecutionContext::RecycleNodeInputs(onnxruntime::NodeIndex)frame_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()Exiting due to terminate flag being set to true.void onnxruntime::RunSince(size_t, StreamExecutionContext &, SessionScope &, const bool &, size_t)since == end/Users/runner/work/1/s/onnxruntime/core/framework/stream_execution_context.hvoid onnxruntime::StreamExecutionContext::CountDownBarrier::Set(int32_t)v >= 0/Users/runner/work/1/s/onnxruntime/core/framework/tensor.ccstatic size_t onnxruntime::Tensor::CalculateTensorStorageSize(MLDataType, const TensorShape &)tensor failed memory size calculationonnxruntime::Tensor::Tensor(MLDataType, const TensorShape &, void *, const OrtMemoryInfo &, ptrdiff_t, gsl::span)elt_type != nullptronnxruntime::Tensor::Tensor(MLDataType, const TensorShape &, std::shared_ptr)onnxruntime::Tensor::Tensor(MLDataType, const TensorShape &, void *, std::shared_ptr, ptrdiff_t, gsl::span)size_t onnxruntime::Tensor::SizeInBytes() consttensor size overflowvoid onnxruntime::Tensor::Init(MLDataType, const TensorShape &, void *, AllocatorPtr, ptrdiff_t, gsl::span)dtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: /Users/runner/work/1/s/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hFinalizePlanFailed to get allocator for location: duplicated locationInternal error.Mem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedTracemodel format error! Need a key for the external data infomodel format error! Need a value for the external data infolocationparsing failedlengthchecksummodel format error!model format error! Missing 'location'/Users/runner/work/1/s/onnxruntime/core/framework/tensor_shape.ccint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constdimension <= num_dimsInvalid dimension of for SizeToDimension. Tensor has dimensions.int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const for SizeFromDimension. Tensor has TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.{}Tensor type is undefinedthe ort_value must contain a constructed tensor or sparse tensor/Users/runner/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatusPtr OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)Argument is not a tensorconst Tensor &(anonymous namespace)::GetIndicesTensor(const OrtValue &, OrtSparseIndicesFormat)Unsupported indices_format passedInvalid TensorProtoExternal data type cannot be STRING.corrupted protobuf data: tensor shape size() does not match the data size() in protoUnpackTensor: the pre-allocate size does not match the size in protodata overflow/Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccStatus onnxruntime::utils::GetExtDataFromTensorProto(const Env &, const char *, const onnx::TensorProto &, void *&, SafeInt &, OrtCallback &)GetExtDataFromTensorProto*/_ORT_MEM_ADDR_/*GetFileLength for External initializer: offset: size to read: given file_length: are out of bounds or can not be read in full.TensorProtoToTensor() tensor shape mismatch!TensorProto type can not be written into Tensor type TensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: ConstantNodeProtoToTensorProtoConstant node: has no data attributescommon::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, const Path &, onnx::TensorProto &, const std::string &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProtoElement_size of: is not supported.Unsupported sparse tensor data type of common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto &, const Path &, onnx::SparseTensorProto &)HasDataType(dense_proto)Must have a valid data typeDenseTensorToSparseTensorProto data_type: UnpackInitializerDataUnsupported type: The given initializer contains external dataUnpackTensorWithExternalDataImplnullptr == p_dataGetExternalDataInfoTensor does not have external data to read from.External data type cannot be UNDEFINED or STRING.TensorProto: external data size mismatch. Computed size: , external_data.length: GetFileContentMemBuffer has not been allocated.string tensor requires allocator to be provided.The preallocated buffer is too small. Requires TensorProtoToOrtValueImplCopySparseDataSparse Indices raw data size does not match expected.Sparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, const Path &, gsl::span, std::function)indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == indices_data.end()Invalid SparseTensor indices. Should be rank 0 or 1. Got:ReadExternalDataForTensorUnpackTensor: the pre-allocated size does not match the raw data size, expected /Users/runner/work/1/s/onnxruntime/core/framework/transpose_helper.ccTransposeSingleAxisOutwardsvoid onnxruntime::TransposeSingleAxisOutwards(gsl::span, const Tensor &, Tensor &, size_t, size_t, const TensorShape *, concurrency::ThreadPool *)Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool *, Tensor &, std::ptrdiff_t, const TensorShapeVector &, const TensorShape &, const Tensor &, std::ptrdiff_t, const TensorShapeVector &) [EnabledDataTypes = onnxruntime::TypeList]SNPEExecutionProviderInternalTestingExecutionProvider/Users/runner/work/1/s/onnxruntime/core/framework/utils.ccconst OrtDevice &onnxruntime::utils::FindDeviceForValue(const SessionState &, std::string_view)exec_plan_ptrInitializeFeedFetchCopyInfoCopyOneInputAcrossDevicesExecuteGraphFindDeviceForValueconst OrtDevice &onnxruntime::utils::FindDeviceForValue(const OrtValueNameIdxMap &, const SequentialExecutionPlan &, std::string_view)CalculateStaticCopyInfoForFeedsbool onnxruntime::utils::FinalizeCopyInfoForFeeds(gsl::span, std::vector &)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(gsl::span &, std::vector &)fetch_alloc_info.size() == copy_info.size()CalculateStaticCopyInfoForFeedStatus onnxruntime::utils::BatchOrCopyMLValue(const SessionState &, const MLValueCopyInfo &, const OrtValue &, OrtValue &, Stream *, std::vector *, std::vector *)Failed to find allocator for device BatchOrCopyMLValueUnsupported OrtValue type to copy between device.Unsupported OrtValue type.ExecuteGraphImplvoid onnxruntime::utils::UpdateWithParentStream(DeviceStreamCollection &, Stream *)Subgraph has nodes running on device: while parent graph node running on device: , this is not supported yet.common::Status onnxruntime::utils::CopyInputsAcrossDevices(const SessionState &, gsl::span, std::vector &, gsl::span, gsl::span)copy_info.size() == num_feedsfeed_streams.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicesA list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.Couple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Constrain input and output types to float tensors.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`WThe weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.RThe recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.sequence_lensOptional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.PThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.QWThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` AWThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.key and value cache shall be 4 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionstoken_offset shall be 2 dimensionsInputs 0 (query) shall be 3 or 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsThe past_key input shall be 4 dimensionsInputs 0 (query) shall be 3 dimensionsInputs 2 (value) shall be 3 dimensionsNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Whether to use rotary position embedding. Default value is 0.Dimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizeThe value to be filled in the attention mask. Default value is -10000.0fCustom scale will be used if specified. Default value is 1/sqrt(head_size)Input tensor with shape (batch_size, sequence_length, input_hidden_size)Merged Q/K/V weights with shape (input_hidden_size, hidden_size + hidden_size + v_hidden_size)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) for input projectionAttention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, total_sequence_length) or (batch_size, sequence_length, total_sequence_length), or index with shape (batch_size) or (2 * batch_size) or (3 * batch_size + 2)pastpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size)When past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)relative_position_biasadditional add to QxK' with shape (batch_size, num_heads, sequence_length, total_sequence_length)When past_present_share_buffer is used, it is required to specify past_sequence_length (could be 0).3D output tensor with shape (batch_size, sequence_length, v_hidden_size)presentpast state for key and value with shape (2, batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).Constrain mask index to integer types/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/bert_defs.ccInputs 0 shall be 2 dimensionsInvalid bias shapeqkv_hidden_sizes should have 3 elementsInput tensor with shape (token_count, input_hidden_size)token_offsetIn packing mode, it specifies the offset of each token(batch_size, sequence_length).cumulative_sequence_lengthA tensor with shape (batch_size + 1). It specifies the cumulative sequence length.A tensor with shape (batch_size, num_heads, sequence_length, sequence_length)or (1, num_heads, sequence_length, sequence_length).It specifies the additional bias to QxK'2D output tensor with shape (token_count, v_hidden_size)PackedAttentionInputs 0 (query) shall be 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsqueryQuery with shape (token_count, hidden_size) or packed qkv with shape (token_count, num_heads, 3, head_size)keyKey with shape (token_count, hidden_size)Value with shape (token_count, v_hidden_size)Bias tensor with shape (hidden_size + hidden_size + v_hidden_size) from input projectionOffset of each token before packing, with shape (batch_size, sequence_length).It specifies the additional bias to QxK'. The shape is (batch_size, num_heads, sequence_length, sequence_length) or (1, num_heads, sequence_length, sequence_length)output tensor with shape (token_count, v_hidden_size)Constrain input and output to float tensors.Constrain mask, offset and sequence length to integer typesPackedMultiHeadAttentionInput tensor with shape (batch_size, 1, input_hidden_size)Mask values of shape (batch_size, total_sequence_length)past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size)When past_present_share_buffer is set, its shape is (2, batch_size, num_heads, max_sequence_length, head_size). The first `batch_size * num_heads * max_sequence_length * head_size` elements correspond to keys and the next `batch_size * num_heads * max_sequence_length * head_size` elements correspond to values. The keys buffer is re-ordered in such a way that its virtual sub-tensor of shape (batch_size, num_heads, max_sequence_length, head_size) which may be perceived as being of shape (batch_size, num_heads, max_sequence_length, head_size / x, x) is reordered to become (batch_size, num_heads, head_size / x, max_sequence_length, x) where `x = 16 / sizeof(T)`.The beam width that is being used while decoding.If not provided, the beam width will be assumed to be 1.cache_indirectionA buffer of shape [batch_size, beam_width, max_output_length] where an [i, j, k] entry specifieswhich beam the 'k' th token came from for the 'j' th beam for batch 'i' in the current iterationCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)output_qkNeed output the cross attention MatMul(Q, K)Query with shape (batch_size, 1, hidden_size) or packed QKV with shape (batch_size, 1, 2 * hidden_size + v_hidden_size)Key with shape (batch_size, 1, hidden_size) for self attention or past_key with shape (batch_size, num_heads, kv_sequence_length, head_size) for cross attentionValue with shape (batch_size, 1, v_hidden_size) for self attention or past_value with shape (batch_size, num_heads, kv_sequence_length, head_size) for cross attentionMask values of shape (batch_size, total_sequence_length) or (batch_size, kv_sequence_length)past_keypast state for key with shape (batch_size, num_heads, past_sequence_length, head_size) for self attentionWhen past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size). The keys buffer is re-ordered in such a way that its virtual sub-tensor of shape (batch_size, num_heads, max_sequence_length, head_size) which may be perceived as being of shape (batch_size, num_heads, max_sequence_length, head_size / x, x) is reordered to become (batch_size, num_heads, head_size / x, max_sequence_length, x) where `x = 16 / sizeof(T)`.past_valuepast state for value with shape (batch_size, num_heads, past_sequence_length, head_size) for self attentionWhen past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size). When past_present_share_buffer is used, it is required to specify past_sequence_length (could be 0).Cross Attention doesn't need this input.present_keypresent state for key with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).present_valuepresent state for value with shape (batch_size, num_heads, total_sequence_length, head_size). If past_present_share_buffer is set, its shape is (batch_size, num_heads, max_sequence_length, head_size), while effective_seq_length = (past_sequence_length + kv_sequence_length).qknormalized Q * K, of shape (batch_size, num_heads, 1, head_size). Constrain qk output types to float32 tensors.Query with shape (batch_size, sequence_length, hidden_size), or packed QKV with shape (batch_size, kv_sequence_length, num_heads, 3, head_size)Key with shape (batch_size, kv_sequence_length, hidden_size), or packed KV with shape (batch_size, kv_sequence_length, num_heads, 2, head_size), or past_key with shape (batch_size, num_heads, kv_sequence_length, head_size)Value with shape (batch_size, kv_sequence_length, v_hidden_size), or past_value with shape (batch_size, num_heads, kv_sequence_length, head_size)key_padding_maskKey padding mask with shape (batch_size), (3 * batch_size + 2), (batch_size, kv_sequence_length), (batch_size, total_sequence_length), or (batch_size, sequence_length, total_sequence_length)relative position bias: addition to QxK' with shape (batch_size, num_heads, sequence_length, total_sequence_length) or (1, num_heads, sequence_length, total_sequence_length)past state for self attention key with shape (batch_size, num_heads, past_sequence_length, head_size)past state for self attention value with shape (batch_size, num_heads, past_sequence_length, head_size)present state for cross attention key with shape (batch_size, num_heads, kv_sequence_length, head_size)or present state for self attention key with shape (batch_size, num_heads, total_sequence_length, head_size)present state for cross attention value with shape (batch_size, num_heads, kv_sequence_length, head_size)or present state for self attention value with shape (batch_size, num_heads, total_sequence_length, head_size)Constrain mask to integer typesNumber of attention heads for qkv_num_headsNumber of attention heads for k and vlocal_window_sizeleft_window_size for local attention (like Mistral). Default value is -1 meaning unused.rotary_interleavedRotate using interleaved pattern. Default value is 0 (False).Query with shape (batch_size, sequence_length, hidden_size), or packed QKV with shape(batch_size, sequence_length, d) where d is (num_heads * head_size + 2 * kv_num_heads * head_size).Key with shape (batch_size, kv_sequence_length, kv_hidden_size) Value with shape (batch_size, kv_sequence_length, kv_hidden_size)past state key with support for format BNSH. When past_key uses same tensor as present_key(k-v cache), it is of length max_sequence_length... otherwise of length past_sequence_length.past state value with support for format BNSH. When past_value uses same tensor as present_value(k-v cache), it is of length max_sequence_length... otherwise of length past_sequence_length.seqlens_k1d Tensor of shape (batch_size). Indicates past sequence lengths for token generation case.total_sequence_lengthScalar tensor of total sequence length (past + new).cos_cache2D tensor with shape (max_sequence_length, head_size / 2).sin_cache3D output tensor with shape (batch_size, sequence_length, hidden_size)present state key with support for format BNSH. When past_key uses same tensor as present_key(k-v buffer), it is of length max_sequence_length... otherwise of length past_sequence_length +kv_sequence_length.present state value with support for format BNSH. When past_value uses same tensor as present_value(k-v buffer), it is of length max_sequence_length... otherwise of length past_sequence_length +kv_sequence_length.Constrain mask to int tensor.GroupQueryAttentionwindowOne sided attention windows length W, or half of total window length3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_sizeweight2D input tensor with shape (hidden_size, 3 * hidden_size)1D input tensor with shape (3 * hidden_size)maskAttention mask with shape (batch_size, sequence_length)global_weightglobal_biasglobalGlobal attention flags with shape (batch_size, sequence_length)GConstrain to integer typesLongformerAttention3D input tensor with shape (sequence_length, batch_size, hidden_size), hidden_size = num_heads * head_size3D input tensor with shape (total_sequence_length, batch_size, hidden_size)q_weight2D input tensor with shape (hidden_size, hidden_size)kv_weight2D input tensor with shape (hidden_size, 2 * hidden_size)2D input tensor with shape (batch_size, total_sequence_length)key_cacheinput tensor with shape (batch_size, num_heads, sequence_length or total_sequence_length, head_size)value_cachestatic_kvIf static_kv = true, cross-attention; else self-attentionuse_pastIf use_past = true, use cache; else no cachehas_layer_stateIf has_layer_state = true, layer_state = {} or [a,b]; else layer_state = Nonehas_key_padding_maskhas_key_padding_mask or not3D output tensor with shape (sequence_length, batch_size, hidden_size)new_key_cacheoutput tensor with shape (batch_size, num_heads, new sequence_length, head_size)new_value_cacheConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.DecoderAttentionCustom scale will be used if specified. Default value is 1.0Rotary embedding dimension. Default value is 0.Number of attention heads. Default value is 0. Must use with rotary_embedding_dim3D tensor with shape (batch_size, sequence_length, hidden_size) or 4D with shape (batch_size, num_heads, sequence_length, head_size)1D tensor with shape (1) or 2D tensor with shape (batch_size, sequence_length)2D tensor with shape (max_sequence_length, head_size / 2) or (max_sequence_length, rotary_embedding_dim / 2)tensor with same shape as input.Constrain input and output types to integer tensorsThe epsilon value to use to avoid division by zero.mask_index_typeThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)2D words IDs with shape (batch_size, sequence_length)segment_ids2D segment IDs with shape (batch_size, sequence_length)word_embedding2D with shape (,hidden_size)position_embedding2D with shape (, hidden_size)segment_embedding1D gamma tensor for layer normalization with shape (hidden_size)1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)2D position ids with shape (batch_size, sequence_length) or (1, sequence_length)1D mask_index tensor with shape (batch_size)embedding_sumsum of word_embedding and position_embedding without layer normalizationConstrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.max_distanceMax distanceis_bidirectionalDefault value is 0.bias_table2D input tensor with shape (num_buckets, num_heads), COL-major(See UT for example)query_lengthThe length of query. Self Attention requires query_length = key_lengthkey_lengthThe length of key.4D output tensor with shape (1, num_heads, sequence_length, sequence_length)Constrain sequence_length to int tensors.RelativePositionBias3D input tensor with shape (batch_size, sequence_length, hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size) or (1, sequence_length, hidden_size) or (sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeSaved mean used during training to speed up gradient computationinv_std_varSaved inverse standard variance used during training to speed up gradient computation.input_skip_bias_sumSum of the input and skip inputs (and bias if it exists) with shape (batch_size, sequence_length, hidden_size).Constrain mean and inv_std_var to float tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)Or 2D input tensor with shape (token_count, hidden_size)3D output tensor with shape (batch_size, sequence_length, hidden_size)Or 2D output tensor with shape (token_count, hidden_size)Sum of the input and skip inputs (and bias if it exists)with shape (batch_size, sequence_length, hidden_size) or (token_count, hidden_size).The NGram size.2D input tensor with shape (batch_size, sequence_length)scores2D input tensor with shape (batch_size, vocab_size)scores_out2D output tensor with shape (batch_size, vocab_size)Constrain indices to integer typesConstrain scores input and output types to float tensors.The minimum NGram size for suffix matching.The maximum NGram size for suffix matching.src_tokensEncoder input ids.cur_tokensDecoder input ids.prev_suffix_match_idxPrevious suffix match indexpred_tokensPredicted token ids from aggressive decodingtokensDecoder input ids after merging predicted tokenssuffix_match_idxnew suffix match indexConstrain to integer types.GemmFastGeluInput tensor with shape (batch_size, sequence_length, hidden_size)sequence_token_countNumber of non-padding tokens in each sequence with shape (batch_size).output tensor with shape (total_tokens, hidden_size)Offset of non-padding tokens, and those of padding tokens. Its shape is (batch_size, sequence_length)cumulated_seq_lenCumulated sequence lengths. Its shape is (batch_size + 1)max_seq_lenMax sequence length without padding. Its shape is (1)Constrain sequence_token_count and token_offset to integer typesRemovePaddingInput tensor with shape (total_tokens, hidden_size)Offset of non-padding tokens and paddings. Its shape is (batch_size, sequence_length)output tensor with shape (batch_size, sequence_length, hidden_size)Constrain token_offset to integer typesRestorePaddingquery_layertensor with shape (batch_size, seq_len, num_heads x head_size) or (token_count, num_heads x head_size)query_bias1-d tensor with shape (num_heads x head_size)rel_postensor with shape (1, num_head, seq_len, seq_len)gemm weight for the gated_ur_linear, shape (head_size, D), D is divisible by 2bias for the gated_ur_linear, shape (D)eco_atensor of shape (1, num_heads, 1, 1)offset of each token with shape (batch_size, seq_len)output tensor with shape (batch_size, num_heads, seq_len, seq_len)GatedRelativePositionBiasAttribute expected to have tensor or sparse tensor typebconeX_bias = Add (X, bias)X_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) = Constant()Error parsing node:Error unexpected extra input in node:'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensions in whisper graphFailed to parse max_length or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarThe input data as Tensor.The output./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/contrib_defs.ccAThe normal input data.The bias input data that is a 1D tensor.CAlpha value.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeLong tensor containing the indices to extract from embedding matrix.padding_idxA 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.scale_grad_by_freqA 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.Constrain input and output types to all numeric tensors.TorchEmbeddingBoolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.apply softmax to elements for dimensions axis or highertrue if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1dataThe bias (or mask) as Tensor.(Optional) Seed to the random generator, if not specified we will auto generate one.The bias input, a vector with the same shape as last dim of data OR same shape with dataresidualThe residual input, must have the same shape as dataratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.The output mask of dropout.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.Constrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.IsAllFiniteisinf_onlyIf true, check only for Inf, -Inf.isnan_onlyIf true, check only for NaN.Constrain the output to a boolean tensor.Input tensors to check.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.Three interpolation modes: bilinear (default), nearest and bicubic.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.4-D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the input data.GridInput offset, 4-D tensor of shape (N, H_out, W_out, 2), where H_out and W_out are the height and width of grid and output, Grid specifies the sampling pixel locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode.4-D tensor of shape (N, C, H_out, W_out).Constrain input types to all tensor types.Constrain output types to float tensors.dimspecify the dimension to unfoldspecify the sizestepspecify the step.Output tensor.Allow inputs and outputs to be any kind of tensor.UnfoldTensorInput cost tensor, it must be 2D tensor of shape M x N, or 1 x M x NFOutput tensor. shape is [2, x], where max(M, N) <= x < M + NConstrain to float tensors.DynamicTimeWarpingThe id of the end-of-sequence tokenThe id of the padding tokenThe id of the token that indicates decoding starts.no repeat ngrams sizeearly stop or notmodel type: 0 for GPT-2; 1 for encoder decoder like T5The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsDecoder subgraph to execute in a loop.Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapeThe sequence used as a prompt for the generation in the encoder subgraph. Shape is (batch_size, sequence_length)The maximum length of the sequence to be generated. Shape is (1)The minimum length below which the score of eos_token_id is set to -Inf. Shape is (1)Number of beams for beam search. 1 means no beam search. Shape is (1)The number of returned sequences in the batch. Shape is (1)Exponential penalty to the length. Default value 1.0 means no penalty.Value > 1.0 encourages longer sequences, while values < 1.0 produces shorter sequences.Shape is (1,)repetition_penaltyThe parameter for repetition penalty. Default value 1.0 means no penalty. Accepts value > 0.0. Shape is (1)vocab_maskMask of vocabulary. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (vocab_size)prefix_vocab_maskMask of vocabulary for first step. Words that masked with 0 are not allowed to be generated, and 1 is allowed. Shape is (batch_size, vocab_size)Custom attention mask. Shape is (batch_size, sequence_length)The forced input id sequence for the decoder subgraph. Shape is (batch_size, initial_sequence_length)logits_processorSpecific logits processor for different types of beamsearch models. Default value 0 means no specific logit processor. Accepts value >= 0. Shape is (1)sequencesWord IDs of generated sequences. Shape is (batch_size, num_return_sequences, max_sequence_length)sequences_scoresFinal beam score of the generated sequences. Shape is (batch_size, num_return_sequences)Processed beam scores for each vocabulary token at each generation step.Beam scores consisting of log softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam.Shape is (max_length - sequence_length, batch_size, num_beams, vocab_size)Constrain input type to float or int tensors.The id of the token that indicates decoding starts (i.e. the start of transcription token id)The id of the translate taskThe id of the transcribe taskThe id of the token that indicates LM startsThe token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.The id of the token that indicates no timestampsThe id of the first timestampMust be 2 for whisperIf nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.Exponential penalty to the length. Default value 1.0 means no penalty. Value > 1.0 encourages longer sequences, while values < 1.0 produces shorter sequences. Shape is (1,)cross_qk_layer_headOnly keep this list of (layer, head) of QK in the final cross_qk output when use_cross_qk is set. Default collect all its shape is (number of (layer, head) to keep, 2), i.e., [[layer_id1, head_id1], [layer_id2, head_id2]......]extra_decoding_idsPart of the decoder_input_ids that we need cross qk for it. it is of shape (batch_size, extra_decoding_ids_len).In such case, we should remove this from the tail of the decoder_input_ids, and put it here. ids < 0 in it (for multiple batch) are treated as stop of the extra_decoding_ids for corresponding batch.Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam. Shape is (max_length - sequence_length, batch_size, num_beams, vocab_size)cross_qkOutput the accumulated stacked Q*K in cross attentions. Let H = number of Head of cross attention, F = the frames or kv-seq-len of the cross attention input, T = real decoded token length, L = number of layers, B = batch size, R = num_return_sequences. It then should return tensor of shape [B, R, L*H, T, F]. If cross_qk_layer_head is given, shape is [B, R, cross_qk_layer_head.shape[0], T, F]non_speech_probsFor whisper model, output the probabilities from logits after encoder and context decoding for the no_speech_token_id. The shape of non_speech_probs is [B]Constrain cross_qk to float32 tensors.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartThe subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.The sequence used as a prompt for the generation. Shape is (batch_size, sequence_length)Word IDs of generated sequences. Shape is (batch_size, max_sequence_length)The value used to module the next token probabilities.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.All filtered values will be set to this float value.Minimumber of tokens we keep per batch example in the output.Presence penalty for custom samplingIf 1 custom sampling logicModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartpresence_maskPresence penalty mask. Shape is (batch_size, vocab_size)Seed for random number generator. Shape is (1)filtered_logitsFiltered logits as input to the mutinomial function for debug purpose. Shape is (batch_size, vocab_size)activation_typeActivation function to use. Choose from relu, gelu, silu and identity. Default is reluNumber of top experts to select from expert pool2D input tensor with shape (num_rows, hidden_size) or 3D input tensor with shape (batch_size, sequence_length, hidden_size)router_probs2D input tensor with shape (num_rows, num_experts)fc1_experts_weights3D input tensor with shape (num_experts, hidden_size, inter_size)fc2_experts_weights3D input tensor with shape (num_experts, inter_size, hidden_size)fc1_experts_bias2D optional input tensor with shape (num_experts, inter_size)fc2_experts_bias2D optional input tensor with shape (num_experts, hidden_size)Constrain input and output types to float or float16 tensors.MoEConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Constrain input0 and output types to float tensorsinput tensor of size n in the signal dimsignal_ndimnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)normalizedmust be 0, normalization currently not supportedmust be 1, only one sided FFTs supportedoutput tensor of size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex partsRfftinput tensor with size (n//2 + 1) in the signal dim and 2 in the last dimension for the real and complex partsnumber of dimensions comprising the signaloutput tensor with size n in the signal dimIrfftinput_0input_1ComplexMulComplexMulConjPadsConstrain input and output types to float tensorsZInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionStrings to tokenizeTokenized stringsInput/Output is a string tensorBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredN-dimensional matrix AN-dimensional matrix BMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Quantization type1-dimensional data blobB_shapeShape information of BConstrain input matrix data types as single precision float tensorConstrain input B data types as data blobConstrain shape of B must be int64 tensor.Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply resultsWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationactivation_axis2-dimensional sparse matrix A. Either COO or CSR formatN-dimensional dense matrix Bsparse_tensor(float)sparse_tensor(double)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(uint32)An input tensor to hash.32-bit hash value.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.Tensor of rank q >= 1.Tensor of rank q-1+r-indices[-1].Constrain input and output types to any tensor type.Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.SequenceSpecify batchs of sequence words to embeddingSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayInput tensor.Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor after padding.xA 1-D input tensor that is to be processed.yA 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'Input can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".2D matrix with shape (M,N)2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.Whether A should be transposed. Float 8 only supprted transA=0.Whether B should be transposed. Float 8 only supprted transB=1.Scalar multiplier for the product of input bias C.Output Type. Same definition as attribute 'to' for operator Cast.Activation function, RELU or GELU or NONE (default).Input tensor C.scaleAScale of tensor A if A is float 8 tensorTSscaleBScale of tensor B if B is float 8 tensorscaleYScale of the output tensor if A or B is float 8.TRConstrain type to input A.Constrain type to input B.Constrain type to input C.Constrain type to result type.Constrain type for all input scales (scaleA, scaleB, scaleY).GemmFloat8The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varInput data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.InvStdDevSaved inverse standard deviation used during training to speed up gradient computation.Constrain input X type to float tensors.Type of Mean and InvStdDev tensors.Constrain output Y, scale and bias type to float tensors.Constrain mean and inv_std_var to be float tensors.Constrain output Y and scale type to float tensors.EfficientNMS_TRTboxesThe boxes input tensor.The scores input tensor.anchorsThe anchors input tensor.num_detectionsThe num_detections output tensor.detection_boxesThe detection_boxes output tensor.detection_scoresThe detection_scores output tensor.detection_classesThe detection_classes output tensor.background_classBackground class ID.box_codingEncoding type for the boxes or anchors inputs.iou_thresholdBox IOU threshold value.max_output_boxesMax detections to output.plugin_versionVersion number of the TRT plugin.score_activationActivation function to apply to the scores input.score_thresholdScore threshold value.MultilevelCropAndResize_TRTfeature_map_0The first feature map input tensor.feature_map_1The second feature map input tensor.feature_map_2The third feature map input tensor.feature_map_3The fourth feature map input tensor.patchesThe cropped patches output tensor.image_sizeImage size.pooled_sizePooled size.PyramidROIAlign_TRTDisentangledAttention_TRTc2c_attentioncontent-to-content attention tensor, QcKc^T.c2p_attentioncontent-to-position attention tensor, QcKr^T.p2c_attentionposition-to-content attention tensor, KcQr^T.disentangled_attentionThe disentangled attention output tensor.spanMaximum relative distance, k.factorScaling factor applied to attention values, 1/sqrt(3d). d is hidden size per head = H/N. H is hidden size, N is number of heads.SnpeDLCpayload of the SNPE DLC file.snpe_version(Optional) SNPE version used to convert the model.target_device(Optional) Target device like CPU, DSP, etc.notes(Optional) Some notes for the modelList of tensors for SNPE DLC inputOne or more outputs, list of tensors for DLC outputConstrain input and output types to uint8, uint16, float tensors.EPContextmain_contextUsually each single EPContext associate with a graph partition.But for some case like QNN, it has single EPContext contains all partitions.In that case, the node with ep_cache_context should set main_context=1. Other nodes set main_context=0 and skip ep_cache_context.The path is relative to this Onnx file. Default is 1.ep_cache_contextpayload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.embed_mode1: indicate ep_cache_context is the context content. 0: indicate ep_cache_context is the file path to the context content.The path is relative to this Onnx file. Default is 1.ep_sdk_version(Optional) SDK version used to convert the model.hardware_architecture(Optional) Hardware architecture.partition_name(Optional) partitioned graph name.source(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainList of tensors for inputsOne or more outputs, list of tensors for outputsConstrain input and output types.The bit-packed output mask.Constrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.size of each input featuresize of each output featurenumber of bits used for weight quantization (default 4)number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.The minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset). It is used to control how input A is quantized or downcast internally while doing computation, for example: 0 means input A will not be quantized or downcast while doing computation. 4 means input A can be quantized with the same block_size to int8 internally from type T1.The input tensor, not quantizedquantization scalezero_pointsquantization zero pointstensor. The output tensor has the same rank as the input. Constrain input and output types to float/half_float tensors.Constrain quantized weight types to uint8.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.quantization data type. 0 for FP4, 1 for NF4.Indicate if the ops run in training_mode, by default, False.Whether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.1-dimensional quantized data for weightabsmaxquantization constantsConstrain input and output types to float/half_float/brain_float tensors.Incompatible dimensionsHalfOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) Alpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) Input rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h: expected to have rank > but has rank Dimension mismatch in unification between is null expected to have tensor or sparse tensor type: Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions. expected to have rank size attribute in UnfoldTensor must greater than 0.size attribute in UnfoldTensor not specified!size attribute in UnfoldTensor not positive and less than the dim size!First input does not have rank 2Second input does not have rank 2Input axis is invalid: inputs are expected to have tensor type and output type should not be null.B input for MatMul must be a 2-D matrix!4b quantization not yet supported on this hardware platform!Input q4 tensors of wrong size!both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionValue of attribute not specifiedAttribute should be of integer type and specify a type. does not specify a valid type.EpsilonXShape = Shape (X)Rank = Size (XShape)Zero1D = Constant()Axis1D = Constant()PrefixShape = Slice (XShape, Zero1D, Axis1D)NumReducedAxes = Sub (Rank, Axis1D)NumReducedAxes = Neg (Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)X2D = Flatten (X)XU = Cast (X2D)Mean2D = ReduceMean (XU)Square = Mul (XU, XU)MeanOfSquare = ReduceMean (Square)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)Deviation = Sub (XU, Mean2D)Normalized = Div (Deviation, StdDev)NormalizedV = Cast (Normalized)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedV, Scale2D)B2D = Flatten (B)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Attribute 'max_output_boxes' must be >= 1.Attribute 'pooled_size' must be >= 1.Input 0 shall be 3 dimensionsThe epsilon value to use to avoid division by zerogroupsThe number of groups of channels. It should be a divisor of the number of channels CActivation after group normalization: 0 for None, 1 for SiLU1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.Input data tensor. Dimensions are (N x H x W x C) when channels_last is 1 or (N x C x H x W) otherwise, where N is the batch size, C is the number of channels, and H and W are the height and width of the data1D gamma tensor for normalization with shape (C), where C is number of channels1D beta tensor for normalization with shape (C), where C is number of channelsThe output tensor of the same shape as XConstrain input X and output Y types to float tensors.Constrain gamma and beta to float tensors.GroupNorm/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/diffusion_defs.ccInput data tensor. Dimensions are (N x H x W x C) when channels_last is 1 or (N x C x H x W) otherwise, where N is the batch size, C is the number of channels, and H and W are the height and width of the data1D beta tensor for normalization with shape (C), where C is number of channels4D or 2D skip tensor. The shape can be (N x H x W x C) or (N x 1 x 1 x C) or (N x C)1D bias tensor. Dimensions are (C), where C is number of channelsThe element-wise sum of input x, skip and bias tensors. It has the same shape as XConstrain input X, skip, bias and output Y, S types to float tensors.SkipGroupNormInput tensor. Dimensions are (N, S, D), where N is the batch size, S are image size, and D is hidden dimensionBias tensor. Dimensions are (D), where D is the same hidden dimension as input tensorThe output tensor with dimensions (N, S, D/2)BiasSplitGeluInput tensor. Dimensions are (N, S, C), where N is the batch size, S is image size H*W, and C is number of channelsBias tensor. Dimensions are (C)Residual tensor. Dimensions are (N, S, C)The output tensor with dimensions (N, S, C)BiasAddbias shall be 1 dimensionTensor must have at least 3 dimensions to convert between channels first and channels last.Output tensor must have at least 3 dimensions/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cctensor rank too smallinvalid scales dimensioninvalid scales value/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.ccInput data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.x_scaleScale of quantized input 'X'. It must be a scalar.x_zero_pointZero point tensor for input 'X'. It must be a scalar.y_scaleScale of quantized output 'Y'. It must be a scalar.y_zero_pointZero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1.Constrain input and output types to signed/unsigned int8 tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input scale. It's a scalar, which means a per-tensor/layer quantization.Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedConstrain input and output types to 8 bit tensors.w_scalew_zero_pointTensor to be added to the output, must be the same shape and format as the output tensor.inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. Assuming zero based indices for the shape array, X.shape[1] == (W.shape[1] * group) == C and W.shape[0] mod G == 0. Or in other words FILTER_IN_CHANNEL multiplied by the number of groups should be equal to DATA_CHANNEL and the number of feature maps M should be a multiple of the number of groups G.Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into.Value of alphaValue of beta1D input tensor1D output tensor/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/onnx_deprecated_operators.ccBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensorTensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`1-D tensor of ending indices (exclusive) of corresponding axis in axes1-D tensor of axes that `starts` and `ends` apply to.Sliced data tensor.Constrain input and output types to all tensor types.The shape of filled tensorThe filled tensorvaluesinput_as_shapeextra_shapeGivenTensorFillInput data to be scaledOutput data after scalingdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevThe previous GRU hidden state.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.tThe timestep for this operation.hiddenThe new GRU hidden state calculated by this op.GRUUnitIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseNegative values are not allowed in a shape specification should specify a shapeInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[0] () + scale[1] (Input data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D tensor for per-axis quantization.Zero point for doing quantization to get 'y'. Shape must match y_scale. Default is uint8 with zero point of 0 if it's not specified.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/quantization_defs.ccN-D quantized Input tensor to be de-quantized.Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. Shape must match x_scale. It's optional. Zero point is 0 when it's not specified.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.Constrain 'y', 'x_scale' to float tensors.bfp_typeThe type of BFP - must match with the BFPType enumblock_dimEach bounding box spans this dimension.Typically, the block dimension corresponds to the reduction dimension of the matrix multipication that consumes the output of this operator.For example, for a 2D matrix multiplication A@W, QuantizeBFP(A) would use block_dim 1 and QuantizeBFP(W) would use block_dim 0.The default is the last dimension.N-D full precision input tensor to be quantized.1-D, contiguous BFP dataShape of xStrides of xConstrain the input to float and bfloat.Constrain y to uint8.Constrain shape and strides to uint64.QuantizeBFPThe datatype to dequantize to.1-D, contiguous, raw, BFP data to be de-quantized.shape of the original tensor.strides of the original tensor.de-quantized tensor.Constrain the input to uint8.Constrain y to float and bfloat16.DequantizeBFPAn input tensor.reducedReduced output tensor.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.Keep the reduced dimension or not, default 1 mean keep reduced dimension.ReduceSumIntegerFirst operand.A_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Second operand.B_zero_pointInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.MulIntegerb_scaleScale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.b_zero_pointZero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.a_scaleScale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_zero_pointZero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointdata_scaledata_zero_pointreduced_scalereduced_zero_pointQLinearReduceMean QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`, `f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise. Coefficient of leakage.X_scaleInput X's scale. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces one output data (Tensor) where the function `f(x) = quantize(Sigmoid(dequantize(x)))`, is applied to the data tensor elementwise. Wwhere the function `Sigmoid(x) = 1 / (1 + exp(-x))` apply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.The input tensorZero point tensor for input 'X'.It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. Number of neurons in the hidden layerCouple the input and forget gates if 1.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R_scaleR's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R_zero_pointR's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.Which axis to concat onY's scale.Y's zero point.T8List of tensors/scale/zero_point for concatenationTVConcatenated tensorConstrain input and output types to 8 bit signed and unsigned tensors.Constrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).condition When True (nonzero), yield x, otherwise yield yX's scale.X's zero point.z_scaleZ's scale.z_zero_pointZ's zero point.Tensor of shape equal to the broadcasted shape of condition, X, and YScale of quantized input 'A'. It is a scalar,which means a per-tensor quantization.Zero point tensor for input 'A'. It is a scalar.Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Its type is int32_t and must be quantized with zero_point = 0 and scale = alpha / beta * a_scale * b_scale.Scale of output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Zero point tensor for output 'Y'. It is a scalar, which means a per-tensor quantization. It is optional. The output is full precision(float32) if it is not provided. Or the output is quantized.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)3D input tensor with shape (batch_size, sequence_length, input_hidden_size)2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_sizeinput_scalescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.weight_scalescale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationAttention mask index with shape (batch_size)input_zero_pointzero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.weight_zero_pointzero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to int8 tensors.word_embedding_quantposition_embedding_quantgamma_quantbeta_quantMaskword_embedding_scaleScale for word embeddingsposition_embedding_scaleScale for position embeddingssegment_embedding_scaleScale for segment embeddingsgamma_scaleScale for 1D gamma tensorbeta_scaleScale for 1D beta tensorword_embedding_zero_pointZero point for word embeddingsposition_embedding_zero_pointZero point for position embeddingssegment_embedding_zero_pointZero Point for segment embeddingsgamma_zero_pointZero Point for 1D gamma tensorbeta_zero_pointZero Point for 1D beta tensorlayernorm_outLayerNorm Outputmask_index_outMask Index OutputConstrain input and output types to float32 tensors.order_inputcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_outputcublasLt order of output matrix.TODO: input tensor of (ROWS, COLS). if less than 2d, will broadcast to (1, X). If 3d, it is treated as (B, ROWS, COS)scale_inputscale of the inputQConstrain to float typesConstrain Scale to float32 typesQuantizeWithOrdercublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of output matrixThe output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)DequantizeWithOrderorder_AcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.order_BcublasLt order of matrix Border_YcublasLt order of matrix Y and optional matrix C3-dimensional matrix Ascale_Ascale of the input A.2-dimensional matrix B. Transposed if order_B is ORDER_COL.scale_Bscale of the input B. Scalar or 1-D float32.scale_Yscale of the output Y.1d bias, not scaled with scale_Y.3d or 2d matrix C. if 2d expand to 3d first. Shape[0] should be 1 or same as A.shape[0] scale_CConstrain bias and scales to float32QOrderedMatMulHidden layer sizes of Q, K, V paths in Attentionorder_weightcublasLt order of weight matrixcublasLt order of global biasscale of the input, scalar value (per tensor) currently.scale_Q_gemmscale of the gemm - scalar (per-tensor quantization)scale_K_gemmscale_V_gemmQ_weight2D input tensor with shape (input_hidden_size, hidden_size), where hidden_size = num_heads * head_sizeK_weightV_weightscale_Q_weightscale of the weight (scalar for per-tensor quantization or 1-D of dims [hidden_size] for per-channel quantization)scale_K_weightscale_V_weightQ_biasK_biasV_biasscale_QKT_gemmscale_QKT_softmaxscale of the softmax result - scalar (per-tensor quantization)scale_values_gemmscale of the gemm - scalar (per-tensor quantization). Also this is the output scale for the operator.Attention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).additional add to QxK' with shape (batch_size, num_heads, sequence_length, sequence_length).Constrain scales to float32 tensors.QOrderedAttentionQOrderedLayerNormalizationorder_XcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.scale_Xscale of the quantized XScale tensor, i.e., gamma vector.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.quantization scale must be float tensors.quantization tensor must be int8 tensors.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.N-dimensional input Ascale of the input Ascale of the output YOutput of the GeluConstrain scales to float32QOrderedGeluorder_global_weightscale_weightscale of the weight1D input tensor with shape (3 * hidden_size), fp32 only currently.scale_biasreserved. (not used as add bias need float value in cublasLt for normal order.)scale_qkv_gemmscale of the output for fused kqv gemmscale_global_weightscale of the global_weightscale_global_gemmscale of the global_qkv_gemmscale_outputscale of the outputBe compatible with float version.QOrderedLongformerAttentionInput data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axisQLinearConvTranspose Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}A_scaleInput A's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B's scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleC_zero_pointResult, has same element type as two inputsShape of quantized tensor must be 1D.axis must be in [-rank, rank-1]. input rank was 'axis' must be in [ , ]. Its actual value is: Required attribute axis is missingaxis must be in [-rank, rank)All inputs to Concat must have same rankCan't merge shape info. Both inferred and declared dimension have values but they differ. Inferred= Dimension= expected to have: or UNDEFINED. Got: Tensor(scalar, or dims=[1]). First entry in the range.limitTensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.deltaTensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.1-D Tensor of the range.Unsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!input_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionsThe past input shall be 5 dimensions/Users/runner/work/1/s/onnxruntime/core/graph/function.cconnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph &, const IndexedSubGraph &)initializer != nullptrInitializer is not found or is not constant initializer./Users/runner/work/1/s/onnxruntime/core/graph/function_utils.ccTAggregatedTypesall_tensor_types_ir4std::unique_ptr onnxruntime::function_utils::CreateSchema(const Graph &, const IndexedSubGraph &, bool)input_arg->Type() != nullptrstd::unique_ptr onnxruntime::function_utils::CreateSchema(const std::string &, const std::string &, const std::unordered_map &, const std::unordered_map &, const SchemaRegistryManager &, const logging::Logger &, bool)The given function name: , domain: is not found in model local functionsonnx_func_protoTinauto onnxruntime::function_utils::IOTypeConstraintHelper(const onnx::FunctionProto &, std::unique_ptr &, const InlinedHashMap &, const InlinedHashMap &)::(anonymous class)::operator()(const onnx::NodeProto &) constit != opset_imports.end()No opset registered for domain in function opset imports.domain_version != -1static_cast(i) < node_op_schema->inputs().size()Too many inputs for op inoutToutALLOW_RELEASED_ONNX_OPSET_ONLY/Users/runner/work/1/s/onnxruntime/core/graph/model_load_utils.hbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() are '0' and '1'. The environment variable contained the value: void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map &, const logging::Logger &, bool, const std::string &, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField &, const google::protobuf::RepeatedPtrField &) [isOutput = false]actuals.size() <= formals.size()Number of actual parameters cannot exceed number of formal parametersvoid onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField &, const google::protobuf::RepeatedPtrField &) [isOutput = true]Tensor element type mismatch. Type mismatch. Current= Input=/Users/runner/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeOptional Type mismatch. Expected: . Got: void onnxruntime::Node::ToProto(NodeProto &, bool) constRemovable attributes were removed before the conversion is started.Serialization of fused function body is not currently supported, ] op_type [Removable attributes were removed before the node is saved.does not have the graph for key LoadFromOrtFormatSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingLoadEdgesFromOrtFormatinput index: is not the same as this node's index:input edgesoutput edgesThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graph(const Model &, GraphProto *, const std::unordered_map &, Version, IOnnxRuntimeOpSchemaCollectionPtr, Graph *, const Node *, const logging::Logger &, bool)graph_proto != nullptrgraph_proto cannot be nullConstantDuplicate constant node sparse initializer name: '' Model is invalid.utils::HasName(sparse_tensor)Sparse initializer must have a name. This model is invalidDuplicate sparse_tensor_initializer: 'Duplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.This is an invalid model. Tensor does not have type information. appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph state to be loaded into must be empty.node_argGraph ctor should have created NodeArg for initializer. Missing:This is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Subgraph output () is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (void onnxruntime::Graph::AddEdge(NodeIndex, NodeIndex, int, int)Invalid node indexes specified when adding edge.Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.void onnxruntime::Graph::RemoveEdge(NodeIndex, NodeIndex, int, int)Invalid node indexes specified when removing edge.Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.BuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.void onnxruntime::Graph::KahnsTopologicalSort(const std::function &, const std::function &) constSome nodes are not included in the topological sort, graph have a cycle.This is an invalid model. Error: the graph is not acyclic.Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Node:Subgraph input missing type.InferAndVerifySubgraphTypesStatus onnxruntime::Graph::UpdateShapeInference(Node &)node.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsThis is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types ( in node (InferAndVerifyTypeMatchindex=Node () Op () output arg () type inference failed, inferred types: Type Error: Type () of output arg () of node () does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. VerifyNodeAndOpMatchFatal error: ) is not a registered function/op) attribute () is required but not specified.Error: Duplicate definition-site for (InitInputsInitializersOutputsPerformTypeAndShapeInferencingForThisAndAllSubgraphsResolveStatus onnxruntime::Graph::Resolve(const ResolveOptions &)outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.void onnxruntime::Graph::AddInitializedTensor(const TensorProto &)existing->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)sparse_tensor_names_.count(tensor_name) == 0sparse_tensor_names_ not in sync with name_to_initial_tensor_!foundgraph_proto_ is not in sync with name_to_initial_tensor_.ReplaceInitializedTensorImplFailed to find existing initializer with name Trying to replace non-external initializer with external dataReplacement tensor's dimensions do not match.Replacement tensor's data type does not match.Status onnxruntime::Graph::ReplaceInitializedTensorImpl(onnx::TensorProto, bool)existing_entry != mutable_initializers.pointer_end()graph_proto_ is not in sync with name_to_initial_tensor_InjectExternalInitializedTensorsReplaced external initializer: void onnxruntime::Graph::AddValueInfo(const NodeArg *)node_arg && node_arg == new_value_infoError: trying to add an value info that are no in graph.NoOpbool onnxruntime::Graph::RemoveNode(NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.onnx::GraphProto onnxruntime::Graph::ToGraphProto() constFailed to convert dense initializer to sparseonnx::GraphProto onnxruntime::Graph::ToGraphProtoWithExternalInitializers(const std::string &, const PathString &, size_t) constexternal_stream.is_open()ToGraphProtoWithExternalInitializersvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto &) constOuter scope node arg name ''was added but does not exist. void onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs(const std::unordered_set *)initializer_node_arg != nullptrCannot find NodeArgs for [CleanUnusedInitializersAndNodeArgsRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node.outer_scope_node_arg != nullptrRemoving NodeArg ' must be either specified in graph inputs or graph initializers.gsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())Node &onnxruntime::Graph::CreateFusedSubGraphNode(const IndexedSubGraph &, const std::string &)nullptr != func_meta_defSetOpSchemaFromRegistryForNode(fused_node)Schema was not found for fused node. Domain: OpType: SinceVersion:void onnxruntime::Graph::FinalizeFuseSubGraph(const IndexedSubGraph &, Node &)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()AddConstantProtoAsInitializerStatus onnxruntime::Graph::AddConstantProtoAsInitializer(const onnx::NodeProto &, std::optional)insert_result.secondConstant node name: conflicts with graph initializer. Check that the node names have been made unique.Unable to constant fold If node: '' Unable to fetch: InlineIfSubgraph_if_Status onnxruntime::Graph::InlineIfSubgraph(bool, Node &, const logging::Logger &)Initializer name: from graph: conflicts with graph initializer. Check name generation above.Node's: input: is not If node's input or previous node output in this subgraphConstant folded (inlined) for If node: InlineFunctionProto_inlfunc_Status onnxruntime::Graph::InlineFunction(Node &)callnode.TryGetFunctionProto(inlined_fp)Node has no function body and cannot be inlined.InlineFunction in inlined subgraph: conflicts with graph initializer. Check Specializing code.void onnxruntime::Graph::SetInputs(gsl::span)input->Exists()Input to set must exist.(") : ("") -> () Inputs: Nodes: Outputs: Duplicate initializer (dense or ConstantNode): 'Node index is out of rangeSource and target must both be tensors , or optional typed entities , or sparse tensorsMergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.Output:fbs_node_arg_names cannot be nullLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg No Graph instance was found for attribute in node Graph attribute inferencing failed: /Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.hconst onnx::TensorShapeProto &onnxruntime::utils::GetShape(const onnx::TypeProto &)shape != nullptrTypeProto must have shape for this to runThis is an invalid model. In Node, , Error /Users/runner/work/1/s/include/onnxruntime/core/common/const_pointer_container.hconst T *onnxruntime::ConstPointerContainer>::at(size_t) const [Container = std::vector]index < data_.size()absl::container_internal::raw_hash_map<>::at/Users/runner/work/1/s/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveInitializerOrtFormatSaveSparseInitializerOrtFormatSaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Missing dims for sparse initializer: LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.UNDEFINEDFLOATINTSTRINGTENSORGRAPHFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORS/Users/runner/work/1/s/onnxruntime/core/graph/graph_proto_serializer.ccvoid onnxruntime::GraphViewerToProto(const GraphViewer &, onnx::GraphProto &, bool, bool)/Users/runner/work/1/s/onnxruntime/core/graph/graph_utils.ccNodeArg &onnxruntime::graph_utils::AddInitializer(Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:const std::string &onnxruntime::graph_utils::GetNodeInputName(const Node &, int)index >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.const std::string &onnxruntime::graph_utils::GetNodeOutputName(const Node &, int)index >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.bool onnxruntime::graph_utils::RemoveNode(Graph &, Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.void onnxruntime::graph_utils::ReplaceNodeInput(Node &, int, NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(Node &, int, NodeArg &)num_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.FindPathFailed since multiple edges matched:int onnxruntime::graph_utils::GetIndexFromName(const Node &, const std::string &, bool)itr != node_args.end()Attempting to get index by a name which does not exist:for node: CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(Graph &, Node &)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputvoid onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(Node &, const std::string &, const std::string &)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0/Users/runner/work/1/s/onnxruntime/core/graph/graph_viewer.cconnxruntime::GraphViewer::GraphViewer(const Graph &, const IndexedSubGraph *)graph_->GetNode(idx) != nullptrIndexedSubGraph contains values not present in the GraphnodeargMismatch between Graph and IndexedSubGraph. Input not found:Mismatch between Graph and IndexedSubGraph. Output not found:nodeMismatch between Graph and IndexedSubGraph. Node not found: const std::vector &onnxruntime::GraphViewer::GetNodesInTopologicalOrder(ExecutionOrder) constInvalid ExecutionOrderconst std::vector &onnxruntime::GraphViewer::GetRootNodes() constfilter_info_ == nullptrNot supported with filtered graph./Users/runner/work/1/s/onnxruntime/core/graph/model.cconnxruntime::Model::Model(ModelProto &&, const PathString &, const IOnnxRuntimeOpSchemaRegistryList *, const logging::Logger &, const ModelOptions &)ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Missing model IR version.Unsupported model IR version: , max supported IR version: ModelONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Invalid istream object.Null model_proto ptr.No graph was found in the protobuf.LoadProtobuf parsing failed.LoadFromBytes less than 0. is less than 0.SaveProtobuf serialization failed. is less than 0.SaveWithExternalInitializersGraph is null. Invalid ORT format model.Failed to load model with error: Load model failed. File doesn't existsystem error number SaveModelSaveModelWithExternalInitializers/Users/runner/work/1/s/onnxruntime/core/graph/node_attr_utils.ccstd::pair onnxruntime::utils::SetNodeAttribute(AttributeProto, NodeAttributes &)utils::HasName(attribute)AttributeProto must have a name./Users/runner/work/1/s/onnxruntime/core/graph/op_identifier_utils.ccLoadOpIdentifierOrtFormat/Users/runner/work/1/s/onnxruntime/core/graph/op_identifier.hLoadFromStringInvalid OpIdentifier string: Failed to parse since_version from /Users/runner/work/1/s/onnxruntime/core/common/string_utils.hInlinedVector onnxruntime::utils::SplitString(std::string_view, std::string_view, bool)!delimiter.empty()delimiter must not be empty/Users/runner/work/1/s/onnxruntime/core/graph/runtime_optimization_record_container.ccAttempting to load runtime optimization records for a previously loaded optimizer: SaveRuntimeOptimizationRecordToOrtFormatLoadRuntimeOptimizationRecordFromOrtFormatDomain already set in registry/Users/runner/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetRegisterOpSchemaInternal, but it its domain is notknown by the checker., but it its version is higherthan the operator set version /Users/runner/work/1/s/onnxruntime/core/util/math_cpu.ccvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = float, Provider = onnxruntime::CPUMathUtil]Gemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = double, Provider = onnxruntime::CPUMathUtil]void onnxruntime::math::Gemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, ptrdiff_t, ptrdiff_t, ptrdiff_t, Eigen::half, const Eigen::half *, const Eigen::half *, Eigen::half, Eigen::half *, ThreadPool *)CblasNoTrans Unexpected CBLAS_TRANSPOSE for TransB of CblasTrans Unexpected CBLAS_TRANSPOSE for TransB of Unexpected CBLAS_TRANSPOSE for TransA of bool onnxruntime::math::NextPosition(int64_t, const int64_t *, int64_t *)dims[d_i] < d_maxOrtThreadPoolParams { thread_pool_size: auto_set_affinity: allow_spinning: dynamic_block_base_: stack_size: affinity_str: set_denormal_as_zero: Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringSize of affinity string must be between 1 and /Users/runner/work/1/s/onnxruntime/core/util/thread_utils.ccstd::unique_ptr onnxruntime::concurrency::CreateThreadPoolHelper(Env *, OrtThreadPoolParams)actual_num_affinities == static_cast(options.thread_pool_size) - 1Number of affinities does not equal to thread_pool_size minus one, affinities: , thread_pool_size: custom join thread function not set;std::vector onnxruntime::concurrency::ReadThreadAffinityConfig(const std::string &)std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)Processor id must consist of only digits: processor_from > 0 && processor_to > 0Processor id must start from 1: processor_from <= processor_toInvalid processor interval: std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_id > 0ReadThreadAffinityConfigFound out-of-range processor id in affinity string: , skip affinity settingFound invalid processor id in affinity string: Failed to read affinities from affinity stringQuant GEMM format: AIsSigned(), BIsSigned() is not supported on this devicebad dimensionsbad mlas activation kindElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range![...truncated...] ERROR LOG MSG NOTIFICATION: Failure to successfully parse the messageonnxruntimeSystem/Users/runner/work/1/s/onnxruntime/core/common/logging/logging.cconnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, Severity, bool, const InstanceType, const std::string *, int)ISink must be provided.default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string &)Default logger already set. /Users/runner/work/1/s/onnxruntime/core/common/path.ccParsestatic Path onnxruntime::Path::Parse(const PathString &)Path &onnxruntime::Path::Concat(const PathString &)first_separator == value.end()Cannot concatenate with a string containing a path separator. String: RelativePathPaths must have the same root to compute a relative path. src root: , dst root: ParsePathRootFailed to parse path root: /Users/runner/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::Start()enabled_void onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrvoid onnxruntime::profiling::Profiler::StartProfiling(const logging::Logger *)custom_logger != nullptrEndTimeAndRecordEventMaximum number of events reached, could not record profile event.Writing profiler data to file {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : " : "} }, ] SessionNodeKernelApi/Users/runner/work/1/s/onnxruntime/core/common/status.cconnxruntime::common::Status::Status(StatusCategory, int, const std::string &)code != static_cast(common::OK)onnxruntime::common::Status::Status(StatusCategory, int, const char *)OKSystemError[ONNXRuntimeError]SUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORunnamed_thread_pool/Users/runner/work/1/s/onnxruntime/core/common/threadpool.ccstd::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()Profiler not started yet{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(ThreadPoolEvent)!points_.empty()LogStart must pair with LogEndvoid onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(ThreadPoolEvent)std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()points_.empty()"thread_id": "", "block_size": [], "core": DistributionDistributionEnqueueWaitWaitRevokeUnknownEvent": {"num_run": "core": onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(ThreadPool *)!current_parallel_section.has_value()Nested parallelism not supported!ps_void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const TensorOpCost &, const std::function &)n >= 0/Users/runner/work/1/s/include/onnxruntime/core/platform/EigenNonBlockingThreadPool.hvirtual void onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection(ThreadPoolParallelSection &, std::function, unsigned int, std::ptrdiff_t) [Environment = onnxruntime::Env]n <= num_threads_ + 1More work items than threadsvirtual void onnxruntime::concurrency::ThreadPoolTempl::RunInParallel(std::function, unsigned int, std::ptrdiff_t) [Environment = onnxruntime::Env] [%sVIWEF is not a valid year_month_day/Users/runner/work/1/s/onnxruntime/core/platform/posix/env.cconnxruntime::(anonymous namespace)::PosixThread::PosixThread(const char *, int, unsigned int (*)(int, Eigen::ThreadPoolInterface *), Eigen::ThreadPoolInterface *, const ThreadOptions &)index >= 0Negative thread index is not allowedcustom_create_thread_fn returned invalid handle.pthread_attr_init failed, error code: error msg: pthread_attr_setstacksize failed, error code: pthread_create failed, error code: Failed to close file descriptor - error code: Invalid fd was supplied: fstatReceived negative size from stat call file "" failed: ReadFileIntoBufferfile_path == nullptroffset < 0length > buffer.size()openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: DeleteFolderDeleteFolder(): nftw() failed with error: nftw_removeremove() failed. Error code: , path: closerealpathFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so/Users/runner/work/1/s/onnxruntime/core/flatbuffers/flatbuffers_utils.ccSaveValueInfoOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.LoadValueInfoOrtFormatNull type info for . Invalid ORT format model.LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.SaveTypeInfoOrtFormatWe do not support type [] for nowSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatLoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Type: is not supported currentlyLoadTensorTypeAndShapeOrtFormatLoadTensorShapeOrtFormatLoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.Field 'name' of 'value_info' is required to be non-empty.' is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: ): data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.Location of external TensorProto ( tensor name: ) should not be empty.) should be a relative path, but it is an absolute path: ) should be file inside the , but the '' points outside the directory) should be stored in , but it doesn't exist or is not accessible., but it is not regular file.TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!) should contain one and only one value field.STRING data (tensor name: ) should not be stored in raw_data fieldvalues of data_type '' should be stored in field '' instead of 'Unrecognized data_type (tensor name: sequenceSequence ( Structure name: , elem_type: ) is not have a valid element type.optionalOptional ( Structure name: mapsetting key_type field (map name: ) to invalid TensorProto key_type is not allowedMap (name: ) should not contain more than one keys field.Length of map keys and map values are not the same (map name: Sparse tensor indices () has values, but NNZ is Sparse tensor () index value at position [] out of range [0, ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute Attribute (name: ) should not contain more than one value field.) should refer to attribute in parent node.Warning: Model contains experimental ops:op_typeNodeProto (name: , type: ) has zero input and zero output.No opset import for domain '' appeared multiple times.ai.onnx.trainingNo Op registered for with domain_version of Op registered for is deprecated in domain_version of graphGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.initTensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph inputSparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializersNodes in a graph must be topologically sorted, however input '' of node: name: OpType: is not output of any previous nodes.' has been used as output names multiple times.No Opset registered for domain Opset import for domain in function op is not compatible with the version imported by model. FunctionOp imports version whereas model imports version functiondomain' has been used multiple times.function () should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input 'Name: is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however 'The model does not have an ir_version set properly.Your model ir_version is higher than the checker's (Your model has duplicate keys in metadata_props.model with IR version >= 3 must specify opset_import for ONNXmodel with IR version < 3 cannot have opset_import specifiedATenConstantFillBad node spec for node. Name: ==> Context: Unable to open proto file: . Please check if it is a valid proto. Unable to parse proto from file: . Please check if it is a valid protobuf file of proto. [CheckerError][OptimizerError]condCondition for the if. The tensor must contain a single element.Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.Only bool/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/controlflow/defs.ccA maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.A boolean termination condition. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.initial_state_and_scan_inputsInitial values of the loop's N state variables followed by M scan_inputsfinal_state_and_scan_outputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor types up to IRv9.seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))seq(tensor(float8e5m2fnuz))optional(seq(tensor(uint8)))optional(seq(tensor(uint16)))optional(seq(tensor(uint32)))optional(seq(tensor(uint64)))optional(seq(tensor(int8)))optional(seq(tensor(int16)))optional(seq(tensor(int32)))optional(seq(tensor(int64)))optional(seq(tensor(bfloat16)))optional(seq(tensor(float16)))optional(seq(tensor(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(tensor(uint8))optional(tensor(uint16))optional(tensor(uint32))optional(tensor(uint64))optional(tensor(int8))optional(tensor(int16))optional(tensor(int32))optional(tensor(int64))optional(tensor(bfloat16))optional(tensor(float16))optional(tensor(float))optional(tensor(double))optional(tensor(string))optional(tensor(bool))optional(tensor(complex64))optional(tensor(complex128))optional(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/controlflow/old.ccAll Tensor types up to IRv4.Scan input was not a tensor.Graph attribute inferencing returned type information for outputs. Expected Scan 'body' subgraph outputs should all be tensors but output was not axis value is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).Int64 tensorAll Tensor typesLoop 'body' subgraph outputs should all be tensors but output was Final N loop carried dependency values then K scan_outputsAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. If node has but subgraphs produce Mismatched type for output then= else=Mismatched tensor element type for output Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output Invalid data type tensor(seq(optional(map(sparse_tensor(Unsuported type proto value case.Invalid tensor data type opaqueDataTypeUtils::FromDataTypeString - Received invalid data type string 'complex64complex128Func_Input for function node is out of boundsOutput for function node No opset import registered for domain '' in function protoThe value for the elements of the output tensor.sparse_valueThe value for the elements of the output tensor in sparse format.value_intThe value for the sole element for the scalar, int64, output tensor.value_intsThe values for the elements for the 1D, int64, output tensor.value_floatThe value for the sole element for the scalar, float32, output tensor.value_floatsThe values for the elements for the 1D, float32, output tensor.value_stringThe value for the sole element for the scalar, UTF-8 string, output tensor.value_stringsThe values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain input types to float tensors.Constrain output types to integral tensors.Scalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors. { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.All values in input have to be in the range:[0, 1].The returned output tensor only has values 0 or 1, same shape as input tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliAttribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorInput tensor must be 2-dimensionalOutput type must be int32 or int64Input tensor must have rank 2All inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)output = Cast (X_greater)/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_float' expect a float.Attribute 'value_string' expect a string.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.pixel_formatPixel format. Can be one of "RGB", "BGR", or "Grayscale".RGBencoded_streamEncoded streamimageDecoded imageConstrain input types to 8-bit unsigned integer tensor.Constrain output types to 8-bit unsigned integer tensor.ImageDecoder/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/image/defs.ccInput tensor must be 1-dimensionalConstrain input to boolean tensor.Constrain output to boolean tensor./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/logical/defs.ccConstrain input types to all numeric tensors.Constrain input types to all (non-complex) tensors.Constrain input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } Constrain input/output to integer tensors.Constrain input to integer tensors.First input operand for the logical operator.Second input operand for the logical operator.Result tensor.First input operand for the bitwise operator.Second input operand for the bitwise operator./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/logical/old.ccequalConstrain input to float tensors.Constrain input to integral tensors.less_equalgreater_equalbroadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/math/defs.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors. { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Gamma = Constant () GammaCast = CastLike (Gamma, X) Zero = Constant () ZeroCast = CastLike (Zero, X) ExpX = Exp (X) AlphaMulExpX = Mul(AlphaCast, ExpX) AlphaMulExpXSubAlpha = Sub (AlphaMulExpX, AlphaCast) Neg = Mul (GammaCast, AlphaMulExpXSubAlpha) Pos = Mul (GammaCast, X) XLessThanZero = Less (X, ZeroCast) Y = Where(XLessThanZero, Neg, Pos) } Coefficient of ELU. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) XLessThanZero = Less (X, ZeroCast) ExpX = Exp (X) ExpXSubOne = Sub (ExpX, OneCast) AlphaMulExpXSubOne = Mul (AlphaCast, ExpXSubOne) Y = Where(XLessThanZero, AlphaMulExpXSubOne, X) } Constrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Mish X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.noneapproximate Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) TwoOverPi = Constant () TwoOverPiCast = CastLike (TwoOverPi, X) C0 = Constant () C0Cast = CastLike (C0, X) SqrtTwoOverPi = Sqrt (TwoOverPiCast) Three = Constant () ThreeCast = CastLike (Three, X) XCubed = Pow (X, ThreeCast) XCubedC0 = Mul (C0Cast, XCubed) XC0XCubed = Sum (X, XCubedC0) TanhInput = Mul (SqrtTwoOverPi, XC0XCubed) ErfApprox = Tanh (TanhInput) PhiApprox = Sum (OneCast, ErfApprox) MultX = Mul (HalfCast, X) Y = Mul (MultX, PhiApprox) Half = Constant () HalfCast = CastLike (Half, X) One = Constant () OneCast = CastLike (One, X) Two = Constant () TwoCast = CastLike (Two, X) SqrtTwo = Sqrt (TwoCast) XSqrt = Div (X, SqrtTwo) ErfXSqrt = Erf(XSqrt) Phi = Sum (OneCast, ErfXSqrt) MultX = Mul (HalfCast, X) Y = Mul (MultX, Phi) Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.slopeSlope tensor. The shape of slope can be smaller than first input X; if so, its shape must be unidirectional broadcastable to XOutput tensor (same size as X) { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } Value of alpha.Value of beta. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Beta = Constant () BetaCast = CastLike (Beta, X) Zero = Constant () ZeroCast = CastLike (Zero, X) One = Constant () OneCast = CastLike (One, X) AlphaMulX = Mul (X, AlphaCast) AlphaMulXAddBeta = Add (AlphaMulX, BetaCast) MinOneOrAlphaMulXAddBeta = Min (AlphaMulXAddBeta, OneCast) Y = Max(MinOneOrAlphaMulXAddBeta, ZeroCast) } { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } Constrain input and output types to numeric tensors.sumoutput = Identity (input)input_less_than_min = Less (input, min)output = Where (input_less_than_min, min, input)input_large_than_max = Less (max, input)output = Where (input_large_than_max, max, input)tmp = Where (input_less_than_min, min, input)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp)Input tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsThe softsign (x/(1+|x|)) values of the input tensor computed element-wise { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { exp_x = Exp (X) one = Constant () one_cast = CastLike (one, X) exp_x_add_one = Add (exp_x, one_cast) Y = Log (exp_x_add_one) } Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Tensor of shape [a_1, a_2, ..., a_n, r]A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleConstrain input and output types to all tensors.The hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.N-dimensional quantized matrix ascale of quantized input azero point of quantized input aN-dimensional quantized matrix bscale of quantized input bzero point of quantized input bscale of quantized output yzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, ..., zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1]. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, ..., zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N]. Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.const_zeroconst_oneexpanded_target = Unsqueeze (target, axes)ignore_index input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) loss = Squeeze (loss_N1dd, axes)loss_Ndd = Squeeze (loss_N1dd, axes)loss = ReduceMean (loss_Ndd)loss = ReduceSum (loss_Ndd)weight_gather = Gather (weight, target)loss_unweighted = Squeeze (loss_N1dd, axes)loss = Mul (loss_unweighted, weight_gather)loss_Ndd = Mul (loss_unweighted, weight_gather) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_ignore_index const_zero_target_typed = Sub (expanded_target, expanded_target) expanded_target_int64 = Cast (expanded_target) mask = Equal (expanded_target_int64, const_ignore_index) transform_targets = Where (mask, const_zero_target_typed, expanded_target) input_gather_element = GatherElements (input, transform_targets)const_zero_floatconst_zero_casted = Cast (const_zero_float)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)loss_NCdd = Neg (input_gather_element_transform)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)squeeze_mask = Squeeze (mask, axes)const_one_floatconst_one_casted = Cast (const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather_temp = Gather (weight, transform_targets)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)weight_gather = Squeeze (weight_gather_temp_1, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk).targetTarget tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.lossThe negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Rank of input does not match the equation indices.Einsum expression string.InputsOperandsConstrain input and output types to all numerical tensor types.Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Shape3D X_NCD = Reshape (scores, Shape3D) X_NDC = Transpose (X_NCD) X_LogSM = LogSoftmax (X_NDC) X_LogSM_NCD = Transpose (X_LogSM) X_shape = Shape (scores) X_Log = Reshape (X_LogSM_NCD, X_shape) log_prob = Identity (X_Log)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels)The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.labelsThe ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.log_probLog probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLossIf `onesided` is `1` and input is real, only values for `k` in `[0, 1, 2, ..., floor(n_fft/2) + 1]` are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., `X[m, k] = X[m, n_fft-k]*`, where `m` denotes "all other dimensions" DFT was not applied on. If the input tensor is complex, onesided output is not possible. Value can be `0` or `1`. Default is `0`.Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.For real input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][1]`. For complex input, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. The final dimension represents the real and imaginary parts of the value in that order.dft_lengthThe length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to `dft_length`. If less than the axis dimension, only the first `dft_length` values will be used as the signal. The axis as a scalar on which to perform the DFT. Default is `-2` (last signal axis). Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.The Fourier Transform of the input vector. If `onesided` is `0`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=0` and `onesided` is `1`, the following shape is expected: `[floor(signal_dim0/2)+1][signal_dim1][signal_dim2]...[signal_dimN][2]`. If `axis=1` and `onesided` is `1`, the following shape is expected: `[signal_dim0][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]`. If `axis=N` and `onesided` is `1`, the following shape is expected: `[signal_dim0][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]`. The `signal_dim` at the specified `axis` is equal to the `dft_length`.Constrain scalar length types to integers.HannConstrain the input size to int64_t.Constrain output types to numeric tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Hamming { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Blackman { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T3. The default value is 1 = FLOAT. num_mel_binsThe number of bands in the mel spectrum.The size of the original DFT. The size of the original DFT is used to infer the size of the onesided DFT, which is understood to be floor(dft_length/2) + 1, i.e. the spectrogram only contains the nonredundant DFT bins.sample_rateSamples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to each spectrogram bin, which dictates how they are mapped into the mel scale.lower_edge_hertzLower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.upper_edge_hertzThe desired top edge of the highest frequency band.The Mel Weight Matrix. The output has the shape: [floor(dft_length/2) + 1][num_mel_bins].Constrain to integer tensors.Constrain to float tensorsConstrain to any numerical types.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.signalInput tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.frame_stepThe number of samples to step between successive DFTs.A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It's an optional value. frame_lengthA scalar representing the size of the DFT. It's an optional value.The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.Constrain signal and output to float tensors.Constrain scalar length types to int64_t. broadcasting: () vs (Wrong op_type name for running propagation: data_0List of tensors for The input tensor of rank >= axis.The output values with the same shape as the input tensor.X_ReduceMax = ReduceMax (input) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_ReduceMax = ReduceMax (input, axes) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) X_Log = Log (X_ReduceSum) output = Sub (X_Sub, X_Log) Invalid value for attribute axisK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.input and zero_point pair is expected to have same type.Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.is_onesided and inverse attributes cannot be enabled at the same timeinput tensor must have rank >= 2, including the complex dimension.%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()rank == static_cast(new_shape_proto.dim_size())%s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointaxis_tensor != nullptraxis input must be a scalar.axis attribute value . Valid values are '-rank <= axis && axis != -1 && axis < rank - 1'dft_length input must be a scalar.The data type of the output tensor. Strictly must be one of the values from DataType enum in TensorProto whose values correspond to T2. The default value is 1 = FLOAT. If 1, returns a window to be used as periodic function. If 0, return a symmetric window. When 'periodic' is specified, hann computes a window of length size + 1 and returns the first size points. The default value is 1. A scalar value indicating the length of the window.A {name} window with length: size. The output has the shape: [size].size input must be a scalar.size input must be greater than 0.num_mel_bins input must be scalar.dft_length input must be scalar.frame_length input must be scalar.window input must have rank = 1.If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/math/old.ccexpanded_targetinput_gather_elementloss_NCddloss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_zero_target_typedexpanded_target_int64transform_targetsconst_zero_castedinput_gather_element_transformsqueeze_maskconst_one_castedweight_gather_tempweight_gather_temp_1X_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeX_Log If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.consumed_inputslegacy optimization attribute.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsValue of alpha default to 0.2Value of beta default to 0.5List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CNumber of top elements to retrieveDimension on which to do the sort.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m, n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.For real input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][1]. For complex input, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. The first dimension is the batch dimension. The following N dimensions correspond to the signal's dimensions. The final dimension represents the real and imaginary parts of the value in that order.The length of the signal as a scalar. If greater than the axis dimension, the signal will be zero-padded up to dft_length. If less than the axis dimension, only the first dft_length values will be used as the signal. It's an optional value. The Fourier Transform of the input vector. If onesided is 0, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[signal_dimN][2]. If axis=1 and onesided is 1, the following shape is expected: [batch_idx][floor(signal_dim1/2)+1][signal_dim2]...[signal_dimN][2]. If axis=2 and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][floor(signal_dim2/2)+1]...[signal_dimN][2]. If axis=N and onesided is 1, the following shape is expected: [batch_idx][signal_dim1][signal_dim2]...[floor(signal_dimN/2)+1][2]. The signal_dim at the specified axis is equal to the dft_length. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).'shape' input must be 1D tensorDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeFirst operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInvalid value for attribute kauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Input tensor must have at least 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/nn/defs.ccThe storage order of the tensor. 0 is row major, and 1 is column major. This attribute is used only to convert an n-tuple index value into a single integer value for producing the second output. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).MaxUnpool op must have either two or three inputs.Input tensor X must have at least 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.RoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedThe weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. The shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeInput data tensor. For 2D image data, it has shape (N, C, H, W) where N is the batch size, C is the number of input channels, and H and W are the height and width. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes. Most common use cases have n = 2 or 3.Weight tensor that will be used in the convolutions. It has shape (oC, C/group, kH, kW), where oC is the number of output channels and kH and kW are the kernel height and width. For more than 2 dimensions, it has shape (oC, C/group, k1, k2, ... , kn).Offset tensor denoting the offset for the sampling locations in the convolution kernel. It has shape (N, offset_group * kH * kW * 2, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Use linear interpolationfor fractional offset values. Sampling locations outside of the padded input tensor gives zero.Optional 1D bias of length oC to be added to the convolution. Default is a tensor of zeros.The mask tensor to be applied to each position in the convolution kernel. It has shape (N, offset_group * kH * kW, oH, oW) for 2D data or (N, offset_group * k1 * k2 * ... * kn * n, o1, o2, ... , on) for nD data. Default is a tensor of ones.Output data tensor that contains the result of convolution. It has shape (N, oC, oH, oW) for 2D data or (N, oC, o1, o2, ..., on) for nD dataDilation value along each spatial axis of the kernel. Default is 1 along each axis.Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.offset_groupNumber of groups of offset. C must be divisible by offset_group. Default is 1.Padding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.DeformConvFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).input_meanrunning (training) or estimated (testing) mean tensor of shape (C).input_varrunning (training) or estimated (testing) variance tensor of shape (C).running_meanThe running mean after the BatchNormalization operator.running_varThe running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The output mask.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Constrain input to only numeric types. { Lambd = Constant () LambdCast = CastLike (Lambd, input) Bias = Constant () BiasCast = CastLike (Bias, input) Zero = Constant () ZeroCast = CastLike (Zero, input) NegLmbda = Neg (LambdCast) InputLessThanNegLambda = Less (input, NegLmbda) InputAddBias = Add (input, BiasCast) InputSubBias = Sub (input, BiasCast) LambdaLessThanInput = Less (LambdCast, input) InputSubBiasOrZero = Where (LambdaLessThanInput, InputSubBias, ZeroCast) output = Where(InputLessThanNegLambda, InputAddBias, InputSubBiasOrZero) } A tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum overScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } Attribute pads must have an even sizeinput must have rank 3.1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Input data tensor to be rearranged from column blocks back into an image. This is a 3-dimensional tensor containing [N, C * n-ary-product(block_shape), L], where N is batch dimension, C is image channel dimension and L is number of blocks.The blocks are enumerated in increasing lexicographic-order of their indices.For example, with an image-size 10*20 and block-size 9*18, there would be 2*3 blocks, enumerated in the order block(0, 0), block(0, 1), block(0, 2), block(1, 0), block(1, 1), block(1, 2).image_shapeThe shape of the spatial dimensions of the image after rearranging the column blocks.This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.block_shapeThe shape of the block to apply on the input.This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.This is the block-shape before dilation is applied to it.Output tensor produced by rearranging blocks into an image.Constrain input and output types to all numeric tensor types.%s:%u: %s: Assertion `%s` failed.BuildContextDependentFunctionBodyLayerNormalizationsinceVersion == 17 || sinceVersion == 18FloatEpsilonEpsilon = Cast (FloatEpsilon)Axes_1 = Constant()Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Tensor to be normalized.Normalized tensor.Constrain input types and output Y type to float tensors.num_groupsThe number of groups of channels. It should be a divisor of the number of channels `C`.Input data tensor. Dimensions for image cases are `(N x C x H x W)`, where `N` is the batch size, `C` is the number of channels, and `H` and `W` are the height and width of the data. Statistics are computed for every group of channels over `C`, `H`, and `W`. For non-image cases, the dimensions are in the form of `(N x C x D1 x D2 ... Dn)`.Scale tensor of shape `(num_groups)`.Bias tensor of shape `(num_groups)`.The output tensor of the same shape as `X`.GroupNormalization Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Constrain input and output types to float and 8 bit tensors.p value of the Lp norm used to pool over the input data.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads. Note that the output_shape attribute value should not include dimensions for batch size and channels, which are automatically inferred.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Invalid value() for attribute 'axis'ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2C = Shape (X)NumGroupsGroupSize = Div (C, NumGroups)N = Shape (X)InstanceShape = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (X, NewShape)Shape3D = Constant ()X3D = Reshape(XReshaped, Shape3D)Axes2Mean = ReduceMean (X3D, Axes2)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)ScaleShape = Constant ()ScaleT = Cast (scale)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/nn/old.ccExponentX_RMEX_squaredX_squaredE_XsquaredVarianceSTDX_varianceProcessed_STDThe storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter.Stride along each spatial axis.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.varThe running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.saved_meanSaved mean used during training to speed up gradient computation. Should not be used for testing.saved_varSaved variance used during training to speed up gradient computation. Should not be used for testing.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.Constrain mean and variance types to float tensors. It allows all float type for U.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn). Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/object_detection/defs.ccAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]max_output_boxes_per_classInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.Float representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected_indicesselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/object_detection/old.ccThe input element.Type of the element in the optional outputThe optional output enclosing the input element.Constrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/optional/defs.ccThe optional input.A scalar boolean tensor. If true, it indicates that optional-type input contains an element. Otherwise, it is empty.Constrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.Output element in the optional input.Constrain output type to all tensor or sequence types.Optional is expected to have an output.Attribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.OptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional-type input must contain an element with type information./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/optional/old.ccOptionalHasElement is expected to have 1 input.Input must be an optional-type value containing an element with type information.Incomplete string literal.Encountered invalid float literal!Value expected but not found.infinfinitynanExpecting primitive type as map key type.Unexpected type in sparse-tensor element type.Unexpected type.Error parsing TensorProto (expected a tensor type).Error parsing TensorProto (expected a tensor shape).Error parsing TensorProto shape (expected numeric dimension).Unhandled type: %dMismatch between expected type and specified value's typeUnexpected attribute type.Empty list attribute value requires type annotation.Singleton attribute value cannot be specified as a list.Unhandled keyword.Expected character [ParseError at position Error context: (line: column: Identifier expected but not found.Integer value expected, but not found.ir_versionopset_importproducer_nameproducer_versionmodel_versiondoc_stringmetadata_propsUnexpected literal type.String value expected, but not found.inttype_protofloatsintsstringstensorsgraphssparse_tensorstype_protosScale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/quantization/defs.ccN-D quantized input tensor to be de-quantized.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.'x_scale' determines the output type.Quantized output tensorOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/quantization/old.ccConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.minus infinity (if supported by the datatype) or the minimum value of the data type otherwise/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/reduction/defs.ccplus infinity (if supported by the datatype) or the maximum value of the data type otherwise { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } sum squareundefinedproduct { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } log summinus infinity (if supported by the datatype) or undefined otherwise { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } log sum exponent { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } L1 norm { data_square = Mul(data, data) sum_square = ReduceSum(data_square, axes) sum_square_dbl = Cast (sum_square) sqrt = Sqrt(sum_square_dbl) reduced = CastLike(sqrt, data) } L2 normThe axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 means keep reduced dimension.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type.'axis' must be in [-rank(indices), rank(indices)-1]/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/reduction/old.ccA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Constrain input and output types to high-precision and 8 bit numeric tensors.{empty_value}The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer. Computes the {name} of the input tensor's elements along the provided axes. The resulting tensor has the same rank as the input if `keepdims` equals 1. If `keepdims` equals 0, then the resulting tensor has the reduced dimension pruned. Input tensors of rank zero are valid. Reduction over an empty set of values yields {empty_value}. If the input data type is Boolean, the comparison should consider `False < True`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`.Defines behavior if 'axes' is empty. Default behavior with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).Constrain input and output types to numeric and Boolean tensors.axes as an input and attribute cannot be specified at the same time.First input tensor must have rank 3One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/rnn/defs.ccA list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size].The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size]./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/rnn/old.ccoutput_sequence Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) fowardOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0. typestr: , has unsupported type: has inconsistent type has unsupported type Operator '' has been deprecated since version ) has input size not in range [min=, max= not in allowed input sizes.) has output size has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input is marked single but has an empty string in the graph) has more outputs ()'s output Unrecognized attribute: for operator Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'type_proto' has unknown expected typeRequired attribute '' is missing.Attribute specification type mismatch.Duplicate type constraint nameCannot find a function builder that satisfies the requested opset version: op_type = , opset_version = Error parsing function body:Extra unparsed input unexpected.ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())Attributes:Inputs:(unnamed)(no doc)(no type) (no explicit description available)Outputs:(no documentation yet)Defined at ai.onnx.preview.training(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence.Constrain output types to any tensor type./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.Constrain input types to any tensor type.input_sequenceInput sequence.Input tensor to be inserted into the input sequence.positionPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain to any tensor type.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).The tensor to splitLength of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationconcat_result expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not availableInvalid ``body`` argument. Expected a graphExpected 1 or more inputs.Expected 1 or more outputs.Input 0 expected but not providedExpected a sequence type for input 0 expected but not providedSequenceMap_loop_body_itercount_cond_in_cond_out_inSequenceMap__seqlen_cond_seqemptyThe graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.additional_inputsAdditional inputs to the graphout_sequenceOutput sequence(s)Constrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMapAttribute dtype should be of integer type and specify a type.SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.Input Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input type for input at index 0 is null. Type info is expected.Invalid value of attribute 'axis'. Rank= Value=Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=Input type was null expected to have tensor or sparse tensor type. Got: Element type of input unknown expected to have sequence typeElement type of sequence input expected to have optional typeElement type of optional input expected to have map typeKey type of map input Value type of map input expected to have type but instead is nullMismatch between number of inferred and declared dimensions. inferred= declared=Mismatched type: inferred=Mismatched tensor element type:Mismatched sparse tensor element type:source sequence type missing element type.target sequence type missing element type.source optional type missing element type.target optional type missing element type.source map type missing key type.target map type missing key type.Mismatched map tensor key type:source map type missing value type.target map type missing value type.Element type of tensor or sparse tensor input was unknownInput was expected to have tensor or sparse tensor type. Got Input element type of does not match existing output type of Output was expected to have tensor type. Got Input was expected to have sequence type. Got Element type of sequence input was unknownInput was expected to have optional type. Got Element type of optional input was unknownInput was expected to have map type. Got Key type of map input was unknownValue type of map input was unknownInput was expected to have either tensor, sequence, optional or map type. Got shape input must be 1D tensorOne and only one attribute must be set out of expected to be a 1D tensor but was DThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Input tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/tensor/defs.ccThe parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.target_typeThe (first) input tensor will be cast to produce a tensor of the same type as this (second input) tensor.Output tensor produced by casting the first input tensor to have the same type as the second input tensor.(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.Specified shape for output.reshapedReshaped data.Shape of the input tensor(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.(Optional) Ending axis for slicing the shape. Negative value means counting dimensions from the back. If omitted, sizes of all axes upto (including) the last one will be included.Input tensor can be of arbitrary type.Constrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..List of tensors for concatenationOptional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.One or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.steps1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1s.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedTransposed output.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.updatesTensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Input and output types can be of any tensor type.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.Tensor of rank q + r - indices_shape[-1] - 1.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.Tensors with at least max(dims) dimensions.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).squeezedReshaped tensor with same data as input.Original tensorList of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).expandedBlocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Input tensor of any shape.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorThe scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.N-D tensor after resizingConstrain input 'X' and output 'Y' to all tensor types.Three interpolation modes: "nearest" (default), "linear" and "cubic". The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if mode is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: "round_prefer_floor" (default, as known as round half down), "round_prefer_ceil" (as known as round half up), "floor", "ceil". Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.If set to 1, "linear" and "cubic" interpolation modes will use an antialiasing filter when downscaling. Antialiasing is achieved by stretching the resampling filter by a factor max(1, 1 / scale), which means that when downsampling, more input pixels contribute to an output pixel.If provided, it specifies a subset of axes that 'roi', 'scales' and 'sizes' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Non-specified dimensions are interpreted as non-resizable. Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.roi1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X or the length of axes, if provided. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X' or the length of 'axes', if provided. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.sizesTarget size of the output tensor. Its interpretation depends on the 'keep_aspect_ratio_policy' value.The number of elements of 'sizes' should be the same as the rank of input 'X', or the length of 'axes', if provided. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double.Three interpolation modes: linear (default), nearest and cubic. The "linear" mode includes linear and N-linear interpolation modes depending on the number of spatial dimensions of the input tensor (i.e. linear for 1 spatial dimension, bilinear for 2 spatial dimensions, etc.). The "cubic" mode also includes N-cubic interpolation modes following the same rules. The "nearest" mode rounds to the nearest even index when the sampling point falls halfway between two indices.Support padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations. If index 0 represents the margin pixel, the reflected value at index -1 will be the same as the value at index 1. For location far away from the border, it will keep being reflected until becoming in bound. If pixel location x = -3.5 reflects by border -1 and becomes x' = 1.5, then reflects by border 1 and becomes x'' = 0.5.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels (voxels, etc.). If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels (voxels, etc.), making the sampling more resolution agnostic.Input tensor of rank r+2 that has shape (N, C, D1, D2, ..., Dr), where N is the batch size, C is the number of channels, D1, D2, ..., Dr are the spatial dimensions.gridInput offset of shape (N, D1_out, D2_out, ..., Dr_out, r), where D1_out, D2_out, ..., Dr_out are the spatial dimensions of the grid and output, and r is the number of spatial dimensions. Grid specifies the sampling locations normalized by the input spatial dimensions. Therefore, it should have most values in the range of [-1, 1]. If the grid has values outside the range of [-1, 1], the corresponding outputs will be handled as defined by padding_mode. Following computer vision convention, the coordinates in the length-r location vector are listed from the innermost tensor dimension to the outermost, the opposite of regular tensor indexing.Output tensor of rank r+2 that has shape (N, C, D1_out, D2_out, ..., Dr_out) of the sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.Constrain input `X` and output `Y` types to all tensor types.Constrain grid types to float tensors.if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.thetainput batch of affine matrices with shape (N, 2, 3) for 2D or (N, 3, 4) for 3Dthe target output image size (N, C, H, W) for 2D or (N, C, D, H, W) for 3Doutput tensor of shape (N, H, W, 2) of 2D sample coordinates or (N, D, H, W, 3) of 3D sample coordinates.Constrain size's type to int64 tensors. { # naming one: 1, one_f: 1.0, one_1d: [1], one_f_1d: [1.0] one = Constant () two = Constant () zero = Constant () four = Constant () one_1d = Constant () zero_1d = Constant () minus_one = Constant () minus_one_f = CastLike (minus_one, theta) zero_f = CastLike (zero, theta) one_f = CastLike (one, theta) two_f = CastLike (two, theta) constant_align_corners = Constant () constant_align_corners_equal_zero = Equal (constant_align_corners, zero) size_ndim = Size (size) condition_is_2d = Equal (size_ndim, four) N, C, D, H, W = If (condition_is_2d) < then_branch = g1 () => (N_then, C_then, D_then, H_then, W_then) { N_then, C_then, H_then, W_then = Split (size) D_then = Identity (one_1d) }, else_branch = g2 () => (N_else, C_else, D_else, H_else, W_else) { N_else, C_else, D_else, H_else, W_else = Split (size) } > size_NCDHW = Concat (N, C, D, H, W) theta_3d = If (condition_is_2d) < then_branch = g3 () => (theta_then) { # theta: N by 2 by 3 => N by 3 by 4 # use of thetaN23 is a way to make shape inference happy when theta is N by 3 by 4. gather_idx_6 = Constant () shape_23 = Constant () gather_idx_23 = Reshape (gather_idx_6, shape_23) shape_N23 = Concat (N, shape_23) gather_idx_N23 = Expand (gather_idx_23, shape_N23) thetaN23 = GatherElements (theta, gather_idx_N23) # N by 2 by 3 => N by 3 by 2 r1, r2 = Split (thetaN23) # N by 1 by 3 r1_ = Squeeze (r1) # N by 3 r2_ = Squeeze (r2) r11, r12, t1 = Split (r1_) # N by 1 r21, r22, t2 = Split (r2_) r11_shape = Shape (r21) float_zero_1d_ = ConstantOfShape (r11_shape) # N by 1 float_zero_1d = CastLike (float_zero_1d_, theta) float_one_1d = Add (float_zero_1d, one_f) # N by 1 R1 = Concat (r11, r12, float_zero_1d, t1) # N by 4 R2 = Concat (r21, r22, float_zero_1d, t2) R3 = Concat (float_zero_1d, float_zero_1d, float_one_1d, float_zero_1d) R1_ = Unsqueeze (R1, one_1d) # N by 1 by 4 R2_ = Unsqueeze (R2, one_1d) R3_ = Unsqueeze (R3, one_1d) theta_then = Concat (R1_, R2_, R3_) # N by 3 by 4 # theta_then = Identity (theta) }, else_branch = g4 () => (theta_else) { theta_else = Identity (theta) } > two_1d = Constant () three_1d = Constant () five_1d = Constant () constant_D_H_W_shape = Slice (size_NCDHW, two_1d, five_1d) # [N, C, D, H, W] => [D, H, W] zeros_D_H_W_ = ConstantOfShape (constant_D_H_W_shape) zeros_D_H_W = CastLike (zeros_D_H_W_, theta) ones_D_H_W = Add (zeros_D_H_W, one_f) D_float = CastLike (D, zero_f) H_float = CastLike (H, zero_f) W_float = CastLike (W, zero_f) start_d, step_d, start_h, step_h, start_w, step_w = If (constant_align_corners_equal_zero) < then_branch = h1 () => (start_d_then, step_d_then, start_h_then, step_h_then, start_w_then, step_w_then) { # => (float, float, float, float, float, float) step_d_then = Div (two_f, D_float) step_h_then = Div (two_f, H_float) step_w_then = Div (two_f, W_float) step_d_half = Div (step_d_then, two_f) start_d_then = Add (minus_one_f, step_d_half) step_h_half = Div (step_h_then, two_f) start_h_then = Add (minus_one_f, step_h_half) step_w_half = Div (step_w_then, two_f) start_w_then = Add (minus_one_f, step_w_half) }, else_branch = h2 () => (start_d_else, step_d_else, start_h_else, step_h_else, start_w_else, step_w_else) { # => (float, float, float, float, float, float) D_float_nimus_one = Sub (D_float, one_f) H_float_nimus_one = Sub (H_float, one_f) W_float_nimus_one = Sub (W_float, one_f) # avoid divide by 0 D_equals_one = Equal (D, one) step_d_else = If (D_equals_one) < then_branch = g5 () => (step_d_else_then) { step_d_else_then = Identity (zero_f) }, else_branch = g6 () => (step_d_else_else) { step_d_else_else = Div (two_f, D_float_nimus_one) } > step_h_else = Div (two_f, H_float_nimus_one) step_w_else = Div (two_f, W_float_nimus_one) start_d_else = Identity (minus_one_f) start_h_else = Identity (minus_one_f) start_w_else = Identity (minus_one_f) } > grid_w_steps_int = Range (zero, W, one) grid_w_steps_float = CastLike (grid_w_steps_int, step_w) grid_w_steps = Mul (grid_w_steps_float, step_w) grid_w_0 = Add (start_w, grid_w_steps) grid_h_steps_int = Range (zero, H, one) grid_h_steps_float = CastLike (grid_h_steps_int, step_h) grid_h_steps = Mul (grid_h_steps_float, step_h) grid_h_0 = Add (start_h, grid_h_steps) grid_d_steps_int = Range (zero, D, one) grid_d_steps_float = CastLike (grid_d_steps_int, step_d) grid_d_steps = Mul (grid_d_steps_float, step_d) grid_d_0 = Add (start_d, grid_d_steps) zeros_H_W_D = Transpose (zeros_D_H_W) grid_d_1 = Add (zeros_H_W_D, grid_d_0) grid_d = Transpose (grid_d_1) zeros_D_W_H = Transpose (zeros_D_H_W) grid_h_1 = Add (zeros_D_W_H, grid_h_0) grid_h = Transpose (grid_h_1) grid_w = Add (grid_w_0, zeros_D_H_W) grid_w_usqzed = Unsqueeze (grid_w, minus_one) grid_h_usqzed = Unsqueeze (grid_h, minus_one) grid_d_usqzed = Unsqueeze (grid_d, minus_one) ones_D_H_W_usqzed = Unsqueeze (ones_D_H_W, minus_one) original_grid = Concat (grid_w_usqzed, grid_h_usqzed, grid_d_usqzed, ones_D_H_W_usqzed) constant_shape_DHW_4 = Constant () original_grid_DHW_4 = Reshape (original_grid, constant_shape_DHW_4) original_grid_4_DHW_ = Transpose (original_grid_DHW_4) original_grid_4_DHW = CastLike (original_grid_4_DHW_, theta_3d) grid_N_3_DHW = MatMul (theta_3d, original_grid_4_DHW) grid_N_DHW_3 = Transpose (grid_N_3_DHW) N_D_H_W_3 = Concat (N, D, H, W, three_1d) grid_3d_else_ = Reshape (grid_N_DHW_3, N_D_H_W_3) grid_3d = CastLike (grid_3d_else_, theta_3d) # grid = Identity (grid_3d) grid = If (condition_is_2d) < then_branch = g1 () => (grid_then) { # [N, D=1, H, W, 3] => [N, H, W, 2] grid_squeezed = Squeeze (grid_3d, one_1d) # [N, H, W, 3] grid_then = Slice (grid_squeezed, zero_1d, two_1d, three_1d) # [N, H, W, 2] }, else_branch = g2 () => (grid_else) { grid_else = Identity (grid_3d) } > } Tensor to copy input into.Constrain input and output types to all tensor, sequence, and optional types.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrain to boolean tensors.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.depthScalar or Rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain input and output types to all tensor types (including bfloat).Constrain to all tensor types.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurrence in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. inverse_indicesA 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge`, `wrap`A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified.input_dataInput to extract the centered crop from.1-D tensor representing the cropping window dimensions.output_dataOutput data.If provided, it specifies a subset of axes that 'shape' refer to. If not provided, all axes are assumed [0, 1, ..., r-1], where r = rank(data). Negative value means counting dimensions from the back. Accepted range is [-r, r-1], where r = rank(data). Behavior is undefined if an axis is repeated.CenterCropPad This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array 4) `wrap` - wrap-around padding as if the data tensor forms a torus Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` Example 4 (`wrap` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [2, 1, 1, 1] mode = 'wrap' output = [ [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], [3.4, 2.3, 3.4, 2.3], [5.7, 4.5, 5.7, 4.5], [1.2, 1.0, 1.2, 1.0], ] ``` output = Cast (input)Target shape may not have multiple -1 dimensions.Invalid position of 0.Invalid dimension value: Invalid Target shape product of 0. Product cannot be 0 in combination with -1Dimension could not be inferred: incompatible shapesaxis must be in [-rank, rank-1].All inputs to Concat must have same rank. Input has rank Both 'split' input and 'num_outputs' attribute were givenMismatch between number of splits () and outputs (Mismatch between the sum of 'split' () and the split dimension of the input (Attribute `num_outputs` value cannot be lower than 1Neither 'split' input nor 'num_outputs' attribute has been givenSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsUnexpected axis value: . Expected range [Axis is referred to more than once.'step' cannot be 0 for SliceInput rank for starts and ends should be the same: (Invalid attribute perm {}, input shape = {Attribute perm for Transpose has repeated value: Input: does not match type of output: type: and Output expected to have tensor or sparse tensor typedata tensor must have rank >= 1axis must be in [-r, r-1]indices must be in [-rank, rank-1].Blocksize must be positiveInput tensor must be 4-dimensional'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Length of input 'size' is . It must be 4 for 2D or 5 for 5D.Indices tensor must have rank >= 1OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.'input' must have rank >= 2'sequence_lens' must have rank of 1Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorCenterCropPad op must have 2 inputs.`shape` only supports `int32_t` or `int64_t` inputsNumber of elements of input 'shape' () does not match the number of axes (k2axes_input = Constant ()x_shape_alldims = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)x_shape = Shape (input_data)padded_sh = Max(x_shape, shape)pad_amount = Sub(padded_sh, x_shape)pad_amount_left = Div(pad_amount, k2)pad_amount_right = Sub(pad_amount, pad_amount_left)pads = Concat (pad_amount_left, pad_amount_right)padded_input = Pad (input_data, pads, , axes_input)padded_input = Pad (input_data, pads)x_shape_alldims2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_input)x_shape2 = Shape (padded_input)sh_diff = Sub (x_shape2, shape)start_dims = Div (sh_diff, k2)end_dims = Add (start_dims, shape)output_data = Slice (padded_input, start_dims, end_dims, axes_input)output_data = Slice (padded_input, start_dims, end_dims)/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/tensor/old.cc4-D tensor of shape (N, C, H_out, W_out) of sampled values. For integer input types, intermediate values are computed as floating point and cast to integer at the end.length of each output. Values should be >= 0.1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. If 'size' is needed, the user must set 'scales' to an empty tensor.The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. May only be set if 'scales' is set to an empty tensor. Returns the indices of the elements that are non-zero (in row-major order - by dimension). NonZero behaves similar to numpy.nonzero: https://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html, but for scalar input, NonZero produces output shape (0, N) instead of (1, N), which is different from Numpy's behavior. Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.constant_value(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrain input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Optional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputpaddingsList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapetilesNumber of repeated copies to make of the input tensor.Axis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.width_scaleThe scale along width dimension. It takes value greater than or equal to 1.height_scaleThe scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar or rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Constrain input and output types to all tensor and sequence types.(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
if coordinate_transformation_mode is `"half_pixel"`,
`x_original = (x_resized + 0.5) / scale - 0.5`
if coordinate_transformation_mode is `"pytorch_half_pixel"`,
`x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
if coordinate_transformation_mode is `"align_corners"`,
`x_original = x_resized * (length_original - 1) / (length_resized - 1)`
if coordinate_transformation_mode is `"asymmetric"`,
`x_original = x_resized / scale`
if coordinate_transformation_mode is `"tf_crop_and_resize"`,
`x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. Given a tensor containing the data to be padded (`data`), a tensor containing the number of start and end pad values for axis (`pads`), (optionally) a `mode`, and (optionally) `constant_value`, a padded tensor (`output`) is generated. The three supported `modes` are (similar to corresponding modes supported by `numpy.pad`): 1) `constant`(default) - pads with a given constant value as specified by `constant_value` (which defaults to 0, empty string, or False) 2) `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis 3) `edge` - pads with the edge values of array Example 1 (`constant` mode): Insert 0 pads to the beginning of the second dimension. ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'constant' constant_value = 0.0 output = [ [0.0, 0.0, 1.0, 1.2], [0.0, 0.0, 2.3, 3.4], [0.0, 0.0, 4.5, 5.7], ] ``` Example 2 (`reflect` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'reflect' output = [ [1.0, 1.2, 1.0, 1.2], [2.3, 3.4, 2.3, 3.4], [4.5, 5.7, 4.5, 5.7], ] ``` Example 3 (`edge` mode): ``` data = [ [1.0, 1.2], [2.3, 3.4], [4.5, 5.7], ] pads = [0, 2, 0, 0] mode = 'edge' output = [ [1.0, 1.0, 1.0, 1.2], [2.3, 2.3, 2.3, 3.4], [4.5, 4.5, 4.5, 5.7], ] ``` output = Cast (input)Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid Target shape product of 0The input is not evenly splittableInput axes has invalid data'step' cannot be 0'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shape'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64rank must be greater than axisRanks inferred () is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect length'axis' must be in [-rank(indices)-1, rank(indices)]Attribute value for pads is requiredAttribute pads has incorrect lengthThe input tensor and grid tensor must have the same rank for GridSample. Got input tensor rank: Got grid tensor rank: The input tensor and grid tensor ranks must be >= 3. Got input tensor and grid tensor ranks: The last dimension of the grid tensor must be the rank of the grid tensor - 2. Got the last dimension of the grid tensor: Dimension value inferred () is not equal to the existing dim value (Either `sizes` or `scales` must be provided, but not both of themUnknown value for `keep_aspect_ratio_policy`: Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".Number of elements of input 'sizes' () must be same as rank of input 'X' (Number of elements of input 'scales' (Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * num_axes] where `num_axes` refers to the number of elements in the `axes` input or the input rank if `axes` are not provided explicitly. `pads` format should be: [x1_begin, x2_begin, ..., x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `axes[i]` and xi_end, the number of pad values added at the end of axis `axes[i]`.1-D tensor of axes that `pads` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated. If not provided, all axes are assumed (`[0, 1, ..., input_rank-1]`).'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Pads has incorrect number of values. Expected 2 * values. Got values.The type of tensor: is undefined so it cannot be parsed.ParseData type mismatch for tensor: . Expected: Actual:Cannot parse data from external tensors. Please load external data into raw data for tensor: Data size mismatch. Tensor: expected size does not match the actual sizeTensor to prepend in concatenationTensor to append in concatenationConcatenated string tensorInputs and outputs must be UTF-8 strings/Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/text/defs.ccTensor with strings to match on.Regex pattern to match on. This must be valid RE2 syntax.Tensor of bools indicating if each input string fully matches the regex pattern specified.Inputs must be UTF-8 stringsOutputs are bools and are True where there is a full regex match and False otherwise.Tensor of strings to split.Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.Maximum number of splits (from left to right). If left unset (or if the number of possible splits are less than maxsplit), it will make as many splits as possible. Note that the maximum possible number of substrings returned with `maxsplit` specified is `maxsplit+1` since the remaining suffix after the `maxsplit`th split is included in the output.Tensor of substrings representing the outcome of splitting the strings in the input on the delimiter. Note that to ensure the same number of elements are present in the final rank, this tensor will pad any necessary empty strings.The number of substrings generated for each input element.The input must be a UTF-8 string tensorTensor of substrings.The number of substrings generated.UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.Input shape must have either [C] or [1,C] dimensions where C > 0Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysmap(int64, string)map(int64, float)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.Input data. It must have the same element type as the keys_* attribute set.Output data. This tensor's element type is based on the values_* attribute set.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.A list of strings.A list of ints.A list of floats.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.A string.An integer.A float.A default tensor.{"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.Data to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output dataOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the default value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the default value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.At least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set.The input type was and the key type are different, which is not permitted for LabelEncoders.At least one of values_tensor, values_strings, values_int64s, values_floats must be set.The number of keys and the number of values must be the same in the LabelEncoder.The default tensor type and the value type The default tensor must be a singleton 1D tensor.Input's shape should be 1D or 2DExactly one of 'cats_*' attributes must be provided.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Non of classlabels_int64s or classlabels_strings is set.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.ZipMap input shape should be 1D or 2D.Input data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels./Users/runner/work/1/b/Release/_deps/onnx-src/onnx/defs/traditionalml/old.ccInput data. It can be either tensor or scalar.A list of strings. One and only one of 'keys_*'s should be set.A list of strings. One and only one of 'value_*'s should be set.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.type case mismatch. existing=key type mismatch from MapProto. existing=type case unsupported. existing=type case unsupported for symbolic shape inference. inferred=Unable to save inferred model to the target path:Graph has inputs but were providedCannot use the same name as both a subgraph initializer and subgraph input: were provided.The number of graph input cannot be smaller than the number of node inputGraph initializer names must appear after the actual inputs: Cannot find missing input: in initializers. , node name: (op_type:tensor_typesequence_typemap_typeoptional_typeopaque_typesparse_tensor_typeNOT_SETCannot infer type and shape for node name . No opset import for domain optype Container for generated shape data cannot be nullptr when enable_data_propagation option is set. is out of bounds.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.Data for input already exists.Inference error(s): Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension : (unk__onnx.AttributeProtoonnx.ValueInfoProtoonnx.NodeProtoonnx.TrainingInfoProtoonnx.ModelProtoonnx.StringStringEntryProtoonnx.TensorAnnotationonnx.GraphProtoonnx.TensorProto.Segmentonnx.TensorProtoonnx.SparseTensorProtoonnx.TensorShapeProto.Dimensiononnx.TensorShapeProtoonnx.TypeProto.Tensoronnx.TypeProto.Sequenceonnx.TypeProto.Maponnx.TypeProto.Optionalonnx.TypeProto.SparseTensoronnx.TypeProto.Opaqueonnx.TypeProtoonnx.OperatorSetIdProtoonnx.FunctionProtoonnx.SequenceProtoonnx.MapProtoonnx.OptionalProto/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/arena.ccMaybeAllocateAligned cannot fail here.CHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/extension_set.ccCHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != nullptr: Index out-of-bounds (field is empty).Extension not found.Non-primitive types can't be packed.Can't get here.Invalid message set extension.Multiple extension registrations for type "", field number /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/extension_set_inl.h/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/generated_message_util.ccNot implemented field number /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/io/coded_stream.ccA protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug./Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: Can't BackUp() after failed Next()./Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): BackUp() can not exceed the size of the last Next() call.CHECK failed: target_ != NULL: CHECK failed: (static_cast(count)) <= (target_->size()): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): (cannot determine missing fields for lite message)MessageLite at 0x/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/message_lite.ccparse exceeded maximum protobuf size of 2GB: Can't message of type "" because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.parsing/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/parse_context.hCan't happen/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/repeated_ptr_field.h/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%dINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=CANCELLEDDEADLINE_EXCEEDEDNOT_FOUNDALREADY_EXISTSPERMISSION_DENIEDUNAUTHENTICATEDRESOURCE_EXHAUSTEDFAILED_PRECONDITIONABORTEDOUT_OF_RANGEUNIMPLEMENTEDINTERNALUNAVAILABLEDATA_LOSS/Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/stubs/stringpiece.ccsize too big: details: /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/stubs/strutil.ccCHECK failed: dest: \x%02x\%03oCHECK failed: i >= 0: FastHexToBuffer() wants non-negative integers, not -inf%.*gCHECK failed: value != nullptr: nullptr output boolean given.yesfnon00123456789abcdefCHECK failed: s != nullptr: This can't happen; base64 decoder state = Logic problem? szsrc = %.1fCHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size - 1]) == ('5'): CHECK failed: (size) <= (6): basic_stringCHECK failed: result != nullptr: /Users/runner/work/1/b/Release/_deps/protobuf-src/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kInt32MaxSize): 'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. serializing/Users/runner/work/1/b/Release/_deps/re2-src/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = Unexpected opcode: /Users/runner/work/1/b/Release/_deps/re2-src/re2/compile.ccshould never happenCompiler::Copy called!No ranges in char classMissing case in Compiler: hello, world/Users/runner/work/1/b/Release/_deps/re2-src/re2/walker-inl.hStack not empty.Walk NULL|%s%d(%p)|| flag=%#x/Users/runner/work/1/b/Release/_deps/re2-src/re2/dfa.ccunhandled opcode: DeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.context does not contain textFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset/Users/runner/work/1/b/Release/_deps/re2-src/re2/nfa.ccunhandled in AddToThreadqUnhandled in step(?,?)(%td,?)(%td,%td)Bad args: nsubmatch=Unexpected opcode in short circuit: in Prog::Fanout()/Users/runner/work/1/b/Release/_deps/re2-src/re2/onepass.ccCannot use SearchOnePass for unanchored matches./Users/runner/work/1/b/Release/_deps/re2-src/re2/parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad call to ParseState::ParsePerlFlagsAddFoldedRange recurses too much.AnyBad hex digit LuLlLtLmLoNlMnMcNdPc\d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]alt -> %d | %daltmatch -> %d | %dbyte%s [%02x-%02x] %d -> %d/icapture %d -> %demptywidth %#x -> %dmatch! %dnop -> %dfail[%02x-%02x] -> %d %d. %s %d+ %s /Users/runner/work/1/b/Release/_deps/re2-src/re2/re2.ccUnknown encoding Error parsing 'Error compiling 'pattern too large - compile failed\x00Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: Unexpected re_anchor value: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyRewrite schema error: '\' not allowed at end.Rewrite schema error: '\' must be followed by a digit or '\'.Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.invalid substitution \ from groupsinvalid rewrite pattern: Error reverse compiling '/Users/runner/work/1/b/Release/_deps/re2-src/re2/regexp.ccRegexp not destroyed.Bad reference count Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )unexpected )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit calledNamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit called/Users/runner/work/1/b/Release/_deps/re2-src/re2/simplify.ccCase not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat [truncated](?:/Users/runner/work/1/b/Release/_deps/re2-src/re2/tostring.cckRegexpCapture cap() == 0?P<>[^\x00-\x{10ffff}](?:)Bad final char: ?{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(){}[]*+?|.^$\[]^-\\\r\t\n\f\x{%x}AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeChorasmianCoCommonCopticCsCuneiformCypriotCypro_MinoanCyrillicDeseretDevanagariDives_AkuruDograDuployanEgyptian_HieroglyphsElbasanElymaicEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhitan_Small_ScriptKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoModiMongolianMroMultaniMyanmarNabataeanNandinagariNew_Tai_LueNewaNkoNoNushuNyiakeng_Puachue_HmongOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOld_UyghurOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangsaTangutTeluguThaanaThaiTibetanTifinaghTirhutaTotoUgariticVaiVithkuqiWanchoWarang_CitiYezidiYiZanabazar_SquareZlZpZsmu held in reader and writer mode simultaneously on entry to nsync_cv_wait_with_deadline() mu not held on entry to nsync_cv_wait_with_deadline() checking a waiter condition while unlocked attempt to nsync_mu_unlock() an nsync_mu held in read mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_runlock() an nsync_mu not held in read mode nsync_mu not held in write mode nsync_mu not held in some mode nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline() nsync_mu_wait woke but condition not true panic: unique_lock::lock: references null mutexunique_lock::unlock: not locked__isPlatformOrVariantPlatformVersionAtLeastos_version_check.cPlatform2 == PLATFORM_MACOS && "unexpected platform"kCFAllocatorNullCFDataCreateWithBytesNoCopyCFPropertyListCreateWithDataCFPropertyListCreateFromXMLDataCFStringCreateWithCStringNoCopyCFDictionaryGetValueCFGetTypeIDCFStringGetTypeIDCFStringGetCStringCFRelease/System/Library/CoreServices/SystemVersion.plistProductVersion:F &2j --\K$)   (   = --\K$3  & 2   l . 7a #/_&/BOx*  8")L0< 3VwOj h&*A fFA6( _o!^   8") C L!P 9.H U     !oDUhy%7Jz[r!l%7Jz[r)!! *#q  ??RG2 /k! ??RG2~__   ]    !     R2   M \\ h#jSS 1 % }} 9# Vo^s&#}}T . 2 .^ftE^ftE 2:U=&]e 0%#}} aKt|E  )#}},5 " 2 ;%#}}paKM%   r(#}}     &/BOx*  8")L0< 3VwOj h&*A  8")n f   h    ~ Ox*OM &%D Q  :M &9.D Q     ) !L  fFA6( _o!^ -#Z1)a  5 " 4 >(#}}t ) 5!* & 29.45-)   8")!D Q 9  8")-O ][+%#}}\- 9Tbg% }}, \1" +% }}`; 6      E1]e+  [(#}}\E1]e+  [(#}}-  :IN%s }}`-  :IN%s }}0- 8GL%q }}5 4 C P%#}}LM7qy*  VV#}}x- +ETY%~ }}H9%% 8 BQ%#}} -b4W%#}}`]Fu  (#}}|YDqy*   (#}} YDqy*   (#}}H.  0         6'   ]    ) # }}- ":KP%u }}l- ":KP%u }}<9# %#}}|9# %#}}@1L X(#}} 1L X(#}}\- ":KP%u }},5 >W `%#}}p=&'Oz(#}}0SS wO  <    :        D  *   ] &  6  &  6  *!!!   r  h  2 J  2 JZ! 1  1  !&!2!    #2 %%K%%$ $  % %  %%K%%K$$$$$$K$$K$$K$$3VwOj hk@       r??)))) ))K./..+.:- + > t+*2 -+**,+Q++* ,*+ + +)* +Z-D$+ - * - *!" )#")#)#( .( +( +( *( *)*) *)*)*)!e +>xWp fF  8")A6( _o!^ $NN"s><">Dii! ~i   L &*AOx*L  8")&/B0< !e x8pQh%o6 Mg9.D Q    9.D Q     c c <     $ $ $ $   K !1 *&9AL>A2hI>M &q@(  !:(M &!D Q !5 9hhp,/`! 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uActivationMlasConvPostProcessFloatFma3Filter4Output2MlasConvPostProcessFloatAvxFilter4Output2.LPostProcessBlock.4.2.SkipAccumulateOutput.LPostProcessBlock.4.2.SkipBiasAddition.LPostProcessBlock.4.2.SkipReluActivationMlasConvPostProcessFloatFma3Filter4Output3MlasConvPostProcessFloatAvxFilter4Output3.LPostProcessBlock.4.3.SkipAccumulateOutput.LPostProcessBlock.4.3.SkipBiasAddition.LPostProcessBlock.4.3.SkipReluActivation_MlasPoolMaximumFloatKernelAvx.LMaximum.ProcessOutputCountLeftPad.LMaximum.ProcessOutputCount.LMaximum.ProcessNextOutputCountBy3.LMaximum.3.ProcessNextRow.LMaximum.3.ProcessNextColumn.LMaximum.3.SkipOverPadding.LMaximum.3.HandlePostProcessing.LMaximum.ProcessRemainingOutputCount.LMaximum.ProcessOutputCountRightPad.LMaximum.ExitKernelMlasPoolMaximumFloatSingleAvx.LMaximum.1.ProcessNextRow.LMaximum.1.ProcessNextColumn.LMaximum.1.SkipOverPadding.LMaximum.1.HandlePostProcessing_MlasPoolAverageExcludePadFloatKernelAvx.LAverageExcludePad.ProcessOutputCountLeftPad.LAverageExcludePad.ProcessOutputCount.LAverageExcludePad.ProcessNextOutputCountBy3.LAverageExcludePad.3.ProcessNextRow.LAverageExcludePad.3.ProcessNextColumn.LAverageExcludePad.3.SkipOverPadding.LAverageExcludePad.3.HandlePostProcessing.LAverageExcludePad.ProcessRemainingOutputCount.LAverageExcludePad.ProcessOutputCountRightPad.LAverageExcludePad.ExitKernelMlasPoolAverageExcludePadFloatSingleAvx.LAverageExcludePad.1.ProcessNextRow.LAverageExcludePad.1.ProcessNextColumn.LAverageExcludePad.1.SkipOverPadding.LAverageExcludePad.1.HandlePostProcessing_MlasPoolAverageIncludePadFloatKernelAvx.LAverageIncludePad.ProcessOutputCountLeftPad.LAverageIncludePad.ProcessOutputCount.LAverageIncludePad.ProcessNextOutputCountBy3.LAverageIncludePad.3.ProcessNextRow.LAverageIncludePad.3.ProcessNextColumn.LAverageIncludePad.3.SkipOverPadding.LAverageIncludePad.3.HandlePostProcessing.LAverageIncludePad.ProcessRemainingOutputCount.LAverageIncludePad.ProcessOutputCountRightPad.LAverageIncludePad.ExitKernelMlasPoolAverageIncludePadFloatSingleAvx.LAverageIncludePad.1.ProcessNextRow.LAverageIncludePad.1.ProcessNextColumn.LAverageIncludePad.1.SkipOverPadding.LAverageIncludePad.1.HandlePostProcessing_MlasReduceMaximumF32KernelAvx.LReduceMaximum.ProcessRemainingCountBy32.LReduceMaximum.ProcessRemainingCountBy8.LReduceMaximum.ProcessRemainingCountLessThan8.LReduceMaximum.ProcessRemainingCountBy1.LReduceMaximum.ExitKernel_MlasComputeSoftmaxOutputF32KernelAvx.LComputeSoftmaxOutput.ProcessRemainingCountBy32.LComputeSoftmaxOutput.ProcessRemainingCountBy8.LComputeSoftmaxOutput.ProcessRemainingCountLessThan8.LComputeSoftmaxOutput.ProcessRemainingCountBy1.LComputeSoftmaxOutput.ExitKernel_MlasComputeLogSoftmaxOutputF32KernelAvx.LComputeLogSoftmaxOutput.ProcessRemainingCountBy32.LComputeLogSoftmaxOutput.ProcessRemainingCountBy8.LComputeLogSoftmaxOutput.ProcessRemainingCountLessThan8.LComputeLogSoftmaxOutput.ProcessRemainingCountBy1.LComputeLogSoftmaxOutput.ExitKernel_MlasReduceMinimumMaximumF32KernelAvx_MlasGemmU8S8CopyPackAAvx2.LCopyPackA.ProcessNextRowM4.LCopyPackA.ProcessNextColumnLoopM4.LCopyPackA.ProcessRemainingColumnsM4.LCopyPackA.CopyRemainingCountKLessThan16M4.LCopyPackA.CopyRemainingCountKLessThan8M4.LCopyPackA.CopyRemainingCountKLessThan4M4.LCopyPackA.CopyRemainingCountKLessThan2M4.LCopyPackA.ProcessPaddedMatrixADataM4.LCopyPackA.ReduceRowSumBufferM4.LCopyPackA.ProcessRemainingRows.LCopyPackA.ProcessNextRowM1.LCopyPackA.ProcessNextColumnLoopM1.LCopyPackA.ProcessRemainingColumnsM1.LCopyPackA.CopyRemainingCountKLessThan16M1.LCopyPackA.CopyRemainingCountKLessThan8M1.LCopyPackA.CopyRemainingCountKLessThan4M1.LCopyPackA.CopyRemainingCountKLessThan2M1.LCopyPackA.ProcessPaddedMatrixADataM1.LCopyPackA.ReduceRowSumBufferM1.LCopyPackA.ExitRoutine_MlasGemmU8S8CopyPackBAvx2.LCopyPackB.SkipUnsignedBitFlipVector.LCopyPackB.ProcessNextColumnN16.LCopyPackB.ProcessNextRowLoopN16.LCopyPackB.InterleaveRowDataN16.LCopyPackB.ProcessRemainingRowsN16.LCopyPackB.StoreColumnSumBufferN16.LCopyPackB.ProcessRemainingColumns.LCopyPackB.ExitRoutine.LCopyPackB.ProcessColumnNUnaligned.LCopyPackB.ProcessNextRowLoopNUnaligned.LCopyPackB.CopyRemainingCountNLessThan8K4.LCopyPackB.CopyRemainingCountNLessThan4K4.LCopyPackB.CopyRemainingCountNLessThan2K4.LCopyPackB.ProcessPaddedMatrixBData.LCopyPackB.ProcessRemainingRowsNUnaligned.LCopyPackB.CopyUnalignedRowLoop.LCopyPackB.CopyRemainingCountNLessThan8KSmall.LCopyPackB.CopyRemainingCountNLessThan4KSmall.LCopyPackB.CopyRemainingCountNLessThan2KSmall.LCopyPackB.DoneCopyRemainingCountNKSmall.LCopyPackB.StoreColumnSumBufferNUnaligned_MlasGemvU8S8KernelAvx2.LProcessRowLoop4.LProcessColumnLoop4By32.LSkipAccumulateOutput4By32.LStoreOutput4By32.LAdvanceRowLoop4.LProcessRemainingRows.LExitKernel.LProcessColumnLoop4By8.LSkipAccumulateOutput4By8.LProcessColumn4By4.LSkipAccumulateOutput4By4.LProcessColumn4BySmallN.LProcessColumn4By1.LComputeOutput4BySmallN.LStoreOutput4BySmallN.LProcessRemainingSmallK.LLoadVectorASingleRemainingByte.LBroadcastVectorARemainingBytes.LPro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lockLoopExit971.LProduceWithU8S8AvxVnni928.LComputeBlockBy4Loop977.LProcessRemainingBlocks977.LComputeBlockBy1Loop977.LComputeBlockLoopExit977.LProduceWithU8U8Avx2928.LComputeBlockBy1Loop1073.LExitProduceOutputBlock928.LComputeBlockLoopExit1073.LSkipAccumulateOutput16xNBlock927.LExitProcessCountM927.LProcessRemainingCountN927.LSkipScaleByZeroPointB1113.LAccumulatorsInitialized1113.LComputeBlockBy1Loop1156.LComputeBlockLoopExit1156.LProduceWithU8S8AvxVnni1113.LComputeBlockBy1Loop1158.LComputeBlockLoopExit1158.LProduceWithU8U8Avx21113.LComputeBlockBy1Loop1176.LExitProduceOutputBlock1113.LComputeBlockLoopExit1176.LSkipAccumulateOutput8xNBlock927.LOutputMasked16xNBlock927.LSkipAccumulateOutputMasked16xNBlock927.LOutputMasked8xNBlock927.LSkipAccumulateOutputMasked8xNBlock927.LProcessCountM3.LProcessNextColumnLoop16xN1234.LSkipScaleByZeroPointB1235.LAccumulatorsInitialized1235.LComputeBlockBy1Loop1278.LComputeBlockLoopExit1278.LProduceWithU8S8AvxVnni1235.LComputeBlockBy1Loop1292.LComputeBlockLoopExit1292.LProduceWithU8U8Avx21235.LComputeBlockBy1Loop1322.LExitProduceOutputBlock1235.LComputeBlockLoopExit1322.LSkipAccumulateOutput16xNBlock1234.LExitProcessCountM1234.LProcessRemainingCountN1234.LSkipScaleByZeroPointB1364.LAccumulatorsInitialized1364.LComputeBlockBy1Loop1407.LComputeBlockLoopExit1407.LProduceWithU8S8AvxVnni1364.LComputeBlockBy1Loop1421.LComputeBlockLoopExit1421.LProduceWithU8U8Avx21364.LComputeBlockBy1Loop1445.LExitProduceOutputBlock1364.LComputeBlockLoopExit1445.LSkipAccumulateOutput8xNBlock1234.LOutputMasked16xNBlock1234.LSkipAccumulateOutputMasked16xNBlock1234.LOutputMasked8xNBlock1234.LSkipAccumulateOutputMasked8xNBlock1234.LProcessCountM5.LProcessNextColumnLoop16xN1505.LSkipScaleByZeroPointB1506.LAccumulatorsInitialized1506.LProduceWithU8S8AvxVnni1506.LComputeBlockBy1Loop1549.LComputeBlockLoopExit1549.LProduceWithU8U8Avx21506.LComputeBlockBy1Loop1589.LExitProduceOutputBlock1506.LComputeBlockLoopExit1589.LSkipAccumulateOutput16xNBlock1505.LExitProcessCountM1505.LProcessRemainingCountN1505.LSkipScaleByZeroPointB1633.LAccumulatorsInitialized1633.LProduceWithU8S8AvxVnni1633.LComputeBlockBy1Loop1676.LComputeBlockLoopExit1676.LProduceWithU8U8Avx21633.LComputeBlockBy1Loop1706.LExitProduceOutputBlock1633.LComputeBlockLoopExit1706.LSkipAccumulateOutput8xNBlock1505.LOutputMasked16xNBlock1505.LSkipAccumulateOutputMasked16xNBlock1505.LOutputMasked8xNBlock1505.LSkipAccumulateOutputMasked8xNBlock1505_MlasConvSymKernelAvx2.LProcessNextInputBlock0.LInputDirect0.LInputIndirection0.LComputeBlockLoopStart0.LComputeBlockBy4Loop2.LProcessRemainingBlocks2.LComputeBlockBy1Loop2.LComputeBlockLoopExit2.LComputeBlockLoopDone0.LBroadcastScaleValue0.LConvertLowAccumulatorsToFloat0.LConvertHighAccumulatorsToFloat0.LConvertFloatsToIntegers0.LStoreQuantizedOutputBy160.LStoreQuantizedOutput4By160.LStoreQuantizedOutput3By160.LStoreQuantizedOutput2By160.LStoreQuantizedOutput1By160.LExitKernel0.LStoreQuantizedOutputBy80.LStoreQuantizedOutput4By80.LStoreQuantizedOutput3By80.LStoreQuantizedOutput2By80.LStoreQuantizedOutput1By80.LComputeBlockLoopBy20.LComputeBlockBy1Loop106.LComputeBlockLoopExit106_MlasConvSymDepthwiseKernelAvx2.LProcessNextInputBlock122.LBroadcastScaleValue122.LConvertLowAccumulatorsToFloat122.LConvertHighAccumulatorsToFloat122.LConvertFloatsToIntegers122.LStoreQuantizedOutputBy16122.LStoreQuantizedOutput4By16122.LStoreQuantizedOutput3By16122.LStoreQuantizedOutput2By16122.LStoreQuantizedOutput1By16122.LExitKernel122_MlasConvSymKernelAvxVnni.LProcessNextInputBlock150.LInputDirect150.LInputIndirection150.LComputeBlockLoopStart150.LComputeBlockBy4Loop152.LProcessRemainingBlocks152.LComputeBlockBy1Loop152.LComputeBlockLoopExit152.LComputeBlockLoopDone150.LBroadcastScaleValue150.LConvertLowAccumulatorsToFloat150.LConvertHighAccumulatorsToFloat150.LConvertFloatsToIntegers150.LStoreQuantizedOutputBy16150.LStoreQuantizedOutput6By16150.LStoreQuantizedOutput5By16150.LStoreQuantizedOutput4By16150.LStoreQuantizedOutput3By16150.LStoreQuantizedOutput2By16150.LStoreQuantizedOutput1By16150.LExitKernel150.LStoreQuantizedOutputBy8150.LStoreQuantizedOutput6By8150.LStoreQuantizedOutput5By8150.LStoreQuantizedOutput4By8150.LStoreQuantizedOutput3By8150.LStoreQuantizedOutput2By8150.LStoreQuantizedOutput1By8150.LComputeBlockLoopBy4150.LComputeBlockBy1Loop386.LComputeBlockLoopExit386.LComputeBlockLoopBy2150.LComputeBlockBy1Loop420.LComputeBlockLoopExit420_MlasConvSymDepthwiseKernelAvxVnni.LProcessNextInputBlock444.LBroadcastScaleValue444.LConvertLowAccumulatorsToFloat444.LConvertHighAccumulatorsToFloat444.LConvertFloatsToIntegers444.LStoreQuantizedOutputBy16444.LStoreQuantizedOutput4By16444.LStoreQuantizedOutput3By16444.LStoreQuantizedOutput2By16444.LStoreQuantizedOutput1By16444.LExitKernel444_MlasGemmDoubleKernelFma3.LProcessCountM2.LProcessNextColumnLoop2xN3.LComputeBlockBy4Loop5.LProcessRemainingBlocks5.LComputeBlockBy1Loop5.LOutputBlock5.LMultiplyAlpha2xNBlock3.LStore2xNBlock3.LProcessRemainingCountN3.LComputeBlockBy4Loop199.LProcessRema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525.LProcessRemainingBlocks1525.LComputeBlockBy1Loop1525.LOutputBlock1525.LMultiplyAlpha2xNBlock1523.LStore2xNBlock1523.LProcessRemainingCountN1523.LComputeBlockBy4Loop1740.LProcessRemainingBlocks1740.LComputeBlockBy1Loop1740.LOutputBlock1740.LMultiplyAlpha1xNBlock1523.LStore1xNBlock1523.LOutputMasked2xNBlock1523.LMultiplyAlphaMasked2xNBlock1523.LStoreMasked2xNBlock1523.LOutputMasked1xNBlock1523.LMultiplyAlphaMasked1xNBlock1523.LStoreMasked1xNBlock1523.LProcessCountM5.LProcessNextColumnLoop2xN1934.LComputeBlockBy4Loop1936.LProcessRemainingBlocks1936.LComputeBlockBy1Loop1936.LOutputBlock1936.LMultiplyAlpha2xNBlock1934.LStore2xNBlock1934.LProcessRemainingCountN1934.LComputeBlockBy4Loop2193.LProcessRemainingBlocks2193.LComputeBlockBy1Loop2193.LOutputBlock2193.LMultiplyAlpha1xNBlock1934.LStore1xNBlock1934.LOutputMasked2xNBlock1934.LMultiplyAlphaMasked2xNBlock1934.LStoreMasked2xNBlock1934.LOutputMasked1xNBlock1934.LMultiplyAlphaMasked1xNBlock1934.LStoreMasked1xNBlock1934_MlasConvNchwFloatKernelFma3.LNchw.4.ProcessOutputCount.LNchw.4.ProcessNextOutputCountBy3.LNchw.4.3.ProcessNextRow.LNchw.4.3.ProcessNextColumn.LNchw.4.3.SkipOverPadding.LNchw.4.3.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCount.LNchw.4.2.ProcessNextRow.LNchw.4.2.ProcessNextColumn.LNchw.4.2.SkipOverPadding.LNchw.4.2.HandlePostProcessing.LNchw.4.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount3.LNchw.3.ProcessOutputCount.LNchw.3.ProcessNextOutputCountBy3.LNchw.3.3.ProcessNextRow.LNchw.3.3.ProcessNextColumn.LNchw.3.3.SkipOverPadding.LNchw.3.3.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCount.LNchw.3.2.ProcessNextRow.LNchw.3.2.ProcessNextColumn.LNchw.3.2.SkipOverPadding.LNchw.3.2.HandlePostProcessing.LNchw.3.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCountLessThan3.LNchw.2.ProcessOutputCount.LNchw.2.ProcessNextOutputCountBy3.LNchw.2.3.ProcessNextRow.LNchw.2.3.ProcessNextColumn.LNchw.2.3.SkipOverPadding.LNchw.2.3.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCount.LNchw.2.2.ProcessNextRow.LNchw.2.2.ProcessNextColumn.LNchw.2.2.SkipOverPadding.LNchw.2.2.HandlePostProcessing.LNchw.2.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount1.LNchw.1.ProcessOutputCount.LNchw.1.ProcessNextOutputCountBy3.LNchw.1.3.ProcessNextRow.LNchw.1.3.ProcessNextColumn.LNchw.1.3.SkipOverPadding.LNchw.1.3.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleFma3Filter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing_MlasConvNchwcFloatKernelFma3.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy3.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy3.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy3.LNchwc.2.3.ProcessNextRow.LNchwc.2.3.ProcessNextColumn.LNchwc.2.3.SkipOverPadding.LNchwc.2.3.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.2.ProcessNextRow.LNchwc.2.2.ProcessNextColumn.LNchwc.2.2.SkipOverPadding.LNchwc.2.2.HandlePostProcessing.LNchwc.2.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount1.LNchwc.1.ProcessOutputCount.LNchwc.1.ProcessNextOutputCountBy3.LNchwc.1.3.ProcessNextRow.LNchwc.1.3.ProcessNextColumn.LNchwc.1.3.SkipOverPadding.LNchwc.1.3.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleFma3Filter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter2.LNchwc.2.1.ProcessNextRow.LNchwc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CoreML13Specification36PaddingLayerParams_PaddingReflectionE__ZTVN6CoreML13Specification37PaddingLayerParams_PaddingReplicationE__ZTVN6CoreML13Specification18PaddingLayerParamsE__ZTVN6CoreML13Specification17ConcatLayerParamsE__ZTVN6CoreML13Specification14LRNLayerParamsE__ZTVN6CoreML13Specification18SoftmaxLayerParamsE__ZTVN6CoreML13Specification16SplitLayerParamsE__ZTVN6CoreML13Specification14AddLayerParamsE__ZTVN6CoreML13Specification19MultiplyLayerParamsE__ZTVN6CoreML13Specification24UnaryFunctionLayerParamsE__ZTVN6CoreML13Specification19UpsampleLayerParamsE__ZTVN6CoreML13Specification25ResizeBilinearLayerParamsE__ZTVN6CoreML13Specification21CropResizeLayerParamsE__ZTVN6CoreML13Specification15BiasLayerParamsE__ZTVN6CoreML13Specification16ScaleLayerParamsE__ZTVN6CoreML13Specification23LoadConstantLayerParamsE__ZTVN6CoreML13Specification22L2NormalizeLayerParamsE__ZTVN6CoreML13Specification18FlattenLayerParamsE__ZTVN6CoreML13Specification18ReshapeLayerParamsE__ZTVN6CoreML13Specification18PermuteLayerParamsE__ZTVN6CoreML13Specification25ReorganizeDataLayerParamsE__ZTVN6CoreML13Specification16SliceLayerParamsE__ZTVN6CoreML13Specification17ReduceLayerParamsE__ZTVN6CoreML13Specification15CropLayerParamsE__ZTVN6CoreML13Specification18AverageLayerParamsE__ZTVN6CoreML13Specification14MaxLayerParamsE__ZTVN6CoreML13Specification14MinLayerParamsE__ZTVN6CoreML13Specification21DotProductLayerParamsE__ZTVN6CoreML13Specification32MeanVarianceNormalizeLayerParamsE__ZTVN6CoreML13Specification25SequenceRepeatLayerParamsE__ZTVN6CoreML13Specification26SimpleRecurrentLayerParamsE__ZTVN6CoreML13Specification14GRULayerParamsE__ZTVN6CoreML13Specification10LSTMParamsE__ZTVN6CoreML13Specification16LSTMWeightParamsE__ZTVN6CoreML13Specification29UniDirectionalLSTMLayerParamsE__ZTVN6CoreML13Specification28BiDirectionalLSTMLayerParamsE__ZTVN6CoreML13Specification39CustomLayerParams_CustomLayerParamValueE__ZTVN6CoreML13Specification42CustomLayerParams_ParametersEntry_DoNotUseE__ZTVN6CoreML13Specification17CustomLayerParamsE__ZTVN6CoreML13Specification20TransposeLayerParamsE__ZTVN6CoreML13Specification24BatchedMatMulLayerParamsE__ZTVN6CoreML13Specification19ConcatNDLayerParamsE__ZTVN6CoreML13Specification20SoftmaxNDLayerParamsE__ZTVN6CoreML13Specification18ReverseLayerParamsE__ZTVN6CoreML13Specification21ReverseSeqLayerParamsE__ZTVN6CoreML13Specification25LoadConstantNDLayerParamsE__ZTVN6CoreML13Specification19FillLikeLayerParamsE__ZTVN6CoreML13Specification21FillStaticLayerParamsE__ZTVN6CoreML13Specification22FillDynamicLayerParamsE__ZTVN6CoreML13Specification29WhereBroadcastableLayerParamsE__ZTVN6CoreML13Specification14SinLayerParamsE__ZTVN6CoreML13Specification14CosLayerParamsE__ZTVN6CoreML13Specification14TanLayerParamsE__ZTVN6CoreML13Specification15AsinLayerParamsE__ZTVN6CoreML13Specification15AcosLayerParamsE__ZTVN6CoreML13Specification15AtanLayerParamsE__ZTVN6CoreML13Specification15SinhLayerParamsE__ZTVN6CoreML13Specification15CoshLayerParamsE__ZTVN6CoreML13Specification15TanhLayerParamsE__ZTVN6CoreML13Specification16AsinhLayerParamsE__ZTVN6CoreML13Specification16AcoshLayerParamsE__ZTVN6CoreML13Specification16AtanhLayerParamsE__ZTVN6CoreML13Specification27PowBroadcastableLayerParamsE__ZTVN6CoreML13Specification15Exp2LayerParamsE__ZTVN6CoreML13Specification23WhereNonZeroLayerParamsE__ZTVN6CoreML13Specification25MatrixBandPartLayerParamsE__ZTVN6CoreML13Specification26UpperTriangularLayerParamsE__ZTVN6CoreML13Specification26LowerTriangularLayerParamsE__ZTVN6CoreML13Specification26BroadcastToLikeLayerParamsE__ZTVN6CoreML13Specification28BroadcastToStaticLayerParamsE__ZTVN6CoreML13Specification29BroadcastToDynamicLayerParamsE__ZTVN6CoreML13Specification27AddBroadcastableLayerParamsE__ZTVN6CoreML13Specification27MaxBroadcastableLayerParamsE__ZTVN6CoreML13Specification27MinBroadcastableLayerParamsE__ZTVN6CoreML13Specification27ModBroadcastableLayerParamsE__ZTVN6CoreML13Specification32FloorDivBroadcastableLayerParamsE__ZTVN6CoreML13Specification32SubtractBroadcastableLayerParamsE__ZTVN6CoreML13Specification32MultiplyBroadcastableLayerParamsE__ZTVN6CoreML13Specification30DivideBroadcastableLayerParamsE__ZTVN6CoreML13Specification17GatherLayerParamsE__ZTVN6CoreML13Specification18ScatterLayerParamsE__ZTVN6CoreML13Specification19GatherNDLayerParamsE__ZTVN6CoreML13Specification20ScatterNDLayerParamsE__ZTVN6CoreML13Specification26GatherAlongAxisLayerParamsE__ZTVN6CoreML13Specification27ScatterAlongAxisLayerParamsE__ZTVN6CoreML13Specification16StackLayerParamsE__ZTVN6CoreML13Specification32RankPreservingReshapeLayerParamsE__ZTVN6CoreML13Specification26ConstantPaddingLayerParamsE__ZTVN6CoreML13Specification27RandomNormalLikeLayerParamsE__ZTVN6CoreML13Specification29RandomNormalStaticLayerParamsE__ZTVN6CoreML13Specification30RandomNormalDynamicLayerParamsE__ZTVN6CoreML13Specification28RandomUniformLikeLayerParamsE__ZTVN6CoreML13Specification30RandomUniformStaticLayerParamsE__ZTVN6CoreML13Specification31RandomUniformDynamicLayerParamsE__ZTVN6CoreML13Specification30RandomBernoulliLikeLayerParamsE__ZTVN6CoreML13Specification32RandomBernoulliStaticLayerParamsE__ZTVN6CoreML13Specification33RandomBernoulliDynamicLayerParamsE__ZTVN6CoreML13Specification34CategoricalDistributionLayerParamsE__ZTVN6CoreML13Specification19ReduceL1LayerParamsE__ZTVN6CoreML13Specification19ReduceL2LayerParamsE__ZTVN6CoreML13Specification20ReduceMaxLayerParamsE__ZTVN6CoreML13Specification20ReduceMinLayerParamsE__ZTVN6CoreML13Specification20ReduceSumLayerParamsE__ZTVN6CoreML13Specification21ReduceProdLayerParamsE__ZTVN6CoreML13Specification21ReduceMeanLayerParamsE__ZTVN6CoreML13Specification23ReduceLogSumLayerParamsE__ZTVN6CoreML13Specification26ReduceSumSquareLayerParamsE__ZTVN6CoreML13Specification26ReduceLogSumExpLayerParamsE__ZTVN6CoreML13Specification21ExpandDimsLayerParamsE__ZTVN6CoreML13Specification22FlattenTo2DLayerParamsE__ZTVN6CoreML13Specification24ReshapeStaticLayerParamsE__ZTVN6CoreML13Specification22ReshapeLikeLayerParamsE__ZTVN6CoreML13Specification25ReshapeDynamicLayerParamsE__ZTVN6CoreML13Specification18SqueezeLayerParamsE__ZTVN6CoreML13Specification15TopKLayerParamsE__ZTVN6CoreML13Specification17ArgMaxLayerParamsE__ZTVN6CoreML13Specification17ArgMinLayerParamsE__ZTVN6CoreML13Specification18SplitNDLayerParamsE__ZTVN6CoreML13Specification15CeilLayerParamsE__ZTVN6CoreML13Specification16RoundLayerParamsE__ZTVN6CoreML13Specification16FloorLayerParamsE__ZTVN6CoreML13Specification15SignLayerParamsE__ZTVN6CoreML13Specification15ClipLayerParamsE__ZTVN6CoreML13Specification22SliceStaticLayerParamsE__ZTVN6CoreML13Specification23SliceDynamicLayerParamsE__ZTVN6CoreML13Specification15TileLayerParamsE__ZTVN6CoreML13Specification19GetShapeLayerParamsE__ZTVN6CoreML13Specification14ErfLayerParamsE__ZTVN6CoreML13Specification15GeluLayerParamsE__ZTVN6CoreML13Specification22RangeStaticLayerParamsE__ZTVN6CoreML13Specification23RangeDynamicLayerParamsE__ZTVN6CoreML13Specification25SlidingWindowsLayerParamsE__ZTVN6CoreML13Specification29LayerNormalizationLayerParamsE__ZTVN6CoreML13Specification32NonMaximumSuppressionLayerParamsE__ZTVN6CoreML13Specification22ClampedReLULayerParamsE__ZTVN6CoreML13Specification18ArgSortLayerParamsE__ZTVN6CoreML13Specification22SliceBySizeLayerParamsE__ZTVN6CoreML13Specification23NeuralNetworkClassifierE__ZTVN6CoreML13Specification17OneHotLayerParamsE__ZTVN6CoreML13Specification17CumSumLayerParamsE__ZTVN6CoreML13Specification22NeuralNetworkRegressorE__ZTVN6CoreML13Specification23NetworkUpdateParametersE__ZTVN6CoreML13Specification9LossLayerE__ZTVN6CoreML13Specification32CategoricalCrossEntropyLossLayerE__ZTVN6CoreML13Specification25MeanSquaredErrorLossLayerE__ZTVN6CoreML13Specification9OptimizerE__ZTVN6CoreML13Specification12SGDOptimizerE__ZTVN6CoreML13Specification13AdamOptimizerE__ZN6CoreML13SpecificationL35SamePadding_SamePaddingMode_entriesE__ZN6CoreML13SpecificationL27SamplingMode_Method_entriesE__ZN6CoreML13SpecificationL38BoxCoordinatesMode_Coordinates_entriesE__ZN6CoreML13SpecificationL44Convolution3DLayerParams_PaddingType_entriesE__ZN6CoreML13SpecificationL38PoolingLayerParams_PoolingType_entriesE__ZN6CoreML13SpecificationL42Pooling3DLayerParams_PoolingType3D_entriesE__ZN6CoreML13SpecificationL49Pooling3DLayerParams_Pooling3DPaddingType_entriesE__ZN6CoreML13SpecificationL54GlobalPooling3DLayerParams_GlobalPoolingType3D_entriesE__ZN6CoreML13SpecificationL42UnaryFunctionLayerParams_Operation_entriesE__ZN6CoreML13SpecificationL45UpsampleLayerParams_InterpolationMode_entriesE__ZN6CoreML13SpecificationL46UpsampleLayerParams_LinearUpsampleMode_entriesE__ZN6CoreML13SpecificationL39FlattenLayerParams_FlattenOrder_entriesE__ZN6CoreML13SpecificationL39ReshapeLayerParams_ReshapeOrder_entriesE__ZN6CoreML13SpecificationL52ReorganizeDataLayerParams_ReorganizationType_entriesE__ZN6CoreML13SpecificationL34SliceLayerParams_SliceAxis_entriesE__ZN6CoreML13SpecificationL41ReduceLayerParams_ReduceOperation_entriesE__ZN6CoreML13SpecificationL36ReduceLayerParams_ReduceAxis_entriesE__ZN6CoreML13SpecificationL32GeluLayerParams_GeluMode_entriesE__ZN6CoreML13SpecificationL43NeuralNetworkMultiArrayShapeMapping_entriesE__ZN6CoreML13SpecificationL38NeuralNetworkImageShapeMapping_entriesE__ZN6CoreML13SpecificationL19ScatterMode_entriesE__ZTIN6CoreML13Specification13NeuralNetworkE__ZTIN6CoreML13Specification24NeuralNetworkImageScalerE__ZTIN6CoreML13Specification22NeuralNetworkMeanImageE__ZTIN6CoreML13Specification26NeuralNetworkPreprocessingE__ZTIN6CoreML13Specification14ActivationReLUE__ZTIN6CoreML13Specification19ActivationLeakyReLUE__ZTIN6CoreML13Specification14ActivationTanhE__ZTIN6CoreML13Specification20ActivationScaledTanhE__ZTIN6CoreML13Specification17ActivationSigmoidE__ZTIN6CoreML13Specification16ActivationLinearE__ZTIN6CoreML13Specification21ActivationSigmoidHardE__ZTIN6CoreML13Specification15ActivationPReLUE__ZTIN6CoreML13Specification13ActivationELUE__ZTIN6CoreML13Specification25ActivationThresholdedReLUE__ZTIN6CoreML13Specification18ActivationSoftsignE__ZTIN6CoreML1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