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ATfHnUSHPHHdH%(HD$81HGHD$HD$H"fHnfl)D$ H¹*Hl$1HHT$HHBHBpHH&ǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0[DHH56/HUOFHH5\܃6HFHuHH@ uHfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H谁H訁HD$ HtH|$HHD$8dH+%(uHHH[]A\A] I H!H!AUH ATfHnUSHPHHdH%(HD$81H7HD$HD$H"fHnfl)D$ H¹*Hl$1HHT$HHBHBpHH$ǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0ZDHH56-HUMFHH5\ڃ6FFHuHH@ uHfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A] IGHdH`AUH ATfHnUSHPHHdH%(HD$81H'HD$HD$H"fHnfl)D$ H¹*Hl$1HHT$HHBHBpHH"ǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07RDHH56+HUKFHH5f؃6DFHuHH@ uHfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H}H}HD$ HtH|$HHD$8dH+%(uHHH[]A\A] IHHAUH ATfHnUSHPHHdH%(HD$81HHD$HD$H"fHnfl)D$ H¹*Hl$1HHT$HHBHBpHH ǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0SDHH56)HUIFHH5fփ6BFHuHH@ uHfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H{H{HD$ HtH|$HHD$8dH+%(uHHH[]A\A] IHHAUH ATfHnUSHPHHdH%(HD$81HHD$HD$H"fHnfl)D$ H¹*Hl$1HHT$HHBHBpHHǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0wJDHH56'HUGFHH5fԃ6@FHuHH@ uHfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HyHyHD$ HtH|$HHD$8dH+%(uHHH[]A\A] I=HZHVAUH 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CHtLcHt$LfoT$ SHC(H5H5HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI*H*H*@AUH ATfHnUSHPHHdH%(HD$81H|HD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0-HH5vlH/HH5lv.H.`^H_nHHHP ^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H3H3HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI )H()H$)@AUH ATfHnUSHPHHdH%(HD$81H{HD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0-HH5vlH.HH5lv.H.^^H_nHHHP }^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H1H1HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI]'Hz'Hv'@AUH ATfHnUSHPHHdH%(HD$81HzHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0' -HH5vlH.HH5lv.H.\^H_nHHHP {^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H/H/HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI%H%H%@AUH ATfHnUSHPHHdH%(HD$81HyHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07-HH5vlH.HH5lv.H.Z^H_nHHHP y^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H-H-HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI$H$H$@AUH ATfHnUSHPHHdH%(HD$81HxHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5lv.H.X^HWpHHHP w^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H+H+HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HIS"Hp"Hl"@AUH ATfHnUSHPHHdH%(HD$81HwHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5 lv.H.V^HWpHHHP u^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H)H)HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI H H @AUH ATfHnUSHPHHdH%(HD$81HvHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0'-HH5vlH.HH5 lv.H.T^HWpHHHP s^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H'H'HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HIHH@AUH ATfHnUSHPHHdH%(HD$81HuHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH.HH5lv.H.R^HWpHHHP q^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H%H%HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HIIHfHb@AUH ATfHnUSHPHHdH%(HD$81HtHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5lv.H.P^HH@ o^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H#H#HD$ HtH|$HHD$8dH+%(uHHH[]A\A]LIHHAUH ATfHnUSHPHHdH%(HD$81HsHD$HD$HbfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5lv.H.N^HH@ m^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H!H!HD$ HtH|$HHD$8dH+%(uHHH[]A\A]LIHH AUH }ATfHnUSHPHHdH%(HD$81HrHD$HD$Hb}fHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH.HH5lv.H.L^HH@ k^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LICH`H\AUH {ATfHnUSHPHHdH%(HD$81HqHD$HD$Hb{fHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH.HH5lv.H.J^HH@ i^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LIHHAUH yATfHnUSHPHHdH%(HD$81HpHD$HD$HbyfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHHkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.H^H$nHHHP g^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HIHH@AUH wATfHnUSHPHHdH%(HD$81HoHD$HD$HbwfHnfl)D$ _H¹*Hl$1HHT$HHBHBpHH׾kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.F^H"nHHHP e^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HI5HRHN@AUH uATfHnUSHPHHdH%(HD$81HnHD$HD$HbufHnfl)D$ _~H¹*Hl$1HHT$HHBHBpHH׼kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH.HH5$kv.H.D^H nHHHP c^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HIHH@AUH sATfHnUSHPHHdH%(HD$81HmHD$HD$HbsfHnfl)D$ _|H¹*Hl$1HHT$HHBHBpHH׺kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH.HH5$kv.H.B^HnHHHP a^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]H}IHH@AUH qATfHnUSHPHHdH%(HD$81HlHD$HD$HbqfHnfl)D$ _zH¹*Hl$1HHT$HHBHBpHH׸kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.@^H8nHHHP _^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]H{I+HHHD@AUH oATfHnUSHPHHdH%(HD$81HkHD$HD$HbofHnfl)D$ _xH¹*Hl$1HHT$HHBHBpHH׶kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.>^H6nHHHP ]^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HyI} H H @AUH mATfHnUSHPHHdH%(HD$81HjHD$HD$HbmfHnfl)D$ _vH¹*Hl$1HHT$HHBHBpHH״kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH.HH5$kv.H.<^H4nHHHP [^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HwI H H @AUH kATfHnUSHPHHdH%(HD$81HiHD$HD$HbkfHnfl)D$ _tH¹*Hl$1HHT$HHBHBpHHײkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH.HH5$kv.H.:^H2nHHHP Y^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H H HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HuI! H> H: @AUH iATfHnUSHPHHdH%(HD$81HhHD$HD$HbifHnfl)D$ _rH¹*Hl$1HHT$HHBHBpHHװkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.8^H0nHHHP W^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H H HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HsIsHH@AUH gATfHnUSHPHHdH%(HD$81HgHD$HD$HbgfHnfl)D$ _pH¹*Hl$1HHT$HHBHBpHH׮kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.6^H.nHHHP U^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(H H HD$ HtH|$HHD$8dH+%(uHHH[]A\A]HqIHH@AUH eATfHnUSHPHHdH%(HD$81HfHD$HD$HbefHnfl)D$ _nH¹*Hl$1HHT$HHBHBpHH׬kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH.HH5$kv.H.4^H,nHHHP S^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HoIH4H0@AUH cATfHnUSHPHHdH%(HD$81HeHD$HD$HbcfHnfl)D$ _lH¹*Hl$1HHT$HHBHBpHHתkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH.HH5$kv.H.2^H*nHHHP Q^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HmIiHH@AUH aATfHnUSHPHHdH%(HD$81HdHD$HD$HbafHnfl)D$ _jH¹*Hl$1HHT$HHBHBpHHרkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0w,HH5vlH.HH5$kv.H.0^H(nHHHP O^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HkIHH@AUH _ATfHnUSHPHHdH%(HD$81HcHD$HD$Hb_fHnfl)D$ _hH¹*Hl$1HHT$HHBHBpHHצkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0g,HH5vlH.HH5$kv.H..^H&nHHHP M^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HiI H*H&@AUH ]ATfHnUSHPHHdH%(HD$81HbHD$HD$Hb]fHnfl)D$ _fH¹*Hl$1HHT$HHBHBpHHפkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.,^HWpHHHP K^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HgI_H|Hx@AUH [ATfHnUSHPHHdH%(HD$81HaHD$HD$Hb[fHnfl)D$ _dH¹*Hl$1HHT$HHBHBpHHעkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H.*^HWpHHHP I^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HeIHH@AUH YATfHnUSHPHHdH%(HD$81H`HD$HD$HbYfHnfl)D$ _bH¹*Hl$1HHT$HHBHBpHHנkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH.HH5$kv.H.(^HWpHHHP G^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HcIH H@AUH WATfHnUSHPHHdH%(HD$81H_HD$HD$HbWfHnfl)D$ _`H¹*Hl$1HHT$HHBHBpHHמkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH.HH5$kv.H.&^HWpHHHP E^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HaIUHrHn@AUH UATfHnUSHPHHdH%(HD$81H^HD$HD$HbUfHnfl)D$ _^H¹*Hl$1HHT$HHBHBpHHלkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0w,HH5vlH.HH5$kv.H.$^HWpHHHP C^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]H_IHH@AUH SATfHnUSHPHHdH%(HD$81H]HD$HD$HbSfHnfl)D$ _\H¹*Hl$1HHT$HHBHBpHHךkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0g,HH5vlH.HH5$kv.H."^HWpHHHP A^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]H]IHH@AUH QATfHnUSHPHHdH%(HD$81H\HD$HD$HbQfHnfl)D$ _ZH¹*Hl$1HHT$HHBHBpHHטkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH.HH5$kv.H. ^HvmHHHP ?^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]H[IKHhHd@AUH OATfHnUSHPHHdH%(HD$81H[HD$HD$HbOfHnfl)D$ _XH¹*Hl$1HHT$HHBHBpHHזkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0,HH5vlH蕿.HH5$kv.H.^HtmHHHP =^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HYIHH@AUH MATfHnUSHPHHdH%(HD$81HZHD$HD$HbMfHnfl)D$ _VH¹*Hl$1HHT$HHBHBpHHהkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH蕽.HH5$kv.H.^HrmHHHP ;^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HWIH H@AUH KATfHnUSHPHHdH%(HD$81HYHD$HD$HbKfHnfl)D$ _TH¹*Hl$1HHT$HHBHBpHHגkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH蕻.HH5$kv.H.^HpmHHHP 9^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HUIAH^HZ@AUH IATfHnUSHPHHdH%(HD$81HXHD$HD$HbIfHnfl)D$ _RH¹*Hl$1HHT$HHBHBpHHאkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0w,HH5vlH蕹.HH5$kv.H.^HnmHHHP 7^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HSIHH@AUH GATfHnUSHPHHdH%(HD$81HWHD$HD$HbGfHnfl)D$ _PH¹*Hl$1HHT$HHBHBpHH׎kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0g,HH5vlH蕷.HH5$kv.H.^HlmHHHP 5^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]HQIHH@AUH EATfHnUSHPHHdH%(HD$81HVHD$HD$HbEfHnfl)D$ _NH¹*Hl$1HHT$HHBHBpHH׌kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0觹,HH5vlH蕵.HH5$kv.H.^HH@ 3^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LOI;HXHTAUH CATfHnUSHPHHdH%(HD$81HUHD$HD$HbCfHnfl)D$ _LH¹*Hl$1HHT$HHBHBpHH׊kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0跶,HH5vlH蕳.HH5$kv.H.^HH@ 1^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LMIHHAUH AATfHnUSHPHHdH%(HD$81HTHD$HD$HbAfHnfl)D$ _JH¹*Hl$1HHT$HHBHBpHH׈kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H07,HH5vlH蕱.HH5$kv.H.^HH@ /^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LKIHHAUH ?ATfHnUSHPHHdH%(HD$81HSHD$HD$Hb?fHnfl)D$ _HH¹*Hl$1HHT$HHBHBpHH׆kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0',HH5vlH蕯.HH5$kv.H.^HH@ -^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LII1HNHJAUH =ATfHnUSHPHHdH%(HD$81HRHD$HD$Hb=fHnfl)D$ _FH¹*Hl$1HHT$HHBHBpHHׄkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0w,HH5vlH蕭.HH5$kv.H. ^HH@ +^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LGIHHAUH ;ATfHnUSHPHHdH%(HD$81HHD$HD$Hb;fHnfl)D$ _DH¹*Hl$1HHT$HHBHBpHHׂkǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0g,HH5vlH蕫.HH5$kv.H. ^HH@ )^HfH$HH$HCHHD$ H$HC CHtLcHt$LfoT$ SHC(HHHD$ HtH|$HHD$8dH+%(uHHH[]A\A]LEIHHAUH 9ATfHnUSHPHHdH%(HD$81HOHD$HD$Hb9fHnfl)D$ _BH¹*Hl$1HHT$HHBHBpHH׀kǂ?HB HB8HB(HBXHBHHHBhHHHHH(H0藮,HH5vlH蕩.HH5$kv.H.^HH@ '^HfH$HH$HCHHD$ H$HC 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b-@L2 b-@S b-@\3 bb}HQb-@Bb-@L2b-@Sb-@\3bb}HQb-@Bb-@L2b-@Sb-@\3HHIT$XHD$@HL$P>y5I HLL$b}Hb]HbQ=HbQHb}@b]@buHbUHbQ5HbQHbu@bU@bmHbMHbQ-HbQ Hbm@bM@O4IIHrbb}Hb"}H b"}H$IbB}H.b}H4b}Hempty()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtCustomOp*; _Alloc = std::allocator; reference = const OrtCustomOp*&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_vector.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtCustomOpDomain*; _Alloc = std::allocator; reference = OrtCustomOpDomain*&]basic_string: construction from null is not validbasic_string::_M_createlengths allocation failedstring buffer allocation failedOutput buffer allocation failedSynchronizeInputs/onnxruntime_src/onnxruntime/core/session/IOBinding.ccSynchronizeOutputs was false.Stacktrace: Trying to get a Tensor, but got: const T& OrtValue::Get() const [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/ort_value.hT* OrtValue::GetMutable() [with T = onnxruntime::Tensor]Trying to get a TensorSeq, but got: const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq]IsTensorSequence()Trying to get a SparseTensor, but got: const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor]IsSparseTensor()the ort_value must contain a constructed tensorInteger overflowstatic void SafeIntExceptionHandler::SafeIntOnOverflow()/onnxruntime_src/onnxruntime/core/common/safeint.hTensor sequence must contain only primitive typesvoid onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/TensorSeq.helem_type_ != nullptrconst OrtValue& onnxruntime::TensorSeq::GetAt(size_t) consti < tensors_.size()TensorSeq: tensor to be added has a different data type.void onnxruntime::TensorSeq::Add(const OrtValue&)IsSameDataType(tensor.Get())Attempt to use DefaultLogger but none has been registered.static void onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity(onnxruntime::logging::Severity)/onnxruntime_src/include/onnxruntime/core/common/logging/logging.hStrings can only reside in CPU memoryonnxruntime::SparseTensor& {anonymous}::ValidateFillInputArgs(OrtValue*, const onnxruntime::TensorShape&, const OrtMemoryInfo*)/onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cctried Filling sparse tensor with negative value in values shape!=T Tensor type mismatch. T* onnxruntime::Tensor::MutableData() [with T = std::__cxx11::basic_string]/onnxruntime_src/include/onnxruntime/core/framework/tensor.hutils::IsPrimitiveDataType(dtype_)input array doesn't equal tensor sizestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]The session already has a PrePackedWeightsContainer instancevector::_M_realloc_insertintra op thread pool must have at least one thread for RunAsyncRegisterCustomOpsUsingFunction: Registration function name must be specified.RegisterCustomOpsUsingFunction: Registration function was not foundtried creating tensor with negative value in shapenot enough space: expected FillSparseTensorCooget() != pointer()MakeCooDataUse MakeCooStrings/onnxruntime_src/onnxruntime/core/framework/sparse_tensor.ccOrtStatus* OrtApis::FillSparseTensorCoo(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IDataTransfer; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IDataTransfer&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/unique_ptr.hFillSparseTensorCsrMakeCsrDataUse MakeCsrStringsOrtStatus* OrtApis::FillSparseTensorCsr(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int64_t*, size_t)tried Filling sparse tensor with negative value in block sparse indices shapeOrtStatus* OrtApis::FillSparseTensorBlockSparse(OrtValue*, const OrtMemoryInfo*, const int64_t*, size_t, const void*, const int64_t*, size_t, const int32_t*)FillSparseTensorBlockSparseMakeBlockSparseDataUse MakeBlockSparseStringsUseCooIndicesSparse format must not be set. Already contains format: Not expecting an allocator setOrtStatus* OrtApis::UseCooIndices(OrtValue*, int64_t*, size_t)UseCsrIndicesThis method does not expect allocator to be setOrtStatus* OrtApis::UseCsrIndices(OrtValue*, int64_t*, size_t, int64_t*, size_t)UseBlockSparseIndicesOrtStatus* OrtApis::UseBlockSparseIndices(OrtValue*, const int64_t*, size_t, int32_t*)Can not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyPosixEnvonnxruntimecpuinfo_initialize failedRegisterCustomOps/onnxruntime_src/onnxruntime/core/platform/posix/env.ccRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in librarytypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IOBinding; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IOBinding&]ORT_LOAD_CONFIG_FROM_MODELopen failed. File doesn't existLoad model system error number status.IsOK()onnxruntime_profile_ORTMORTmodel_loading_arrayGiven model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const onnxruntime::PathString&)/onnxruntime_src/onnxruntime/core/session/inference_session.ccCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int)ModelProto 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_protoModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().__n < this->size()CopyOneInputAcrossDevicesthis->_M_is_engaged()BindInputSize mismatch: index= it.second=std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = OrtValue; _Alloc = std::allocator; reference = OrtValue&; size_type = long unsigned int]/onnxruntime_src/onnxruntime/core/framework/utils.ccconstexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/optionaltypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IStreamCommandHandleRegistry; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IStreamCommandHandleRegistry&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::Stream*; _Alloc = std::allocator; const_reference = onnxruntime::Stream* const&; size_type = long unsigned int]onnxruntime::common::Status onnxruntime::IOBinding::BindInput(const std::string&, const OrtValue&)mapped_feed_names_.size() == feed_names_.size()N11onnxruntime6StreamEN11onnxruntime11DummyStreamESt19_Sp_counted_deleterIPN11onnxruntime18IExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN6google8protobuf11MessageLiteEN11onnxruntime22IKernelTypeStrResolverEN4onnx10ModelProtoEN11onnxruntime21KernelTypeStrResolverEN11onnxruntime29OpSchemaKernelTypeStrResolverEN11onnxruntime16InferenceSessionEN11onnxruntime7EnvTimeE*N11onnxruntime12_GLOBAL__N_112PosixEnvTimeEN11onnxruntime3EnvEN11onnxruntime9TelemetryE*N11onnxruntime12_GLOBAL__N_18PosixEnvESt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime10IAllocatorESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime34IAllocatorImplWrappingOrtAllocatorESt23_Sp_counted_ptr_inplaceIN11onnxruntime34IAllocatorImplWrappingOrtAllocatorESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime23NotImplementedExceptionEN11onnxruntime20OnnxRuntimeExceptionEEnableOrtCustomOps: Custom operators in onnxruntime-extensions a are not enabledonnxruntime_profsession.load_modoad_model_format?1.17.1The 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: build type=Release, cmake cxx flags: -DNDEBUG -Wp,-D_FORTIFY_SOURCE=2 -Wp,-D_GLIBCXX_ASSERTIONS -fstack-protector-strong -O3 -pipe -fstack-clash-protection -fcf-protection -ffunction-sections -fdata-sections -Wno-restrict -DCPUINFO_SUPPORTEDindex is out of boundsoffsets buffer is not equal to tensor sizeoutput buffer is too small. Use GetStringTensorDataLength.buffer size is too small for string elementelement index is out of boundsout of indexinternal errorstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; const_reference = const onnxruntime::NodeArg* const&; size_type = long unsigned int]basic_string::appendstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::__cxx11::basic_string; _Alloc = std::allocator >; const_reference = const std::__cxx11::basic_string&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = long int; _Alloc = std::allocator; reference = long int&; size_type = long unsigned int]vector::reservestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long int; _Alloc = std::allocator; reference = long int&]basic_string::_M_replace_auxbasic_string::_M_replacemax_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkmax_power_of_two_extend_bytesInvalid key found: initial_growth_chunk_size_bytes name shorterMake the EP:onnxruntime::GetAvailableExecutionProviderNames()::/onnxruntime_src/onnxruntime/core/providers/get_execution_providers.ccprovider.name.size() <= kMaxExecutionProviderNameLenInvalid build with no providers availablethis API does not support stringslocation dimensions do not match shape sizeinvalid location rangegsl::span onnxruntime::Tensor::DataAsSpan() const [with T = std::__cxx11::basic_string]const T& OrtValue::Get() const [with T = std::map, std::__cxx11::basic_string >]onnxruntime::DataTypeImpl::GetType() == type_Invalid index requested for map type.const T& OrtValue::Get() const [with T = std::map, double>]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = double; _Alloc = std::allocator; reference = double&]const T& OrtValue::Get() const [with T = std::map >]const T& OrtValue::Get() const [with T = std::map]Tensor must always contain primitive types. Found: OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = std::__cxx11::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]len >= 0 && static_cast(len) < std::numeric_limits::max()OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]Value type is not supported yet: value_type != nullptrconst T& OrtValue::Get() const [with T = std::vector >]Map is missing type entry for its valuestatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h++index < c.size()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; const_reference = const onnxruntime::utils::data_types_internal::TypeNode&; size_type = long unsigned int]Sequence is missing type entry for its elementstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with T = std::map, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::vector, float> >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType > >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with T = std::map; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]Input is not of one of the supported sequence types.Input is not of type sequence or map.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = std::__cxx11::basic_string; V = std::__cxx11::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map, long int>]const T& OrtValue::Get() const [with T = std::map, float>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = std::__cxx11::basic_string; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]const T& OrtValue::Get() const [with T = std::map]Input is not of one of the supported map types.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)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.OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = std::__cxx11::basic_string; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue]OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::__cxx11::basic_string; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue]Key type is not supported yet.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.Expecting all elements to be tensors. Got: OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**)Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.in[idx]->IsTensor()Unsupported input typeopaque(ml_type != nullptrnon_tensor_base != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**)Opaque type is not a non_tensor type!!!OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t)static void OrtEnv::Release(OrtEnv*)/onnxruntime_src/onnxruntime/core/session/ort_env.cc/onnxruntime_src/onnxruntime/core/session/provider_bridge_ort.ccenv_ptr == p_instance_.get()Unload*ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_*ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_N11onnxruntime18IExecutionProviderEN11onnxruntime13NonTensorTypeISt3mapIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4lessIlESaISt4pairIKlS7_EEEEEN11onnxruntime7MapTypeISt3mapIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4lessIlESaISt4pairIKlS7_EEEEEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdSt4lessIS7_ESaISt4pairIKS7_dEEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdSt4lessIS7_ESaISt4pairIKS7_dEEEEESt19_Sp_counted_deleterIPvPFvS0_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN3gsl15narrowing_errorEN11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElSt4lessIS7_ESaISt4pairIKS7_lEEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElSt4lessIS7_ESaISt4pairIKS7_lEEEEEN11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEEN11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEEN11onnxruntime12DataTypeImplEN4onnx9TypeProtoEN11onnxruntime17NonTensorTypeBaseEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS7_ESaISt4pairIKS7_fEEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS7_ESaISt4pairIKS7_fEEEEEN11onnxruntime13NonTensorTypeISt6vectorISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS8_ESaISt4pairIKS8_fEEESaISF_EEEEN11onnxruntime12SequenceTypeISt6vectorISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfSt4lessIS8_ESaISt4pairIKS8_fEEESaISF_EEEETensorrtExecutionProviderCUDAExecutionProviderMIGraphXExecutionProviderROCMExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderTvmExecutionProviderVitisAIExecutionProviderQNNExecutionProviderNnapiExecutionProviderJsExecutionProviderCoreMLExecutionProviderArmNNExecutionProviderACLExecutionProviderDmlExecutionProviderRknpuExecutionProviderWebNNExecutionProviderXnnpackExecutionProviderCANNExecutionProviderAzureExecutionProviderCPUExecutionProviderSpecified provider is not supportypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Graph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Graph&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&]UnpackTensor: the pre-allocate size does not match the size in protodata overflowIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by CopyTensorCopySparseTensors/onnxruntime_src/onnxruntime/core/framework/data_transfer.ccunordered_map::atstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = float; _Alloc = std::allocator; reference = float&]__pos <= size()std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::operator[](size_type) [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; reference = char&; size_type = long unsigned int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/basic_string.h/build/Release/_deps/protobuf-src/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: vector::_M_range_insertconst T* onnxruntime::Tensor::Data() const [with T = std::__cxx11::basic_string]const T* onnxruntime::Tensor::Data() const [with T = long int]const T* onnxruntime::Tensor::Data() const [with T = float]const T* onnxruntime::Tensor::Data() const [with T = double]input array is too shortstatic const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger()Tensor type mismatch.type == dtype_void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constTensor size () != new size (void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&)shape_.Size() == new_shape.Size()T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] Max:node_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got:onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const/onnxruntime_src/include/onnxruntime/core/graph/graph.hint onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const/onnxruntime_src/onnxruntime/core/framework/node_index_info.hoffset >= 0 && static_cast(offset) < node_values_size_Please fetch output tensor with specified shape.T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor]/onnxruntime_src/include/onnxruntime/core/framework/op_kernel_context.hp_ml_valueonnxruntime::KernelDefBuilder& onnxruntime::KernelDefBuilder::VariadicAlias(int, int)/onnxruntime_src/onnxruntime/core/framework/kernel_def_builder.ccinput_offset >= 0 && output_offset >= 0Missing Input: const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::SparseTensor]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] is not present.Required input at index input_ptrconst T& onnxruntime::OpKernelContext::RequiredInput(int) const [with T = onnxruntime::Tensor]T* OrtValue::GetMutable() [with T = onnxruntime::SparseTensor]Required output at index output_ptronnxruntime::Tensor& onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape&)LoadDynamicLibraryFromProviderFailed to load dynamic library typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeArg; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeArg&]void onnxruntime::TensorSeq::Add(onnxruntime::Tensor&&)void onnxruntime::TensorSeq::Add(OrtValue&&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtValue; _Alloc = std::allocator; reference = OrtValue&]IsSameDataType(tensor)N4onnx13FunctionProtoEN11onnxruntime23NodeAttributes_IteratorEN11onnxruntime28NodeAttributes_Iterator_ImplEN11onnxruntime18Node__EdgeIteratorEN11onnxruntime23Node__EdgeIterator_ImplEN11onnxruntime18Node__NodeIteratorEN11onnxruntime23Node__NodeIterator_ImplEN11onnxruntime22SequenceTensorTypeBaseEN11onnxruntime16OptionalTypeBaseEN11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_9MLFloat16EEEN11onnxruntime18SequenceTensorTypeIjEEN11onnxruntime12OptionalTypeINS_9TensorSeqEjEEN11onnxruntime18SequenceTensorTypeImEEN11onnxruntime12OptionalTypeINS_9TensorSeqEmEEN11onnxruntime16SparseTensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime16SparseTensorTypeINS_10Float8E5M2EEEN11onnxruntime16SparseTensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime16SparseTensorTypeINS_12Float8E4M3FNEEEN11onnxruntime16SparseTensorTypeINS_9MLFloat16EEEN11onnxruntime16SparseTensorTypeINS_8BFloat16EEEN11onnxruntime16SparseTensorTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime16SparseTensorTypeIdEEN11onnxruntime16SparseTensorTypeIfEEN11onnxruntime16SparseTensorTypeImEEN11onnxruntime16SparseTensorTypeIlEEN11onnxruntime16SparseTensorTypeIjEEN11onnxruntime16SparseTensorTypeIiEEN11onnxruntime16SparseTensorTypeItEEN11onnxruntime16SparseTensorTypeIsEEN11onnxruntime16SparseTensorTypeIhEEN11onnxruntime16SparseTensorTypeIaEEN11onnxruntime20SparseTensorTypeBaseEN11onnxruntime16SparseTensorTypeIbEEN11onnxruntime10TensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime10TensorTypeINS_10Float8E5M2EEEN11onnxruntime10TensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime10TensorTypeINS_12Float8E4M3FNEEEN11onnxruntime10TensorTypeINS_9MLFloat16EEEN11onnxruntime10TensorTypeINS_8BFloat16EEEN11onnxruntime10TensorTypeIdEEN11onnxruntime10TensorTypeIfEEN11onnxruntime10TensorTypeImEEN11onnxruntime10TensorTypeIlEEN11onnxruntime10TensorTypeIjEEN11onnxruntime10TensorTypeIiEEN11onnxruntime10TensorTypeItEEN11onnxruntime10TensorTypeIsEEN11onnxruntime10TensorTypeIhEEN11onnxruntime10TensorTypeIaEEN11onnxruntime14TensorTypeBaseEN11onnxruntime10TensorTypeIbEEN11onnxruntime17PrimitiveDataTypeINS_14Float8E5M2FNUZEEEN11onnxruntime17PrimitiveDataTypeINS_10Float8E5M2EEEN11onnxruntime17PrimitiveDataTypeINS_14Float8E4M3FNUZEEEN11onnxruntime17PrimitiveDataTypeINS_12Float8E4M3FNEEEN11onnxruntime17PrimitiveDataTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEEN11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEEN11onnxruntime17PrimitiveDataTypeIdEEN11onnxruntime17PrimitiveDataTypeIfEEN11onnxruntime17PrimitiveDataTypeImEEN11onnxruntime17PrimitiveDataTypeIlEEN11onnxruntime17PrimitiveDataTypeIjEEN11onnxruntime17PrimitiveDataTypeIiEEN11onnxruntime17PrimitiveDataTypeItEEN11onnxruntime17PrimitiveDataTypeIsEEN11onnxruntime17PrimitiveDataTypeIhEEN11onnxruntime17PrimitiveDataTypeIaEEN11onnxruntime21PrimitiveDataTypeBaseEN11onnxruntime17PrimitiveDataTypeIbEESt23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime35TensorShapeProto_Dimension_IteratorEN11onnxruntime40TensorShapeProto_Dimension_Iterator_ImplEN4onnx11TensorProtoEN4onnx9NodeProtoEN6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io20ZeroCopyOutputStreamEN6google8protobuf2io26CopyingOutputStreamAdaptorEN6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamEN6google8protobuf2io19OstreamOutputStreamEN4onnx10GraphProtoENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEN4onnx14AttributeProtoEN4onnx18TypeProto_SequenceEN4onnx18TypeProto_OptionalEN4onnx22TypeProto_SparseTensorEN4onnx16TypeProto_TensorEN4onnx16TensorShapeProtoEN11onnxruntime12CPUAllocatorEN5Eigen19ThreadPoolInterfaceEN11onnxruntime9EnvThreadEN11onnxruntime11concurrency27ExtendedThreadPoolInterfaceE*N11onnxruntime12_GLOBAL__N_111PosixThreadEN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEEN11onnxruntime7logging11OStreamSinkEN11onnxruntime7logging5ISinkEN11onnxruntime7logging8CLogSinkEN11onnxruntime15UserLoggingSinkESt14default_deleteIN11onnxruntime18IExecutionProviderEEPFvPvESt8functionIFvPvEESt19_Sp_counted_deleterIPvSt8functionIFvS0_EESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_make_shared_tag00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899?80C?C CPU allocator not foundAttribute type not supported yet.Rd,Od\OdOdOdPdMdstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; const_reference = onnxruntime::NodeArg* const&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::common::Status::State; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::common::Status::State&]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; const_reference = const std::shared_ptr&; size_type = long unsigned int]SyncProvidersonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t, gsl::span)/onnxruntime_src/onnxruntime/core/framework/tensor.ccelt_type != nullptrT* onnxruntime::Tensor::MutableData() [with T = bool]T* onnxruntime::Tensor::MutableData() [with T = signed char]T* onnxruntime::Tensor::MutableData() [with T = unsigned char]T* onnxruntime::Tensor::MutableData() [with T = short int]T* onnxruntime::Tensor::MutableData() [with T = short unsigned int]T* onnxruntime::Tensor::MutableData() [with T = int]T* onnxruntime::Tensor::MutableData() [with T = unsigned int]T* onnxruntime::Tensor::MutableData() [with T = long int]T* onnxruntime::Tensor::MutableData() [with T = long unsigned int]T* onnxruntime::Tensor::MutableData() [with T = float]T* onnxruntime::Tensor::MutableData() [with T = double]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E4M3FN]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E4M3FNUZ]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E5M2]T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::Float8E5M2FNUZ]const T* onnxruntime::Tensor::Data() const [with T = bool]const T* onnxruntime::Tensor::Data() const [with T = signed char]const T* onnxruntime::Tensor::Data() const [with T = unsigned char]const T* onnxruntime::Tensor::Data() const [with T = short int]const T* onnxruntime::Tensor::Data() const [with T = short unsigned int]const T* onnxruntime::Tensor::Data() const [with T = int]const T* onnxruntime::Tensor::Data() const [with T = unsigned int]const T* onnxruntime::Tensor::Data() const [with T = long unsigned int]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E4M3FN]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E4M3FNUZ]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E5M2]const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::Float8E5M2FNUZ]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long int]const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor]invalid arg_num.virtual int onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputs(size_t) const/onnxruntime_src/onnxruntime/core/session/standalone_op_invoker.ccarg_num < static_cast(input_count_)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = OrtDevice; _Alloc = std::allocator; reference = OrtDevice&; size_type = long unsigned int]onnxruntime::common::Status onnxruntime::IOBinding::BindOutputImpl(const std::string&, const OrtValue&, OrtDevice)mapped_output_names_.size() == output_names_.size()Size mismatchCopy to device type: Unable to find a data transfer for copying from device type: libonnxruntime_providers_shared.sovoid onnxruntime::ProviderSharedLibrary::Ensure()onnxruntime::Provider& onnxruntime::ProviderLibrary::Get()EnsureProvider_SetHostGetGetProviderSessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared librarySessionOptionsAppendExecutionProvider_CANN: Failed to load shared librarySessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryTryGetProviderInfo_CUDATryGetProviderInfo_ROCMCUDA and/or ROCM execution provider is either not enabled or not available.device_typeenable_npu_fast_compiletruedevice_idnum_of_threadscache_direnable_opencl_throttlingdisable_dynamic_shapesnum_streamsSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryProvider options key/value cannot be emptyMaximum string length for a provider options key/value is 1024.SessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryDMLQNNOpenVINOSNPEXNNPACKWEBNNAZUREJSVitisAISessionOptionsAppendExecutionProvider/onnxruntime_src/onnxruntime/core/session/provider_registration.ccOrtStatus* OrtApis::SessionOptionsAppendExecutionProvider(OrtSessionOptions*, const char*, const char* const*, const char* const*, size_t)Unknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'TryGetProviderInfo_TensorRTThe custom op domain name OrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared libraryTensorRT Provider not available, can't get interface for itonnxruntime::ProviderInfo_TensorRT& onnxruntime::GetProviderInfo_TensorRT()AddTensorRTCustomOpDomainToSessionOption is already in session option.InvokeOpmatching node is missinginvalid node input count: , expect: invalid node output countstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; reference = onnxruntime::NodeArg*&]Kernel definition was not found for node Domain:'typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::KernelDef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::KernelDef&]kernel already mapped to existing nodestandalone_CreateOp' op_type:N11onnxruntime8FunctionEN11onnxruntime12FunctionImplEN11onnxruntime15OpKernelContextEN11onnxruntime10standalone23StandAloneKernelContextEN4onnx14ValueInfoProtoESt23_Sp_counted_ptr_inplaceISt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEN11onnxruntime11FuncManager8FuncInfoESt4hashIS6_ESt8equal_toIS6_ESaISt4pairIKS6_S9_EEESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime11ExLibLoaderEN11onnxruntime12ProviderHostEN11onnxruntime16ProviderHostImplEN11onnxruntime15ProviderHostCPUEN11onnxruntime19ProviderHostCPUImplETensorRT execution provider is not enabled in thd in this build.CUDA execution provider is not enabled in this bCANN execution poneDNN execution provider is not enabled in thisROCm execution pgraph_optimization_level is not validInvalid attribute.Attribute has no float value.Attribute has no int64 value.Unknown attribute type.Size of data not large enough to hold a float.Size of data not large enough to hold the array of floats.Size of data not large enough to hold an int64.Size of data not large enough to hold the array of int64.Size of data not large enough to hold the string.Size of data not large enough to hold the array of strings."e e e"e, e "e$!eNo requested allocator availableFailed to fetch a stream hosting the requested resourceAttribute does not exist.Output buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input name::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output nameAddInitializer/onnxruntime_src/onnxruntime/core/framework/session_options.ccAn OrtValue for this name has already been added: ::OrtKernelInfo cannot get a valid logger from its execution providerEnv is nullOrtMemoryInfo is nullProvided allocator is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Provided OrtMemoryInfo is nullNo allocator for this device has been registered for sharing.ComputeAsync`InlinedVector::at(size_type) const` failed bounds checkFailed to fetch type shape info for the index.Result buffer is not large enoughInvalid context::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a typeSession config entry '' was not found.Output buffer is not large enough for session config entryRegisterCustomOpsLibrary/onnxruntime_src/onnxruntime/core/session/abi_session_options.ccFailed to unload handle for dynamic library SetOutputTypeShapeInvalid shape info/onnxruntime_src/onnxruntime/core/session/custom_ops.ccsymbolic and integer dims mismatch!IsCompatibleinput count does not match input to be of optional type input to be of variadic type input to keep same arity input to be of single typeoutput count does not match output to keep same arity output to be of single typestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; const_reference = const onnx::OpSchema::FormalParameter&; size_type = long unsigned int]custom op schemas mismatch, expecting input to keep same homogeneity output to be of optional type output to be of variadic type output to keep same homogeneityInput index: contains null pointersExpecting same size spansinit_num == values.size()AddExternalInitializersonnxruntime::common::Status onnxruntime::SessionOptions::AddExternalInitializers(gsl::span >, gsl::span)Unsupported version '' in custom op 'onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&)Only the last i == count - 1Another version of operator ''has a different number of defined list of typesall types to a custom op may be marked variadic.onnxruntime::CreateSchema(const std::string&, const std::vector&)::. onnxruntime allows the overloading of an operator if all versions have the same number of declared static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o)) is not implemented in CreateAndRegisterAllocatorV2()No kernels to registers.ops.size() > 01 == undefinedonnx::OpSchema onnxruntime::CreateSchema(const std::string&, const std::vector&)custom op registered at runtimeThere 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.CreateCustomRegistryRegisterOpSetRegisterOpSchemaInternal (domain: ) from file , but it its domain is notSchema error: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::OpSchema; _Alloc = std::allocator; reference = onnx::OpSchema&]Domain already set in registry/onnxruntime_src/onnxruntime/core/graph/schema_registry.ccTrying to register schema with name , but it is already registered from file , but it its version is higherthan the operator set version *ZN7OrtApis25KernelContext_ParallelForEPK16OrtKernelContextPFvPvmEmmS3_EUllE0_FvRN4onnx16InferenceContextEEPFvRN4onnx16InferenceContextEEN4onnx8OpSchema20num_outputs_allowed_MUliE_EN4onnx8OpSchema19num_inputs_allowed_MUliE_EN4onnx15ISchemaRegistryEN11onnxruntime30IOnnxRuntimeOpSchemaCollectionEN11onnxruntime27OnnxRuntimeOpSchemaRegistryESt23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIvELN9__gnu_cxx12_Lock_policyE2EE*ZN11onnxruntime22CreateKernelCreateInfoERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPK11OrtCustomOpEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISH_EEE_N11onnxruntime14CustomOpKernelEN11onnxruntime8OpKernelEN3Ort9ExceptionE12OrtAllocatorN11onnxruntime16OrtAllocatorImplEN11onnxruntime34OrtAllocatorImplWrappingIAllocatorESt23_Sp_counted_ptr_inplaceIN11onnxruntime14LibraryHandlesESaIvELN9__gnu_cxx12_Lock_policyE2EEexecution_mode iode is not validCpustd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::DataTypeImpl*; _Alloc = std::allocator; const_reference = const onnxruntime::DataTypeImpl* const&; size_type = long unsigned int]tensor(uint8)tensor(uint16)tensor(int8)tensor(int16)tensor(bfloat16)tensor(float16)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)) Declared=[ShapeInferenceError] Mismatch between inferred and declared type. Inferred=Unsupported Source/Target type=qTGfqTGqTGfhfqTGqTGffshape inference not yet supported for non-tensor typesOrtShapeInferContext::OrtShapeInferContext(onnx::InferenceContext&)value_case == ONNX_NAMESPACE::TypeProto::kTensorTypestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::__cxx11::basic_string; _Alloc = std::allocator >; reference = std::__cxx11::basic_string&]consumed_inputsAbsArgMaxArgMinCeilconcat_resultDivFloorGlobalLpPoolHardmaxLogSoftmaxdata_0MaxRoiPoolminNegReciprocalReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMinReduceProdReduceSumReduceSumSquareSqrtSublegacy optimization attribute.Constrain input and output types to float tensors./build/Release/_deps/onnx-src/onnx/defs/math/old.cc/build/Release/_deps/onnx-src/onnx/defs/reduction/old.cc/build/Release/_deps/onnx-src/onnx/defs/nn/old.ccWhich axis to concat on. Default value is 1.Constrain output types to float tensors./build/Release/_deps/onnx-src/onnx/defs/tensor/old.ccp value of the Lp norm used to pool over the input data, default is 2.0./build/Release/_deps/onnx-src/onnx/defs/nn/defs.ccminus infinity (if supported by the datatype) or undefined otherwiseminus infinity (if supported by the datatype) or the minimum value of the data type otherwiseplus infinity (if supported by the datatype) or the maximum value of the data type otherwiseCoefficient of leakage.slopePReluConstrain input and output types to signed numeric tensors.AcosT1AsinAtanCosThe ratio of random dropouttransAtransBGreateractivationslinear_before_resetGRULessSinTanNumber of times to sample.sample_sizeseeddtypeT2Multinomial/build/Release/_deps/onnx-src/onnx/defs/math/defs.ccConstrain input to boolean tensor.Constrain output to boolean tensor./build/Release/_deps/onnx-src/onnx/defs/logical/defs.ccConstrain input to integral tensors./build/Release/_deps/onnx-src/onnx/defs/logical/old.ccWhether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Constrain input to float tensors.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. 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 A 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./build/Release/_deps/onnx-src/onnx/defs/rnn/old.cc(Optional) Seed to the random generator, if not specified we will auto generate one.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Constrain input types to float tensors.Constrain output types to integral tensors./build/Release/_deps/onnx-src/onnx/defs/generator/defs.ccpadsWhich axis to split on. length of each outputList 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`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled.Constrain input and output types to all tensor types.Which axis to concat onreshapedConstrain input and output types to all tensors.The 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./build/Release/_deps/onnx-src/onnx/defs/controlflow/old.ccExpandsequence_lensinitial_state_and_scan_inputsfinal_state_and_scan_outputsnum_scan_inputsInt64 tensorScanN4onnx13TypeProto_MapE22OrtDefaultCpuAllocatorN4onnx14InferenceErrorE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZN7OrtApis25KernelContext_ParallelForEPK16OrtKernelContextPFvPvmEmmS7_EUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime20CreateCustomRegistryEN3gsl4spanIKP17OrtCustomOpDomainLm18446744073709551615EEERSt10shared_ptrINS_14CustomRegistryEEEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime12CreateSchemaERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKSt6vectorIPK11OrtCustomOpSaISB_EEEUlRN4onnx16InferenceContextEE0_TVConstrain output types to any teany tensor type.Constrain input and output types to all tensor tAll Tensor types(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.(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 float32Constrain output types to be numerics./build/Release/_deps/onnx-src/onnx/defs/generator/old.cc(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'.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Constrain input types to all numeric tensors.The value for the elements of the output tensor.Constrain input and output types to float/int tensors.Constrain output types to boolean tensors.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported.Constrain to all tensor types.conditionConstrain to boolean tensors.CompressConstrain input types.ConstantOfShapeEyeLikeConstantSinhCoshAsinhAcoshAtanhIsNaNNonZeronearestscalescase_change_actionis_case_sensitivestopwordslocaleStringNormalizerValuesTopKfmodModThreshold valueThresholdedRelub_zero_pointT3MatMulIntegery_scaley_zero_pointx_scalex_zero_pointDequantizeLineardetect_positivedetect_negativeIsInfmax_output_boxes_per_classiou_thresholdscore_thresholdselected_indicescenter_point_boxNonMaxSuppressionbatch_axisReverseSequenceTwo interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)Constrain input 'X' and output 'Y' to all tensor types./build/Release/_deps/onnx-src/onnx/defs/tensor/defs.ccstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"List 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./build/Release/_deps/onnx-src/onnx/defs/text/defs.ccConstrain index tensor to int64Dimension on which to do the sort.Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to high-precision numeric tensors. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } Constrain output mask types to boolean tensors.Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./build/Release/_deps/onnx-src/onnx/defs/quantization/old.ccConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.(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.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./build/Release/_deps/onnx-src/onnx/defs/object_detection/old.cc(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.Input and output types can be of any tensor type.BitShiftRoundDetConstrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to floating-point tensors.Constrain input and output types to all numeric tensors.Constrain input and output types to all numerical tensor types.The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Constrain input and output types to any tensor type.Type 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 typesConstrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.Constrain input and output types to numeric tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } 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.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Einsum expression string.equationEinsumbatch_dimsGatherNDTindlossreductionNegativeLogLikelihoodLosstraining_modeCeluLessOrEqualGreaterOrEquallabelslog_probSoftmaxCrossEntropyLossPowsum squareproductlog sumlog sum exponentL1 normL2 norm/build/Release/_deps/onnx-src/onnx/defs/reduction/defs.ccIf 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.axis tensor can be int32 or int64 only { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } (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./build/Release/_deps/onnx-src/onnx/defs/rnn/defs.ccA 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.The 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].Couple the input and forget gates if 1.One (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.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } Constrain input and output types to all tensor and sequence types.exclusivereverseCumSumallowzerolayoutinput_forgetinitial_cY_cRNNupperTriluHardSwishBoolean. Whether the identification of stop words in X is case-sensitive. DefaulDefault is falseThe data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.Constrain output types to all numeric tensors and bool tensors.Type of the element in the optional outputConstrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types./build/Release/_deps/onnx-src/onnx/defs/optional/defs.ccConstrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor./build/Release/_deps/onnx-src/onnx/defs/optional/old.ccConstrain output type to all tensor or sequence types.(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.Constrain output to int64 tensor.BernoulliOptionalOptionalHasElementOptionalGetElementtarget_typeCastLikestartMultiplicative 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).coordinate_transformation_modeConstrain types to float tensors.Constrain types to int tensors./build/Release/_deps/onnx-src/onnx/defs/object_detection/defs.ccType of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Constrain indices to integer typesGraph 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 IRv4.Constrain input and output types to all tensor types (including bfloat). { 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) } { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } spatial_scaleoutput_heightoutput_widthsampling_ratioroisbatch_indicesRoiAlignupdatesScatterNDnoneScatterElementsthen_branchelse_branchOnly boolIfThe first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.The epsilon value to use to avoid division by zero.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Constrain input types and output Y type to float tensors.Type of Mean and InvStdDev tensors.The 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.Constrain input types to any sequence type.Constrain to any tensor or sequence type./build/Release/_deps/onnx-src/onnx/defs/sequence/defs.ccIf 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.Constrain scalar length types to int64_t.Constrain 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) } { 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) } { 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. 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.Constrain signal and output to float tensors.epsilonstash_typeInvStdDevinput_sequenceadditional_inputsout_sequenceSequenceMaponesidedinversedft_lengthDFTHannHannWindowHammingHammingWindowBlackmanBlackmanWindowoutput_datatypenum_mel_binssample_ratelower_edge_hertzupper_edge_hertzConstrain to integer tensors.Constrain to float tensorsMelWeightMatrixsignalframe_stepwindowframe_lengthSTFT 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], ] ``` Supported modes: `constant`(default), `reflect`, `edge`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.Constrain input X and output types to float tensors. { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Which 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. { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { 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) } { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { 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) } Constrain input to integer tensors.Constrain input/output to integer tensors.The number of groups of channels. It should be a divisor of the number of channels `C`.input_dataoutput_dataCenterCropPadMishnum_outputsBitwiseAndBitwiseOrBitwiseXorBitwiseNotnum_groupsGroupNormalizationConstrain input types to all (non-complex) tensors.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 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. Please refer to operator Cast description for further details.Dilation 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.Number 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.(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./build/Release/_deps/onnx-src/onnx/defs/quantization/defs.ccAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9./build/Release/_deps/onnx-src/onnx/defs/controlflow/defs.cc 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], ] ``` Supported modes: `constant`(default), `reflect`, `edge`, `wrap`(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.Constrain output to int64 tensor, which should be a scalar though.saturatedilationskernel_shapeoffset_groupstridesDeformConvSizeif 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.Constrain grid types to float tensors.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) } > } If `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`.Constrain scalar length types to integers.Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.Pixel format. Can be one of "RGB", "BGR", or "Grayscale".Constrain input types to 8-bit unsigned integer tensor.Constrain output types to 8-bit unsigned integer tensor./build/Release/_deps/onnx-src/onnx/defs/image/defs.ccRegex pattern to match on. This must be valid RE2 syntax.Outputs are bools and are True where there is a full regex match and False otherwise.Inputs and outputs must be UTF-8 stringsDelimiter 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.The input must be a UTF-8 string tensorThe number of substrings generated.align_cornersthetagridAffineGridapproximateRGBpixel_formatencoded_streamimageImageDecoderpatternInputs must be UTF-8 stringsRegexFullMatchStringConcatdelimitermaxsplitTensor of substrings.StringSplitThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./build/Release/_deps/onnx-src/onnx/defs/traditionalml/defs.ccThe 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 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.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.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.The input type must be a tensor of a numeric type.The size of each input in the input listThe 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.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./build/Release/_deps/onnx-src/onnx/defs/traditionalml/old.ccThe 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).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'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 intercepts, if used.The total number of regression targets, 1 if not defined.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.The input type must be a tensor of a numeric type, either [C] or [N,C].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.Flag indicating whether the regression is a one-class SVM or not.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'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'Node id for each node. Node ids must restart at zero for each tree and increase sequentially.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 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.nodes_missing_value_tracks_trueThe id of the tree that each node is in.The index of the target that each weight is forIndicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'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)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.ArrayFeatureExtractorBinarizermap(int64, float)map(int64, string)TO_FLOATcast_toDENSEmap_formmax_mapCastMapcats_stringscats_int64s_Unuseddefault_stringdefault_int64CategoryMappermap(string, int64)map(int64, double)map(string, float)map(string, double)string_vocabularyint64_vocabularyDictVectorizerinputdimensionsFeatureVectorizerValue(s) to change toimputed_value_floatsA value that needs replacing.replaced_value_floatValue(s) to change to.imputed_value_int64sreplaced_value_int64ImputerA list of labels.classes_stringsLabelEncodercoefficientsA collection of intercepts.interceptsmulti_classclasslabels_stringsclasslabels_intsNONEpost_transformLinearClassifierWeights of the model(s).LinearRegressorOne of 'MAX,' 'L1,' 'L2'zerosOneHotEncoderLINEARkernel_typekernel_paramsChosen support vectorssupport_vectorsone_classSupport vector coefficients.n_supportsrhoSVMRegressorTree id for each node.nodes_treeidsnodes_nodeidsFeature id for each node.nodes_featureidsnodes_valuesnodes_hitratesnodes_modesnodes_truenodeidsnodes_falsenodeidstarget_treeidsThe node id of each weighttarget_nodeidstarget_idsThe weight for each targettarget_weightsThe total number of targets.n_targetsaggregate_functionbase_valuesTreeEnsembleRegressorseq(map(string, float))seq(map(int64, float))classlabels_int64sZipMapInput tensor can be of arbitraryInput and output types can be of any tensor type to all tensor, sequence, and op optional types.Constrain to any numerical typesstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; const_reference = const onnxruntime::AllocPlanPerValue&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator; reference = onnxruntime::AllocPlanPerValue&; size_type = long unsigned int]An 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.Device:[DeviceType: MemoryType: DeviceId:OrtMemoryInfo:[ id: OrtMemType: OrtAllocatorType:std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::shared_ptr; _Alloc = std::allocator >; reference = std::shared_ptr&; size_type = long unsigned int]Could not find OrtValue with name 'NhwcConvadditionmultiplicationreducedkeepdimsReduceSumIntegerBiasGeluBiasSplitGeluskipBiasAddis_inner_broadcastBiasSoftmaxsqeuclideanCDistComplexMulComplexMulConjInversesignal_ndimnormalizedIrfftThe NGram size.Tidscores_outNGramRepeatBlocktoken_offsetRestorePaddingRfftConstrain to float tensors.Constrain to integer types.DynamicTimeWarpingconv_window_sizechar_embedding_sizeConstrain to tensor(int32).Constrain to tensor(float).WordConvEmbeddingGemmFastGelu/onnxruntime_src/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.ccC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_point/onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.ccC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointConstrain 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./onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.ccConstrain input X and output Y types to float tensors./onnxruntime_src/onnxruntime/core/graph/contrib_ops/diffusion_defs.ccapply 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 - 1The 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".Constrains input to only numeric types.Constrain input and output types to float or half tensors.number of dimensions comprising the signalmust be 0, normalization currently not supportedmust be 1, only one sided FFTs supportedConstrain scores input and output types to float tensors./onnxruntime_src/onnxruntime/core/graph/contrib_ops/bert_defs.ccConstrain token_offset to integer typesnumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)Integer 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.Value of alphaValue of betaAffineParametricSoftplusThe scale to apply.ImageScalerborderCropstartsendsDynamicSliceinput_as_shapeextra_shapeGivenTensorFilldrop_stateshidden_prevgatesseq_lengthshiddenGRUUnitacross_channelsnormalize_varianceMeanVarianceNormalizationScaling valueScaledTanh/onnxruntime_src/onnxruntime/core/graph/contrib_ops/onnx_deprecated_operators.ccBias applied to each channel, same size as C.A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.seq(tensor(bfloat16))MemcpyFromHostMemcpyToHostException caught: /onnxruntime_src/onnxruntime/core/session/environment.ccConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.Provided type is not an optional tensorconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalTensor(onnxruntime::MLDataType)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.hIsOptionalTensor(type)N11onnxruntime16GraphTransformerESt18bad_variant_accessN4onnx11SchemaErrorEZN4onnx19RegisterOpSetSchemaIN11onnxruntime18internal_nhwc_onnx24OpSet_Internal_NHWC_ONNXEEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime7contrib21OpSet_ONNX_DeprecatedEEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaIN11onnxruntime7contrib20OpSet_Microsoft_ver1EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver4EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver3EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver2EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver1EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver20EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver19EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver18EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver17EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver16EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver15EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver14EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver13EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver12EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver11EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver10EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver9EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver8EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver7EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver6EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver5EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver4EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver3EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver2EEEvibEUlONS_8OpSchemaEE_ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver1EEEvibEUlONS_8OpSchemaEE_*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE_*ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_[json.exception.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Node; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Node&]AddCustomOpDomainsReceived nullptr for custom registrytypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SessionState; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SessionState&]Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: onnxruntime::logging::Severity onnxruntime::GetSeverity(const SessionOptions&)session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)LoadOrtModelBytesLoad model from failed. Only bytes were able to be read.Sequential modeParallel mode are '0' and '1'. FinalizeSessionOptionsis_model_proto_parsedort_configoperator()session_optionsintra_op_num_threadsSetIntraOpNumThreadsinter_op_num_threadsSetInterOpNumThreadsexecution_modeSetExecutionModeSetting execution_mode to graph_optimization_levelSetGraphOptimizationLevelenable_profilingSetEnableProfilingSetting enable_profiling to The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT configModelProto needs to be parsed to check for ORT config within itonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const SessionOptions&, const onnx::ModelProto&, bool, SessionOptions&)ParseOrtConfigJsonInModelProto/onnxruntime_src/onnxruntime/core/session/inference_session_utils.ccFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: 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 modelcannot compare iterators of different containersintra_op_num_threads option in the model file must be an integerUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to inter_op_num_threads option in the model file must be an integerUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integerUnsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integerSetting 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: enable_profiling option in the model file must be an integerUnsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: LoadWithLoader_M_get() != nullptrException during loading: Unknown exceptionThis session already contains a loaded model.std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::Model; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::Model]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/shared_ptr_base.hEncountered unknown exception in LoadWithLoader()offFlush-to-zero and denormal-as-zero are Could not finalize session options while constructing the inference session. Error Message: void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&) max_num_graph_transformation_steps:Creating and using per session threadpools since use_per_session_threads_ is truesession.intra_op.allow_spinningsession.intra_op_thread_affinitiesAffinity string must not be emptycustom join thread function not set for intra op thread poolsession.inter_op.allow_spinningcustom 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 falseWhen the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.session_env.EnvCreatedWithGlobalThreadPools()void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*)/onnxruntime_src/onnxruntime/core/common/profiler.ccTraceSessionOptionsSession Options { execution_mode: execution_order:DEFAULTPRIORITY_BASEDUNKNOWN 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: graph_optimization_level: intra_op_param: inter_op_param: use_per_session_threads: thread_pool_allow_spinning: use_deterministic_compute: config_options: { }session.set_denormal_as_zerosession.force_spinning_stopConstructorCommonsession--intra-opsession.dynamic_block_baseDynamic block base set to !to.affinity_str.empty()to.custom_join_thread_fn-inter-opsession_logger != nullptr%Y-%m-%d_%H-%M-%S.jsonError during EndProfiling(): ~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.Having 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 std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]/onnxruntime_src/onnxruntime/core/framework/execution_providers.hRegisterExecutionProvider has already been registered.Provider SaveToOrtFormatRegisterCustomOpNodeSchemasstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]/onnxruntime_src/onnxruntime/core/graph/model.ccUnable to find schema for node. Domain:'Failed to save ORT format model to file: Failed to load model because protobuf parsing failed.session.strict_shape_type_inferencesession.disable_aot_function_inlining/onnxruntime_src/onnxruntime/core/framework/graph_partitioner.ccThis model does not have any local functions defined. AOT Inlining is not performedsession.debug_layout_transformationInlineFunctionsAOTAOT inlining completed. () functions of () pruned.TransformGraphsession.disable_quant_qdqCastFloat16TransformerMemcpyTransformerLoadOrtModelWithLoaderLoadOpsetImportOrtFormatai.onnxLoadFromOrtFormatThis 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 Missing Model. Invalid ORT format model.session.use_ort_model_bytes_for_initializers/onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.ccModel must have opset imports. Invalid ORT format model.opset import domain is null. Invalid ORT format model.Graph is null. Invalid ORT format model.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Model; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Model&]/onnxruntime_src/onnxruntime/core/graph/graph.ccmodel_loading_uri failed:N11onnxruntime21SchemaRegistryManagerESt23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime17MemcpyTransformerEN11onnxruntime21InsertCastTransformerEN11onnxruntime25EnsureUniqueDQForNodeUnitEN6google8protobuf14FatalExceptionEN8nlohmann6detail16invalid_iteratorEN8nlohmann6detail9exceptionE@@BAddPredefinedTransformersThis transformer is already registered Unexpected data type. Actual: ()) , expected: (Unsupported 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: cpugpuvector::_M_default_appendfeedfetch names has elements, but elements.Invalid with name: '' expected to be of type: but received a tensorValidateInputsOutputssparse_tensor but received a sparse tensorIsOptionalSeqTensor(type)seq but received a tensor sequenceProvided type is not an optional sequence tensorconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetElementTypeFromOptionalSeqTensor(onnxruntime::MLDataType)ParseStringWithClassicLocaleFailed to parse value: "/onnxruntime_src/include/onnxruntime/core/common/parse_string.hT onnxruntime::ParseStringWithClassicLocale(std::string_view) [with T = long unsigned int; std::string_view = std::basic_string_view]Replaying the captured IsGraphCaptured()Session was not initializedSession not initialized.Invalid output name:FeedsFetchesInfoRunning with tag: ExecuteGraphShrinkMemoryArenasUnable to shrink arena: error message: model_run CUDA Graph for this model with tag: onnxruntime::common::Status onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraph()/onnxruntime_src/onnxruntime/core/session/inference_session.hCached EP instance for graph replay is not set yet before calling ReplayGraph()At least one output should be requested.memory.enable_memory_arena_shrinkage/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.honnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = OrtDevice; _Alloc = std::allocator; const_reference = const OrtDevice&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; reference = onnxruntime::MLValueCopyInfo&; size_type = long unsigned int]Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions&, std::unique_ptr&)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)cannot create std::vector larger than max_size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const OrtDevice*; _Alloc = std::allocator; reference = const OrtDevice*&; size_type = long unsigned int]disable_synchronize_execution_providersStart another run for necessary memory allocation or graph capture.Encountered unknown exception in Run()input name cannot be emptyoutput name cannot be emptyNULL input supplied for input onnxruntime::common::Status onnxruntime::InferenceSession::Run(const onnxruntime::RunOptions&, gsl::span, gsl::span, gsl::span, gsl::span)fetch_unique_ptrs[i] != nullptrGetModelMetadataModel was not loadedModel was not loaded.GetModelInputsGetOverridableInitializersGetModelOutputsEndProfiling[ ] {"cat" : ""pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : } }, Profiler is disabled.Writing profiler data to file Could not write a profile because no model was loaded.NewIOBindingInitializeInitializing session.session.use_env_allocatorssession.save_model_formatsaveapplyInvalid value for PartitionOrtFormatModelFinalizeSessionStateNchwcTransformer1024session_initializationSession has already been initialized.Adding default CPU execution provider.InjectExternalInitializedTensorsReplaced external initializer: This session will use the allocator registered with the environment.optimization.minimal_build_optimizations value of 'save' is only valid when saving an ORT format model.This session cannot use the CUDA Graph feature as requested by the user 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.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.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.ApplyOrtFormatModelRuntimeOptimizations/onnxruntime_src/onnxruntime/core/framework/session_state.ccUnable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Serializing 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_bytesSession successfully initialized.Exception during initialization: Encountered unknown exception in Initialize()unknown exceptionLoad*ZN11onnxruntime16InferenceSession8RunAsyncEPK13OrtRunOptionsN3gsl4spanIKPKcLm18446744073709551615EEENS5_IKPK8OrtValueLm18446744073709551615EEES9_NS5_IPSA_Lm18446744073709551615EEEPFvPvPSF_mP9OrtStatusESH_EUlvE_*ZN11onnxruntime16InferenceSession10InitializeEvEUlRKN4onnx8OpSchemaEE0_*ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_*ZN11onnxruntime16InferenceSession12LoadOrtModelERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEUlvE_*ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_*ZN11onnxruntime16InferenceSession13LoadOnnxModelERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEUlRSt10shared_ptrINS_5ModelEEE_St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime20CPUExecutionProviderESt19_Sp_counted_deleterIPN11onnxruntime20CPUExecutionProviderESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEApi01session.strict_shape_type_inferesession.allow_released_opsets_onCPUExecutionProvsession.use_ort_model_bytes_direstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = char; _Alloc = std::allocator; reference = char&]invalid string: ill-formed UTF-8 byteHjxjjjhjHjHjXjjobjectbooleanbinarydiscardednumbercannot use at() with key '' not found G G G Gw GR GR GR G G Gtrue literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','unknown tokenend of input'[', '{', or a literalsyntax error while parsing - ; last read: 'unexpected ; expected jܲjLj::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>; reference = nlohmann::basic_json<>*&]type must be string, but is SGGG;G/G#GGGGGGvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = char; _Alloc = std::allocator]invalid number; expected digit after '.'invalid number; expected digit after '-'invalid number; expected digit after exponent signinvalid number; expected '+', '-', or digit after exponentinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid 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 literaljj::pop_back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator*>]std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(iterator) [with _Key = std::__cxx11::basic_string; _Val = std::pair, nlohmann::basic_json<> >; _KeyOfValue = std::_Select1st, nlohmann::basic_json<> > >; _Compare = std::less; _Alloc = std::allocator, nlohmann::basic_json<> > >; iterator = std::_Rb_tree, std::pair, nlohmann::basic_json<> >, std::_Select1st, nlohmann::basic_json<> > >, std::less, std::allocator, nlohmann::basic_json<> > > >::iterator]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_tree.hvoid std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; reference = nlohmann::basic_json<>&]object keyobject separatornumber overflow parsing 'jjxjjjjjjHjjjjjjjpost_layout_transform_step_onnxruntime::InferenceSession::TransformGraph(onnxruntime::Graph&, bool):: mutablesession.qdqisint8allowedQDQFinalCleanupTransformersession.enable_quant_qdq_cleanupUnsupported optimization level: onnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::SatApplyContextVariant&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cctuning_resultstype must be array, but is . Ignoring...ParseTuningResultsFromModelMetadataFound tuning results in the model file to be used while loading the modelTuning results stored in the model file cannot be parsed. Error message: GG=G1GIG~G~G~G%GGApplyGraphTransformer modified: with status: /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.ccApplyTransformers/onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.ccDoubleQDQPairsRemoverConstantSharingConstantFoldingMatMulAddFusionReshapeFusionQDQPropagationTransformerRocmBlasAltImplTransposeOptimizerTransposeOptimizer_session.x64quantprecisionQDQS8ToU8TransformerGemmActivationFusionMatMulIntegerToFloatFusionDynamicQuantizeMatMulFusionSimplifiedLayerNormFusionAttentionFusionEmbedLayerNormFusionGatherToSplitFusionGatherToSliceFusionMatmulTransposeFusionBiasGeluFusionSkipLayerNormFusionFastGeluFusionQuickGeluFusionGeluApproximationBiasSoftmaxFusionBiasDropoutFusionMatMulScaleFusionMatMulActivationFusionAvx2WeightS8ToU8TransformerConvAddActivationFusionsession.disable_double_qdq_removerCommonSubexpressionEliminationoptimization.enable_gelu_approximationonnxruntime::InlinedVector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const onnxruntime::InlinedHashSet >&)N11onnxruntime15NhwcTransformerEN11onnxruntime20ConvActivationFusionEN11onnxruntime28QDQSelectorActionTransformerEN11onnxruntime32FreeDimensionOverrideTransformerEN11onnxruntime25RuleBasedGraphTransformerEN11onnxruntime11RewriteRuleEN11onnxruntime25SelectorActionTransformerEN11onnxruntime21DoubleQDQPairsRemoverEN11onnxruntime15ConstantSharingEN11onnxruntime15ConstantFoldingEN11onnxruntime15MatMulAddFusionEN11onnxruntime13ReshapeFusionEN11onnxruntime25QDQPropagationTransformerEN11onnxruntime15RocmBlasAltImplEN11onnxruntime18TransposeOptimizerEN11onnxruntime20QDQS8ToU8TransformerEN11onnxruntime20GemmActivationFusionEN11onnxruntime26MatMulIntegerToFloatFusionEN11onnxruntime27DynamicQuantizeMatMulFusionEN11onnxruntime10GeluFusionEN11onnxruntime15LayerNormFusionEN11onnxruntime25SimplifiedLayerNormFusionEN11onnxruntime15AttentionFusionEN11onnxruntime20EmbedLayerNormFusionEN11onnxruntime19GatherToSplitFusionEN11onnxruntime19GatherToSliceFusionEN11onnxruntime21MatmulTransposeFusionEN11onnxruntime14BiasGeluFusionEN11onnxruntime19SkipLayerNormFusionEN11onnxruntime14FastGeluFusionEN11onnxruntime15QuickGeluFusionEN11onnxruntime17GeluApproximationEN11onnxruntime17BiasSoftmaxFusionEN11onnxruntime17BiasDropoutFusionEN11onnxruntime17MatMulScaleFusionEN11onnxruntime22MatMulActivationFusionEN11onnxruntime27Avx2WeightS8ToU8TransformerEN11onnxruntime26QDQFinalCleanupTransformerEN11onnxruntime16NchwcTransformerEN11onnxruntime23ConvAddActivationFusionEN11onnxruntime30CommonSubexpressionEliminationEN8nlohmann6detail11parse_errorEN8nlohmann6detail12out_of_rangeEN8nlohmann6detail10type_errorESt14default_deleteIN11onnxruntime20CPUExecutionProviderEESt14default_deleteIN11onnxruntime5ModelEE*ZN11onnxruntime12_GLOBAL__N_123PartitionOrtFormatModelERNS_5GraphERKNS_18ExecutionProvidersERNS_21KernelRegistryManagerERNS_12SessionStateERKNS_13ConfigOptionsERKNS_7logging6LoggerEEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlRKS1_E1_*ZN11onnxruntime16InferenceSession14TransformGraphERNS_5GraphEbEUlS2_RbRKNS_18IExecutionProviderERKSt8functionIFvRKS1_EEE0_St23_Sp_counted_ptr_inplaceIN11onnxruntime12CPUAllocatorESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime17ExecutionPlanBaseEN11onnxruntime23SequentialExecutionPlanE QDQFinalCleanupTeanupTransformerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator; reference = onnxruntime::NodeArg*&; size_type = long unsigned int]YieldOpcom.microsoftBatchNormalizationInstanceNormalizationQLinearConvMaxUnpoolGlobalMaxPoolLRNGridSampleDepthToSpaceSpaceToDepthQLinearAveragePoolQLinearGlobalAveragePoolchannels_lastcom.ms.internal.nhwctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::NodeRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::NodeRef&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::GraphRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::GraphRef&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::basic_string_view; _Alloc = std::allocator >; reference = std::basic_string_view&; size_type = long unsigned int]Layout/Transpose optimization for InsertedPrecisionFreeCast_cast node to cast from float16 to float32 on cpu/onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h/onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.ccRecurseApplyImpl/onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.ccMatMulIntegerToFloatidx < input_defs.size()bool onnxruntime::IsIsolatedFp16NodeOnCpu(const Node&, Graph&, const KernelRegistry&)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = int; _Alloc = std::allocator; const_reference = const int&; size_type = long unsigned int]InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constdtype_attribute->second.has_i()RemoveDuplicateCastTransformer/onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.ccfused LayerNorm subgraphs SimplifiedLayerNormalization/onnxruntime_src/onnxruntime/core/optimizer/matmul_activation_fusion.ccFusedMatMul with activation FusedMatMulActivation_FusedActivationactivation_/onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add virtual onnxruntime::common::Status onnxruntime::MatmulBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/matmul_bn_fusion.ccGenerated from Matmul BatchNormalization fusionscale_tensorbias_tensormean_tensorvar_tensormatmul_b_tensorMatMulBnFusion_GemmB_MatMulBnFusion_GemmBiasMatMulBnFusion_GemmConstant initializer NodeArg shape should not be null. NodeArg: std::optional onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.ccvoid onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int]Unsupported data type: GetScalarConstantInitializerstd::optional > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)div_inputs.size() == 2mul_inputs.size() == 2FN27onnx_transpose_optimization15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4hashISJ_ESt8equal_toISJ_ESaISJ_EEEPFN27onnx_transpose_optimization15CostCheckResultERKNS_3api8GraphRefERKNS1_7NodeRefERKSt6vectorIlSaIlEERKSt13unordered_setINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt4hashISJ_ESt8equal_toISJ_ESaISJ_EEESt19bad_optional_accessN11onnxruntime30RemoveDuplicateCastTransformerEN11onnxruntime6ActionEN11onnxruntime14ReplaceWithNewEN11onnxruntime19ReplaceWithNewFixedE  '7   cannot create std::deque larger than max_size()std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::front() [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_deque.hpermcom.microsoft.nchwcReorderOutputreorderReorderInputNhwcTransformerNhwcFusedConvbool onnxruntime::GetTransposePerms(const Node&, std::vector&)/onnxruntime_src/onnxruntime/core/optimizer/matmul_transpose_fusion.cctranspose_node.InputDefs().size() == 1std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; reference = const onnxruntime::Node*&]size_t onnxruntime::UpdateConsumerCount(Graph&, NodeArg*, InlinedHashMap&)!node_consumers.empty()Fused MatMul and Scale_FusedMatMulAndScalestd::vector onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)input_node.InputDefs().size() == 2 && scale_and_index->second < 2std::vector onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const onnxruntime::InlinedHashSet >&)output_node.OutputDefs().size() == 1onnxruntime::Node* onnxruntime::ReorderCastAndTranspose(Graph&, Node*, InlinedHashMap&, std::deque&, bool&, bool&)Created a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodescast != nullptrtransBatchAtransBatchBfused MatMul and Transpose MatMul_With_Transposereshapestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = int; _Alloc = std::allocator; reference = int&; size_type = long unsigned int]spatialbn_scalebn_B_bn_nchwcllllllllllllasymmetricnearest_mode/onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.ccvoid onnxruntime::NchwcTransformerImpl::TransformConv(onnxruntime::Node&)output_channels <= nchwc_output_channelsBuffer overflow/onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.cccom.microsoft.nc      SqueezeUnsqueezestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&]Invalid node index constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge::NodeInfo; _Dp = std::_Optional_base]const onnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEnd(const onnxruntime::Graph&, End) const/onnxruntime_src/onnxruntime/core/graph/extended_graph_edge.hstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNode(const onnxruntime::Graph&, const onnxruntime::Node&, int)node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()std::optional onnxruntime::{anonymous}::GetPreviousPropagationEdge(const onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccsrc_node != nullptrstatic std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput(const onnxruntime::Graph&, const onnxruntime::Node&, int)node_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()std::optional onnxruntime::{anonymous}::GetNextPropagationEdge(const onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&)dst_node != nullptrauto_padNOTSETweight_zp_s8_2_u8Unexpected data type for QuantizeLinear input y_zero_point of bool onnxruntime::GetQConstantLowerUpper(const Graph&, const Node&, float&, float&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/clip_quantizelinear.ccCleanUpNodeSequence with name "" and /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_final_cleanup.ccCleaning up back-to-back nodes: onnxruntime::Node* onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEnd(onnxruntime::Graph&, End) constAt least one graph node must be specified in the propagation edge.onnxruntime::common::Status onnxruntime::{anonymous}::InsertQDQPair(onnxruntime::Graph&, const onnxruntime::graph_utils::ExtendedGraphEdge&, onnxruntime::NodeArg&, onnxruntime::NodeArg*, const std::string&, const onnxruntime::logging::Logger&)Inserted by QDQPropagationTransformerFailed to set op schema for added Q node.Failed to set op schema for added DQ node.src_node || dst_nodeInsertQDQPairInserting Q/DQ pair between node ("", index: at NodeArg ""._pre_q_q_to_dq_post_dqTryConvertDynamicQuantizeLSTM/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/avx2_weight_s8_to_u8.ccUnable 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,constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::graph_utils::ExtendedGraphEdge; _Dp = std::_Optional_base]PropagateQBackwardPropagateDQForward/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.ccqdq_s8_to_u8_zp_conversionqdq_s8_to_u8_quant/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/ensure_unique_dq_for_node_unit.ccEnsureUniqueDQForEachExplicitOutputEdge/duplicatedAdded by dropdrop_no_int16_supportGatherdropDQ1DQ2DQQDQSelectorActionTransformerdropSplitQDQ*DQQGemmN11onnxruntime3QDQ13SplitSelectorEN11onnxruntime3QDQ22SplitNodeGroupSelectorEN11onnxruntime3QDQ21SplitReplaceWithQuantEN11onnxruntime3QDQ26VariadicReplaceWithQLinearEN11onnxruntime3QDQ21InputVariadicSelectorEN11onnxruntime3QDQ25VariadicNodeGroupSelectorEN11onnxruntime3QDQ22ConvReplaceWithQLinearEN11onnxruntime3QDQ12ConvSelectorEN11onnxruntime3QDQ21ConvNodeGroupSelectorEN11onnxruntime3QDQ24MatMulReplaceWithQLinearEN11onnxruntime3QDQ14MatMulSelectorEN11onnxruntime3QDQ23MatMulNodeGroupSelectorEN11onnxruntime3QDQ20GemmReplaceWithQuantEN11onnxruntime3QDQ12GemmSelectorEN11onnxruntime3QDQ21GemmNodeGroupSelectorEN11onnxruntime3QDQ23WhereReplaceWithQLinearEN11onnxruntime3QDQ13WhereSelectorEN11onnxruntime3QDQ22WhereNodeGroupSelectorEN11onnxruntime3QDQ14BinarySelectorEN11onnxruntime3QDQ23BinaryNodeGroupSelectorEN11onnxruntime3QDQ18ReplaceWithQLinearEN11onnxruntime3QDQ23UnaryReplaceWithQLinearEN11onnxruntime3QDQ13UnarySelectorEN11onnxruntime3QDQ22UnaryNodeGroupSelectorEN11onnxruntime3QDQ19DropDQNodesSelectorEN11onnxruntime3QDQ23DropDQNodeGroupSelectorEN11onnxruntime3QDQ17NodeGroupSelectorEN11onnxruntime11RemoveNodesEN11onnxruntime15MergeIntoTargetEN11onnxruntime3QDQ20DropQDQNodesSelectorEN11onnxruntime3QDQ24DropQDQNodeGroupSelectorEN11onnxruntime12NodeSelectorEN11onnxruntime3QDQ12BaseSelectorEN11onnxruntime3QDQ17QDQReplaceWithNewE*N11onnxruntime12_GLOBAL__N_130GraphConstantInitializerGetterE*ZN11onnxruntime12_GLOBAL__N_119CleanUpNodeSequenceENS0_12NodeSequenceERNS_5GraphEmRKNS_7logging6LoggerEEUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_EnsureUniqueDQForNodeUnit    EnsureUniqueDQFoqueDQForNodeUnit 4std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; reference = const onnxruntime::Node*&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; const_reference = const onnxruntime::Node* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = int; _Alloc = std::allocator; reference = int&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnxruntime::Node::EdgeEnd*; _Alloc = std::allocator; reference = const onnxruntime::Node::EdgeEnd*&; size_type = long unsigned int]ValidateNodeGroupDQNodes, target node: /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/shared/utils.ccQDQ node group cannot have DQ node that produces a graph output. DQ node: QDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator; const_reference = const onnxruntime::Node* const&]onnxruntime::Node* onnxruntime::NodesToOptimize::GetNode(size_t, bool) const/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.hindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)A target node must be set.onnxruntime::NodesToOptimizeIndices onnxruntime::NodesToOptimizeIndicesBuilder::Build() const/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/helpers.cctarget_node != NodesToOptimizeIndices::kEmptyNodeIndexNode index value is too large to save to ORT format model: onnxruntime::GetNodesToOptimizeIndices(gsl::span, NodeIndex, gsl::span, int, int)::node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexQuickGelu/onnxruntime_src/onnxruntime/core/optimizer/quick_gelu_fusion.cc/onnxruntime_src/onnxruntime/core/optimizer/rocm_blas_alt_impl.cc/onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.ccFuseReluClip__min_zero_constantUnexpected data type for Clip 'min' input of virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc/onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = int; _Alloc = std::allocator; const_reference = const int&]Cannot replace concat node with initializer:Total fused reshape node count: ConcatTrainingFuse_SubgraphFused reshape node: ApplySelectorsAndActionsMatchAndProcessMatched /onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/selector_action_transformer.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; reference = gsl::not_null&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Action; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Action&]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::NodesToOptimizeIndices; _Dp = std::_Optional_base]ApplySavedRuntimeOptimizationsApplying runtime optimization action Nodes to optimize are not valid, skipping action.SetOpSinceVersionForProducedNodesNot enough produced nodes in the runtime optimization record.) does not match produced node op in runtime optimization record (Too many produced nodes in the runtime optimization record. for transformer Missing action New node op (MoveInputOutputImplIndex out of rangeMoveInputOutput/onnxruntime_src/onnxruntime/core/optimizer/selectors_actions/actions.ccCreateReplacementNodeRunForSaveFailed to set node op schema.Failed to remove node.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; reference = gsl::not_null&]Existing registration with name void onnxruntime::SelectorActionRegistry::RegisterSelectorAndAction(const std::string&, const OpVersionsMap&, std::unique_ptr, std::unique_ptr)inserted_in_name_to_entry*ZNK11onnxruntime3QDQ22SplitNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_*ZNK11onnxruntime3QDQ23DropDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_*ZNK11onnxruntime3QDQ24DropQDQNodeGroupSelector5CheckERKNS_11GraphViewerERKNS_4NodeERKSt6vectorIPS6_SaIS9_EESD_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_   Index out of ranstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = long int; _Alloc = std::allocator; const_reference = const long int&; size_type = long unsigned int]constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&; size_type = long unsigned int]ForEachWithIndexconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::unique_ptr; _Dp = std::_Optional_base, false, false>]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::basic_string_view; _Alloc = std::allocator >; const_reference = const std::basic_string_view&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx_transpose_optimization::api::TensorRef; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx_transpose_optimization::api::TensorRef&]!(__hi < __lo)constexpr const _Tp& std::clamp(const _Tp&, const _Tp&, const _Tp&) [with _Tp = long int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_algo.hnoop_with_empty_axesdup_replacements.find(&arg) == dup_replacements.end()onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const onnxruntime::InitializedTensorSet&)::/onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc_FloatCast Input to float_CastSkipLayerNormalization/onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs after before MemcpyAddCopyNodeCopy from/to host memorySNPEExecutionProvider' doesn't support memcpy Execution type 'ProcessInitializersInternalTestingExecutionProvidervoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&)bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const onnxruntime::InitializedTensorSet&) Memcpy nodes are added to the graph . 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.*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISA_ESt8equal_toISA_ESaISt4pairIKSA_SE_EEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISA_ESt8equal_toISA_ESaISt4pairIKSA_SE_EEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISC_ESt8equal_toISC_ESaISt4pairIKSC_SG_EEEEUlRKNS_7NodeArgEmE_ ̼+ std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = long unsigned int; _Alloc = std::allocator; const_reference = const long unsigned int&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::basic_string_view; _Alloc = std::allocator >; reference = std::basic_string_view&]Unsupported ONNX opset: NhwcMaxPoolstorage_order does not match rank Permutation length out of bounds for shape Permutation entry virtual void onnxruntime::ApiValueInfo::PermuteDims(const std::vector&)/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimization/ort_optimizer_api_impl.ccperm.size() == gsl::narrow_cast(shape_proto->dim_size())0 <= p && p_int < shape_proto->dim_size()Failed to get size of TensorProtovirtual size_t onnxruntime::ApiTensor::NumElements() constNo NodeArg found for name node_arg_ != nullptrvirtual std::unique_ptr onnxruntime::ApiGraph::GetValueInfo(std::string_view) conststd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&] domain not found in opset imports.int onnxruntime::GetSinceVersionForNewOp(std::string_view, std::string_view, const std::unordered_map, int>&)opset_import_iter != domain_to_version_map.end()An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.Transpose Optimizer is adding an unexpected node: since_version.has_value()Existing destination type is not compatible with source type.virtual 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)virtual std::vector onnxruntime::ApiTensor::Data() constAdded in transpose optimizer/onnxruntime_src/onnxruntime/core/optimizer/transpose_optimizer.ccTranspose optimizer failed: Failed to find initializer for name: virtual void onnxruntime::ApiGraph::TransposeInitializer(std::string_view, const std::vector&)successTransposeInitializerFailed to find initializer to reshape with name virtual void onnxruntime::ApiGraph::ReshapeInitializer(std::string_view, const std::vector&) to have different number of elementsCannot reshape initializer new_num_elts == old_num_eltsN27onnx_transpose_optimization3api8GraphRefEN11onnxruntime8ApiGraphEN27onnx_transpose_optimization3api12ValueInfoRefEN11onnxruntime12ApiValueInfoEN27onnx_transpose_optimization3api9TensorRefEN11onnxruntime9ApiTensorEN27onnx_transpose_optimization3api7NodeRefEN11onnxruntime7ApiNodeEconst_transpose_nspose_optimizerUnsqueezeElimination_/onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node Start CheckNodesInPathKk_reshape const not matchedPass CheckNodesInPathK/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.hk_transpose has not perm attributeStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.Pass CheckNodesInPathQq_transpose perm attribute not matchedFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.gather indices not matched.Start MatchInputMaskSubgraphDistilBertOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3Failed to find reshape shape path 1Failed to find reshape shape path 2Pass MatchInputMaskSubgraphDistilBertStart MatchPastSubgraphFailed to match v_concatpast_v_gather indices != 1past_k_gather indices != 0Pass MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vpast_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 and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphStart ValidateGemmInitializerPass ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedUnexpected data type for Clip input of onnxruntime::optimizer_utils::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::/onnxruntime_src/onnxruntime/core/optimizer/utils.ccbool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector&, const std::vector&, const onnxruntime::logging::Logger&)input_indices.size() == expected_values.size() && input_indices.size() > 0Slice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:CheckSliceParametersExpected value:Start MatchGemmSubgraphFaild to match gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedPass MatchGemmSubgraphInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathCheckSliceParameters 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 expected is not in valid range [-axis int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)/onnxruntime_src/onnxruntime/core/providers/common.hIsAxisInRange(axis, tensor_rank)Mask_Int32Cast mask from int64 to int32MaskCastStart FuseGptAttentionFaild to find path v to SplitFailed to find path for qFailed to find path for kFused Attention subgraphs unidirectionalmask_filter_valueFaild to find path to qkv_matmulCheckNodesInPathV return falseOutput edge count not expected for nodes in path vMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLq and v are not from same Split nodeCheckNodesInPathQ returns falseUsing transpose optimized patternopt_k_transpose perm attribute not matchedk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT.FuseSubGraphFaild to find path vFuseSubGraphQKFused an attention node.FuseSubGraphQKDistilBert/onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; reference = std::reference_wrapper&; size_type = long unsigned int]Failed in match input mask subgraphTotal fused Attention node count: BitmaskBiasDropoutBitmaskDropoutfused Add-Dropout-(Add) for /onnxruntime_src/onnxruntime/core/optimizer/bias_dropout_fusion.cc/onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.ccfused Add and Gelufused into softmax(input + bias)/onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.ccqkv_weightsqkv_bias  into softmax(inax(input + bias)w+2@>yE>   qqqpqqqqȷqVq4qq%qxqxqqLqqvirtual std::string onnxruntime::{anonymous}::actions::FuseConvActivationAction::OpType(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc and domain: Unsupported operator: Expected Conv then Add.virtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddRelu::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) constconv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"virtual std::vector onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ValueMoves(const onnxruntime::ReplaceWithNew::RuntimeState&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_act_fusion.ccNot eliminating output of node /onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.ccvirtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constrepresentative.output_index != kInvalidOutputIndex] because it's the graph's output.$rr$r$r$r$rrr$r$rfr<rConstantFoldIfNode': /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.ccIf node condition initializer: `' is expected to have a single boolean elementUnable to constant fold. InlineIfSubgraph failed allocator_ptr_can't constant fold Unsupported output type of . Can't constant fold Failed to get allocator for optimizeronnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector&, const onnxruntime::InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&, const std::function&)>&)/onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.ccCould not find a CPU kernel and hence typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeIndexInfo; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeIndexInfo&]virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constfetches.size() == node->OutputDefs().size() ImplicitInputs:Invalid input index for node . Index: ExplicitInputs:typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::{anonymous}::InitializerValue; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::{anonymous}::InitializerValue&]void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/graph/graph_utils.cc/onnxruntime_src/onnxruntime/core/optimizer/constant_sharing.ccTotal shared scalar initializer count: virtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.ccconv_W_tensor_protoadd_B_tensor_protoconv_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_bn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.ccmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_HardSigmoidExpected activation node.activation != nullptractivation_paramsvirtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constFailed to get Clip min/max constants.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)virtual onnxruntime::NodeAttributes onnxruntime::{anonymous}::actions::FuseConvAddActivationAction::ExtraAttributes(const onnxruntime::ReplaceWithNew::RuntimeState&) constConvActivationFusionConvActConvAddReluConvAddAct*N11onnxruntime12_GLOBAL__N_19selectors25ConvAddActivationSelectorE*N11onnxruntime12_GLOBAL__N_17actions27FuseConvAddActivationActionE*N11onnxruntime12_GLOBAL__N_19selectors22ConvActivationSelectorE*N11onnxruntime12_GLOBAL__N_17actions24FuseConvActivationActionE*N11onnxruntime12_GLOBAL__N_19selectors11ConvAddReluE*N11onnxruntime12_GLOBAL__N_17actions15FuseConvAddReluE*ZNK11onnxruntime15ConstantFolding9ApplyImplERNS_5GraphERbiRKNS_7logging6LoggerEEUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_N11onnxruntime13IDataTransferEN11onnxruntime15CPUDataTransferEN11onnxruntime15IExecutionFrameEN11onnxruntime23OptimizerExecutionFrameEortshared_RandomUniformRandomNormalRandomUniformLikeRandomNormalLike  /onnxruntime_src/onnxruntime/core/optimizer/common_subexpressionn_elimination.ccL>Cast Input from int64 to int32DynamicQuantizeLinearDynamicQuantizeMatMul/onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc/onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64CheckInputConflicting free dimension overrides./onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.ccwhich does not equal the specified override of Invalid free dimension override.The model has input '' with a fixed dimension size /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: 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 shapeRangeFailed to match Shape node. MatchPositionEmbeddingSubgraphsFromGatherOutput 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. 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.mask_indexfused EmbedLayerNorm subgraphs Input id is not valid. Segment id is not valid. Word embedding shape not expected.Input 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.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). FuseSubGraphDistilBert/onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.ccfast_gelu_outputfused GPT2Gelu subgraphs GPT2GeluFreeDimensionOverrideTransformeronnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)/onnxruntime_src/onnxruntime/core/optimizer/gather_fusion.ccSqueeze for Fused Gather nodesSplit for Fused Gather nodesSqueezeAxesInitializervirtual onnxruntime::common::Status onnxruntime::GatherToSliceFusion::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) constUnsqueeze for Fused Gather nodesrange_input_defs.size() == 3unsqueeze_output_Unsqueeze_UnsqueezeAxesInitializerSliceAxesInitializerSlice for Fused Gather nodes*ZN11onnxruntimeL28IsReducibleDoubleQDQSequenceERNS_5GraphERmmS2_S2_EUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_ Gelu approximatiposition_embeddi#B ;CG'7=*BL?@@std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; reference = std::reference_wrapper&]Fused Gemm with TransposeCompute_Create_State_Release_State_TryCreateKernelstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::KernelRegistry; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::KernelRegistry]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::shared_ptr; _Alloc = std::allocator >; reference = std::shared_ptr&]other_sum_input != nullptrvirtual bool onnxruntime::GemmSumFusion::SatisfyCondition(const onnxruntime::Graph&, const onnxruntime::Node&, const onnxruntime::logging::Logger&) const/onnxruntime_src/onnxruntime/core/optimizer/gemm_sum_fusion.cccalled_ == 1void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()Invalid other(scalers) sizevoid onnxruntime::Initializer::scale_by_axis(const onnxruntime::Initializer&, int, bool)/onnxruntime_src/onnxruntime/core/optimizer/initializer.ccscalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = float]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long int]Expecting the same data typeExpecting the same sizesize() == other.size()onnxruntime::Initializer& onnxruntime::Initializer::add(const onnxruntime::Initializer&)data_type() == other.data_type()onnxruntime::Initializer& onnxruntime::Initializer::sub(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::mul(const onnxruntime::Initializer&)onnxruntime::Initializer& onnxruntime::Initializer::div(const onnxruntime::Initializer&)QLinearGetThresholdsFromInputsiou_threshold must be in range [0, 1].Erffused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc/onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.ccEluSeluSoftsignfused Gemm FusedGemmvirtual onnxruntime::common::Status onnxruntime::GemmSumFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) constnew_gemm_input_defs.size() == 3new_gemm_output_defs.size() == 1gemm_input_edge.src_arg_index < 2sum_output_edge.src_arg_index == 0graph.RemoveNode(gemm_node.Index())sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(sum_node.Index())Fused Gemm with Sum_sum_transformed!sum_input_movedInitializer must have a datatypeonnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&)utils::HasDataType(tensor_proto)model_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.IsEmpty()Tried to allocate without valid type information, ort_value index=string tensor requires allocator to be provided.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Tensor; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Tensor&]/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToOrtValueImplopsetinit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8init_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14aRegisterOnnxOperatorKernelsRegisterCPUKernelsRegisterOnnxMLOperatorKernelsRegisterCpuContribKernelsRegisterNchwcKernelsRegisterQuantizationKernelsGetKernelRegistry/onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc/onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constPrepareCompute/onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.hMust have a valid input shape./onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: N11onnxruntime19ConstantOfShapeBaseINS_8TypeListIJlNS_9MLFloat16EfdasihtjmbEEEEE*N11onnxruntime12_GLOBAL__N_115ConstantOfShapeEN11onnxruntime25IExecutionProviderFactoryEN11onnxruntime18CpuProviderFactoryESt23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIvELN9__gnu_cxx12_Lock_policyE2EE*ZN11onnxruntime20CPUExecutionProvider25CreatePreferredAllocatorsEvEUliE_N11onnxruntime3QDQ24BinaryReplaceWithQLinearE*ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKN4onnx11TensorProtoESt4hashISG_ESt8equal_toISG_ESaISt4pairIKSG_SK_EEERKNS_4PathERKNS_18IExecutionProviderERKSt8functionIFbRSQ_EEEUlRKNS_7NodeArgEmE_  e/providers/cpu/object_detection/non_max_suppres generated at run ??Output type not supported in this build: std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/random.hstd::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double]_M_stddev > _RealType(0)_M_a <= _M_bstd::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float]std::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double](null)input count mismatchCould not infer data type from input tensor with data type start 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 can not be zero!delta in Range operator should be scalar like tensor, yet got shape:Must have a single dimensiont_proto_p->dims_size() == 1t_proto_p->dims()[0] == 1utils::HasDataType(t_proto)SetValueFromTensorProtoonnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList]Must have a single dimension of 1void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList]ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())Tensor proto with external data for value attribute is not supported.!utils::HasExternalData(t_proto)Unsupported value attribute datatype: ,Htttt@ttPt,H`tttpt tInvalid dtype of onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.hinfo.GetAttr("mean", &mean_).IsOK()info.GetAttr("scale", &scale_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDhighonnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&)info.GetAttr("high", &high_).IsOK()info.GetAttr("low", &low_).IsOK()onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.hmin_ <= max_onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span, gsl::span)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.hdims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&)info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&)Empty dimensions for input tensor/onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.ccbatch_size is < 1num_classes is < 1num_samples is < 1MultinomialComputeInvalid data type of void onnxruntime::SliceIteratorBase::Init(gsl::span, gsl::span, gsl::span)dims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()Cannot apply CumSum operator on a scalar/onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cconnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext*) const [with T = float]N11onnxruntime6CumSumIlEEN11onnxruntime6CumSumIiEEN11onnxruntime6CumSumIdEEN11onnxruntime6CumSumIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_6Clip_6IfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplImEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIlEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIjEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIiEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIhEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIaEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIdEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_4Clip11ComputeImplIfEclEPKNS_6TensorES8_S8_PS6_PS1_EUllE_EEvSA_lOT_lEUllE_N11onnxruntime4ClipEN11onnxruntime13clip_internal10Clip_6BaseIfEEN11onnxruntime6Clip_6IfEEN11onnxruntime5RangeEN11onnxruntime11MultinomialEN11onnxruntime17RandomUniformLikeEN11onnxruntime13RandomUniformEN11onnxruntime16RandomNormalLikeEN11onnxruntime12RandomNormalEFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEEPFN11onnxruntime6common6StatusERNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteIS8_EEE??0???AC? Einsum op: There must be atleast one inputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; reference = const onnxruntime::Tensor*&]There was a problem acquiring temporary memory allocator in Einsum opvector::_M_fill_insertNumber of subscripts in the input equation does not match number of input tensorsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator; const_reference = const onnxruntime::Tensor* const&; size_type = long unsigned int]Found 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 Another operand has a dim value of Einsum 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 . The shape is: in the same dimensionFound 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 inputsstarts.size()=dims.size()=dims.size() == starts.size()extents.size()=steps.size()=dims.size() == steps.size()void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = long int]dims.size() == extents_.size()void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = int]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = double]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [with T = float]onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/det.ccInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedX != nullptrMissing 'equation' attribute->basic_string::substronnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.hinfo.GetAttr("equation", &equation_).IsOK()%s: __pos (which is %zu) > this->size() (which is %zu)Einsum op: The candidate output does not match the actual output's shapeonnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&, void*)/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccoutput.SizeInBytes() == input.SizeInBytes()The rank of the input must match permutation size for Transposebool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const gsl::span&)input_rank == permutation.size()onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccnum_broadcasted_indices < num_of_ellipsis_dims_The broadcasted dimensions of the inputs are incompatibledim_iter == rankThe innermost dims should have the same dim value to parse the diagonal elementsstd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]Einsum op: Unsupported data type for Diagonal Length of permutation must match the rank of the input to be permutatedstd::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, onnxruntime::AllocatorPtr, void*, const DeviceHelpers::Transpose&)Einsum op: Transpose failed: Cannot parse the diagonal elements along dims std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*)rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)] for input shape std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]Rank of the input must match number of subscript labels corresponding to the inputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; reference = onnxruntime::TensorShape&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::EinsumEquationPreprocessor; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::EinsumEquationPreprocessor&]Inputs have ellipses in them but the provided output subscript does not contain an ellipsis/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator; const_reference = const onnxruntime::TensorShape&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]Einsum op: An implementation for the input type DeviceCompute is not supported yetFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEPFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_N3gsl4spanIKlLm18446744073709551615EEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_N3gsl4spanIKlLm18446744073709551615EEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvEFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEPFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEFN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERS8_PKNS_11TensorShapeEPvEPFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvEPFN11onnxruntime6common6StatusERKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERS8_PKNS_11TensorShapeEPvEN11onnxruntime6EinsumEN11onnxruntime3DetIfEE??Einsum op: The candidate output cannot be reshaped into the op's outputvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cccandidate_output.Shape().Size() == output_shape.Size()Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: The override dims are not compatible with given tensor's shape. std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = float]left.Shape().Size() == left_shape_override.Size()right.Shape().Size() == right_shape_override.Size()Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsEinsum op: Input shapes do not alignData types of the inputs must match for MatMulstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]input_1.DataType() == input_2.DataType()Only 1 batch dimension is allowed for MatMulinput_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Batch dimension should match for MatMul;input_shape_1_override[0] == input_shape_2_override[0]Incompatible matrix dimensions for matMulinput_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rankleft_dim == right_dimvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor&, const gsl::span&) [with T = long int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const gsl::span&, bool) [with T = long int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const gsl::span&, const onnxruntime::Tensor&, const gsl::span&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, DeviceHelpers::MatMul&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr; DeviceHelpers::MatMul = std::function]axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.hCan broadcast 0 by 0 or 1. largest <= 1onnxruntime::Broadcaster::Broadcaster(gsl::span, gsl::span)void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)InputBroadcaster can only start at span boundary!void onnxruntime::InputBroadcaster::AdvanceBy(size_t)offset % span_size_ == 0) for tensor of length:Invalid start/ending offset [Broadcast Output range [onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len) are not at boundary of span with size:start_offset % span_size == 0 && real_end % span_size == 0onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&)N11onnxruntime3SubIlEEN11onnxruntime3SubIiEEN11onnxruntime3SubIdEEN11onnxruntime3SubIfEEN11onnxruntime3AddIlEEN11onnxruntime3AddIiEEN11onnxruntime3AddIdEEN11onnxruntime3AddIfEE ElementWiseKernel/onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt]N11onnxruntime8functors4SqrtIdEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEEN11onnxruntime8functors4SqrtIfEEN11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEEN11onnxruntime8functors10ReciprocalIdEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIdEEEEN11onnxruntime8functors10ReciprocalIfEEN11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEEN11onnxruntime8functors4CeilIdEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIdEEEEN11onnxruntime8functors4CeilIfEEN11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEEN11onnxruntime8functors5FloorIdEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIdEEEEN11onnxruntime8functors5FloorIfEEN11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEEN11onnxruntime8functors3NegIlEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEEN11onnxruntime8functors3NegIiEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEEN11onnxruntime8functors3NegIaEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEEN11onnxruntime8functors3NegIdEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEEN11onnxruntime8functors3NegIfEEN11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEEN11onnxruntime8functors26ElementWiseRangedTransformImEEN11onnxruntime8functors3AbsImEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEEN11onnxruntime8functors26ElementWiseRangedTransformIjEEN11onnxruntime8functors3AbsIjEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEEN11onnxruntime8functors26ElementWiseRangedTransformItEEN11onnxruntime8functors3AbsItEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEEN11onnxruntime8functors26ElementWiseRangedTransformIhEEN11onnxruntime8functors3AbsIhEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEEN11onnxruntime8functors26ElementWiseRangedTransformIlEEN11onnxruntime8functors3AbsIlEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEEN11onnxruntime8functors26ElementWiseRangedTransformIiEEN11onnxruntime8functors3AbsIiEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEEN11onnxruntime8functors26ElementWiseRangedTransformIsEEN11onnxruntime8functors3AbsIsEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEEN11onnxruntime8functors26ElementWiseRangedTransformIaEEN11onnxruntime8functors3AbsIaEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEEN11onnxruntime8functors26ElementWiseRangedTransformIdEEN11onnxruntime8functors3AbsIdEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEEN11onnxruntime8functors26ElementWiseRangedTransformIfEEN11onnxruntime8functors3AbsIfEEN11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEEN11onnxruntime3DivIlEEN11onnxruntime3DivIiEEN11onnxruntime3DivIdEEN11onnxruntime3DivIfEEN11onnxruntime3MulIlEEN11onnxruntime3MulIiEEN11onnxruntime3MulIdEEN11onnxruntime3MulIfEE onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log]N11onnxruntime6Mean_8IfEEN11onnxruntime6Mean_6IfEEN11onnxruntime14GreaterOrEqualIlEEN11onnxruntime14GreaterOrEqualIiEEN11onnxruntime14GreaterOrEqualIdEEN11onnxruntime14GreaterOrEqualIfEEN11onnxruntime11LessOrEqualIlEEN11onnxruntime11LessOrEqualIiEEN11onnxruntime11LessOrEqualIdEEN11onnxruntime11LessOrEqualIfEEN11onnxruntime5EqualINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime5EqualIdEEN11onnxruntime5EqualIfEEN11onnxruntime5EqualIlEEN11onnxruntime5EqualIiEEN11onnxruntime5EqualIbEEN11onnxruntime7GreaterIlEEN11onnxruntime7GreaterIiEEN11onnxruntime7GreaterIdEEN11onnxruntime7GreaterIfEEN11onnxruntime4LessIlEEN11onnxruntime4LessIiEEN11onnxruntime4LessIdEEN11onnxruntime4LessIfEEN11onnxruntime5Min_8EN11onnxruntime5Min_6IfEEN11onnxruntime5Max_8EN11onnxruntime5Max_6IfEEN11onnxruntime5Sum_8IdEEN11onnxruntime5Sum_8IfEEN11onnxruntime5Sum_6IdEEN11onnxruntime5Sum_6IfEEN11onnxruntime8functors3LogIdEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIdEEEEN11onnxruntime8functors3LogIfEEN11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEEN11onnxruntime8functors3ExpIdEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEEN11onnxruntime8functors3ExpIfEEN11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEEN11onnxruntime3PowE      Unsupported Y type: Unsupported X type: Must have 1 or more inputsinputCount >= 1data_n.Shape() == shapeonnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.ccAll inputs must have the same shapeonnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]data_1.Shape() == shapefmod must have value either 0 or 1onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&)(fmod == 0) || (fmod == 1)fmod attribute must be true for floating point typesvoid onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = float; typename std::enable_if::value, void>::type = void]void onnxruntime::mod_internal::CallModImpl::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = double; typename std::enable_if::value, void>::type = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext*) constN11onnxruntime3ModEN11onnxruntime8Expand_8INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime5PReluIfEEN11onnxruntime5AtanhIfEEN11onnxruntime5AcoshIfEEN11onnxruntime5AsinhIfEEN11onnxruntime4CoshIfEEN11onnxruntime4SinhIfEEN11onnxruntime4AtanIfEEN11onnxruntime4AcosIfEEN11onnxruntime4AsinIfEEN11onnxruntime3TanIfEEN11onnxruntime3CosIfEEN11onnxruntime3SinIdEEN11onnxruntime3SinIfEEN11onnxruntime3XorEN11onnxruntime2OrEN11onnxruntime3AndEN11onnxruntime3NotEN11onnxruntime3ErfIfEEN11onnxruntime10BitwiseXorImEEN11onnxruntime10BitwiseXorIjEEN11onnxruntime10BitwiseXorItEEN11onnxruntime10BitwiseXorIhEEN11onnxruntime10BitwiseXorIlEEN11onnxruntime10BitwiseXorIiEEN11onnxruntime10BitwiseXorIsEEN11onnxruntime10BitwiseXorIaEEN11onnxruntime9BitwiseOrImEEN11onnxruntime9BitwiseOrIjEEN11onnxruntime9BitwiseOrItEEN11onnxruntime9BitwiseOrIhEEN11onnxruntime9BitwiseOrIlEEN11onnxruntime9BitwiseOrIiEEN11onnxruntime9BitwiseOrIsEEN11onnxruntime9BitwiseOrIaEEN11onnxruntime10BitwiseNotImEEN11onnxruntime10BitwiseNotIjEEN11onnxruntime10BitwiseNotItEEN11onnxruntime10BitwiseNotIhEEN11onnxruntime10BitwiseNotIlEEN11onnxruntime10BitwiseNotIiEEN11onnxruntime10BitwiseNotIsEEN11onnxruntime10BitwiseNotIaEEN11onnxruntime10BitwiseAndImEEN11onnxruntime10BitwiseAndIjEEN11onnxruntime10BitwiseAndItEEN11onnxruntime10BitwiseAndIhEEN11onnxruntime10BitwiseAndIlEEN11onnxruntime10BitwiseAndIiEEN11onnxruntime10BitwiseAndIsEEN11onnxruntime10BitwiseAndIaEE  onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char]'. Valid values are 'LEFT' or 'RIGHT'.LEFTRIGHTInvalid direction value of 'onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int]*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_*ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_N11onnxruntime8BitShiftImEEN11onnxruntime8BitShiftIjEEN11onnxruntime8BitShiftIhEE@@Invalid usage. Input 1 is a shape with no data.onnxruntime::Expand_8 >::Compute(onnxruntime::OpKernelContext*) const::input_count >= 1onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = double]onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext*) const [with T = float]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]input_size < std::numeric_limits::max()onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg]??????@C@C̐̐@@??;f?;f?Ó´?Ó´?RV??RV??i@i@.>r,@.>r,@MK1@MK1@~}c@~}c@&3T@&3T@!^Q@!^Q@% 7@% 7@ &@ &@3N,F@3N,F@KKKKCCCC????5?5?5?5?!=!=!=!=뽸뽸뽸%=%=%=%=O]O]O]O]>>>>P*P*P*P*L>L>L>L>>>>>+eG?+eG?00@.?@.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ?pAr1??K9B.?ʡE.@ʡE.f@ @.@@std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr >; _Alloc = std::allocator > >; reference = std::unique_ptr >&]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs]Memory allocation failed. Size=static void onnxruntime::IAllocator::ValidateAllocation(void*, size_t)/onnxruntime_src/include/onnxruntime/core/framework/allocator.hp != nullptr || size == 0onnxruntime::GemmBase::GemmBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_base.hinfo.GetAttr("transA", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()info.GetAttr("alpha", &alpha_).IsOK()static void onnxruntime::IAllocator::ValidateAllocator(const T&) [with T = std::shared_ptr]allocator != nullptrstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::IAllocator; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::IAllocator]c_shape is required if c_data is providedvoid onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const TensorShape*, T*) [with T = float; ptrdiff_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.hc_shape != nullptronnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&)left.NumDimensions() == 2 || left.NumDimensions() == 1Gemm: Invalid bias shape for broadcastright.NumDimensions() == 2left[i] >= 0right[i] >= 0 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T*, const TensorShape*, T*) [with T = double; ptrdiff_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=N11onnxruntime6MatMulIlEEN11onnxruntime6MatMulIiEEN11onnxruntime6MatMulIdEEN11onnxruntime6MatMulIfEEN11onnxruntime7HardmaxIfEEZNK11onnxruntime4GemmIdE17ComputeActivationEPdlPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime13BufferDeleterEZNK11onnxruntime4GemmIfE17ComputeActivationEPflPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIvEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPvE_N11onnxruntime4GemmIdEEN11onnxruntime8GemmBaseEN11onnxruntime4GemmIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_SGEMM_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_SGEMM_DATA_PARAMS&; size_type = long unsigned int]SoftmaxCPU inputs N, D and N * D must be < std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = double; _Alloc = std::allocator; reference = double&; size_type = long unsigned int]Signk_temp > 0void onnxruntime::TopkOpset9ConstructorCommon(const OpKernelInfo&, int&, unsigned int&)/onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.ccop_kernel_info.GetAttr("k", &k_temp).IsOK()op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const OpKernelInfo&, int&)sortedvoid onnxruntime::TopkOpset11ConstructorCommon(const OpKernelInfo&, int&, bool&, bool&)op_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()input 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 negative] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatork argument [ComputeImplOpset13/onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.cc*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_N11onnxruntime4TopKILi11EiEEN11onnxruntime4TopKILi11ElEEN11onnxruntime4TopKILi11EdEEN11onnxruntime4TopKILi11EfEEN11onnxruntime4TopKILi10EdEEN11onnxruntime4TopKILi10EfEEN11onnxruntime4TopKILi9EdEEN11onnxruntime4TopKILi9EfEEN11onnxruntime7SoftmaxIdEEN11onnxruntime7SoftmaxIfEEN11onnxruntime4SignEN11onnxruntime5RoundIdEEN11onnxruntime5RoundIfEEN11onnxruntime5RoundINS_9MLFloat16EEE?@@input count mismatch, expected 1 input - the tensor to be proces+eG?; 333333?Input data with index: is NaNInput of string must have output of int64Input of int64 must have output of string Invalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[) >=TO_STRINGTO_INT64Invalid CAST_TO value of Expected DENSE or SPARSEInvalid PACK_MAP value of onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.hinfo.GetAttr("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.hinfo.GetAttr("map_form", &attr).IsOK()onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string&)info.GetAttr("max_map", &max_map_).IsOK()max_map must be > 0 if map_form is SPARSEmap_form_ != PACK_MAP::SPARSE || max_map_ > 0/onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&)num_entries == int_categories.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::__cxx11::basic_string; _Alloc = std::allocator >; reference = std::__cxx11::basic_string&; size_type = long unsigned int]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::__cxx11::basic_string; TargetType = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.hinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::__cxx11::basic_string; TargetType = float]Invalid input type of value: %fUnexpected CAST_TO value of Expected std::map or std::mapNegative index values are not permitted. First entry in map has index value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cccur_input == end_input || cur_input->first >= 0onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::__cxx11::basic_string; TTo = float]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::__cxx11::basic_string; TTo = long int]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::__cxx11::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::__cxx11::basic_string; TTo = std::__cxx11::basic_string]N11onnxruntime2ml16DictVectorizerOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfEEN11onnxruntime2ml16DictVectorizerOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime2ml14CategoryMapperEN11onnxruntime2ml7CastMapEN11onnxruntime2ml11BinarizerOpIfEEN11onnxruntime2ml23ArrayFeatureExtractorOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml23ArrayFeatureExtractorOpIlEEN11onnxruntime2ml23ArrayFeatureExtractorOpIiEEN11onnxruntime2ml23ArrayFeatureExtractorOpIdEEN11onnxruntime2ml23ArrayFeatureExtractorOpIfEE*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_*ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_*ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_string_vocabularInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = float; _Alloc = std::allocator; const_reference = const float&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = float; _Alloc = std::allocator; reference = float&; size_type = long unsigned int]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::__cxx11::basic_string; TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::__cxx11::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, double>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, float>]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map, long int>]inputdimensions attribute must be providedonnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.hstatus.IsOK() && !input_dimensions_.empty()Number of inputs (input_tensor_ptr != nullptrInvalid input type:) does not match number of inputdimensions values (virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.ccExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) constEmpty value of imputed values.Empty input dimensions.Invalid type/onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&)info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()keys_floatsvalues_stringsLabelEncoder_2num_keys == num_valuesonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::__cxx11::basic_string]keys_stringsvalues_floatsdefault_floatonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = float]keys_int64sonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]values_int64sonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]LabelEncoder could not unpack default tensor T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = float; std::string = std::__cxx11::basic_string]result.IsOK()T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = std::__cxx11::basic_string; std::string = std::__cxx11::basic_string]N11onnxruntime2ml14LabelEncoder_2IflEEN11onnxruntime2ml14LabelEncoder_2IlfEEN11onnxruntime2ml14LabelEncoder_2INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfEEN11onnxruntime2ml14LabelEncoder_2IfNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml12LabelEncoderEN11onnxruntime2ml9ImputerOpEN11onnxruntime2ml17FeatureVectorizerEN11onnxruntime2ml16DictVectorizerOpIldEEN11onnxruntime2ml16DictVectorizerOpIlfEEN11onnxruntime2ml16DictVectorizerOpIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml16DictVectorizerOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdEEint64_vocabularyonnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = long int]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int]LabelEncoder is missing attribute std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = long int; std::string = std::__cxx11::basic_string]LabelEncoder could not unpack tensor attribute keys_tensorvalues_tensorkeys.size() == values.size()Keys and values must have the same length.onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = std::__cxx11::basic_string; std::string = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = long int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = std::__cxx11::basic_string]default_tensorT onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = short int; std::string = std::__cxx11::basic_string]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = short int; std::string = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = short int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = short int; _Alloc = std::allocator; reference = short int&; size_type = long unsigned int]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = float; std::string = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = float]std::vector onnxruntime::ml::GetAttribute(const onnxruntime::OpKernelInfo&, const std::string&, const std::string&) [with T = double; std::string = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = double; TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = double; TValue = std::__cxx11::basic_string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = std::__cxx11::basic_string; TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = double; TValue = long int]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = double]N11onnxruntime2ml14LabelEncoder_4IldEEN11onnxruntime2ml14LabelEncoder_4IdlEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEdEEN11onnxruntime2ml14LabelEncoder_4IdNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_4IddEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEsEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_EEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEfEEN11onnxruntime2ml14LabelEncoder_4INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime2ml14LabelEncoder_4IflEEN11onnxruntime2ml14LabelEncoder_4IfNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_4IffEEN11onnxruntime2ml14LabelEncoder_4IlfEEN11onnxruntime2ml14LabelEncoder_4IlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_4IllEEN11onnxruntime2ml14LabelEncoder_2IllEEN11onnxruntime2ml14LabelEncoder_2INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime2ml14LabelEncoder_2IlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml14LabelEncoder_2IffEEN11onnxruntime2ml14LabelEncoder_2INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_EERank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = std::__cxx11::basic_string]LOGISTICSOFTMAXSOFTMAX_ZERO!coefficients_.empty()onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.ccvoid onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&)onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.hinfo.GetAttr("norm", &norm).IsOK()onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string&)Invalid normalize value of num_categories_ > 0One and only one of the 'cats_*' attributes must be definedonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cctmp_cats_int64s.empty() || tmp_cats_strings.empty()onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::__cxx11::basic_string]Empty scale in attributes!scale_.empty()Scale size: () != (onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.ccscale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int]Input shape needs to be at least a single dimension.Invalid size requested for allocation: void* onnxruntime::IAllocator::AllocArray(size_t, size_t)void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = int]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = long int]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span&) [with SrcType = double]Unsupported input element type of Scores output is incorrect size. Expected:void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span, ptrdiff_t, ptrdiff_t, ptrdiff_t, const std::vector&, const std::vector&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) constscores_output_data.size() >= scores_output_size * shape_size == out.size()Input shape had more than 2 dimension. Dims=Unsupported data type of Unknown Category and zeros = 0.N11onnxruntime2ml8ScalerOpIlEEN11onnxruntime2ml8ScalerOpIdEEN11onnxruntime2ml8ScalerOpIfEEN11onnxruntime2ml15OneHotEncoderOpINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime2ml15OneHotEncoderOpIdEEN11onnxruntime2ml15OneHotEncoderOpIfEEN11onnxruntime2ml15OneHotEncoderOpIlEEN11onnxruntime2ml10NormalizerEN11onnxruntime2ml15LinearRegressorEZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_Lm18446744073709551615EEEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_N11onnxruntime2ml16LinearClassifierEz@@3ֳ??POLYRBFSVMCommon/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&)vectors_per_classprob_aprob_bSVMClassifiercoefficients_.size() > 0/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::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() > 0static size_t onnxruntime::IAllocator::ValidatedCalcMemSizeForArray(size_t, size_t)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float]TreeEnsembleClassifier/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int]base_values_as_tensorInitnodes_hitrates_as_tensornodes_values_as_tensortarget_weights_as_tensorSUM/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.honnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int]Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1Invalid argumentUnexpected mode:onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = int; ThresholdType = float; OutputType = float]class_weights_as_tensorclass_weightsclass_treeidsclass_nodeidsclass_idscomputeonnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = int; ThresholdType = float; OutputType = float]TreeEnsemble only works on 1D, 2D tensors.void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = int; ThresholdType = float; OutputType = float]One path in the graph requests feature std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::ScoreValue&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector, 6, std::allocator > >; _Alloc = std::allocator, 6, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 6, std::allocator > >&; size_type = long unsigned int]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hpredictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]predictions.size() == (size_t)n_targets_or_classes_Unknown aggregation function in TreeEnsemble.onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = int; ThresholdType = float; OutputType = float] features. but input tensor has N11onnxruntime2ml6detail18TreeEnsembleCommonIiffEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIiffEEN11onnxruntime2ml22TreeEnsembleClassifierIiEEN11onnxruntime2ml6detail18TreeEnsembleCommonIlffEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIlffEEN11onnxruntime2ml22TreeEnsembleClassifierIlEEN11onnxruntime2ml6detail18TreeEnsembleCommonIddfEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIddfEEN11onnxruntime2ml22TreeEnsembleClassifierIdEEN11onnxruntime2ml6detail28TreeEnsembleCommonAttributesEN11onnxruntime2ml6detail18TreeEnsembleCommonIfffEEN11onnxruntime2ml6detail28TreeEnsembleCommonClassifierIfffEEN11onnxruntime2ml22TreeEnsembleClassifierIfEEZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPfE_N11onnxruntime2ml12SVMRegressorIfEE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfLm18446744073709551615EEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime2ml9SVMCommonEN11onnxruntime2ml13SVMClassifierEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRSt8functionIFvlEEEEvPS1_lOT_lEUllE_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_N11onnxruntime2ml8ScalerOpIiEE@ ף;X dims is empty.onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = double; ThresholdType = double; OutputType = float]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = double; _Alloc = std::allocator; const_reference = const double&; size_type = long unsigned int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const onnxruntime::OpKernelInfo&) [with InputType = float; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = long int; ThresholdType = float; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = double; ThresholdType = double; OutputType = float]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMax; InputType = double; ThresholdType = double; OutputType = float]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::ScoreValue&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; const_reference = onnxruntime::ml::detail::TreeNodeElement* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector, 3, std::allocator > >; _Alloc = std::allocator, 3, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 3, std::allocator > >&; size_type = long unsigned int]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >; int64_t = long int]onnxruntime::common::Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with InputType = double; ThresholdType = double; OutputType = float]nodes_truenodeidclasslabels_int64sbase_values_asnodes_hitrates_avoid onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]L욅<Ԙ蘅XԘ8Ԙ ԘԘԘԡ$tĢ0ࠅȠLLLlLħLLtLx`P@إ 44쮅4D444D4P@0 void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorAverage; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(onnxruntime::InlinedVector >&, const onnxruntime::ml::detail::TreeNodeElement&, gsl::span >) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorSum; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorMin; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >] but got Tree id mismatch. Expected with flags size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = int; ThresholdType = float; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&; size_type = long unsigned int]False node must always be the next node, but it isn't at index size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = long int; ThresholdType = float; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = double; ThresholdType = double; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&; size_type = long unsigned int]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(size_t, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, onnxruntime::InlinedVector&, const std::vector&, const std::vector&, const std::vector&, const std::vector&, std::vector&, int64_t, onnxruntime::InlinedVector&) [with InputType = float; ThresholdType = float; OutputType = float; size_t = long unsigned int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >; int64_t = long int; onnxruntime::InlinedVector = absl::lts_20240116::InlinedVector >]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_24TreeAggregatorClassifierIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMaxIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorMinIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_17TreeAggregatorSumIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_21TreeAggregatorAverageIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS1_24TreeAggregatorClassifierIiffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_?void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >]ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMaxIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorMinIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_17TreeAggregatorSumIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_21TreeAggregatorAverageIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS1_24TreeAggregatorClassifierIddfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMaxIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorMinIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_17TreeAggregatorSumIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS1_21TreeAggregatorAverageIlffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_target_weightsbase_values_as_tensorvoid onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(onnxruntime::InlinedVector >&, onnxruntime::InlinedVector >&) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >]' is specified but is empty.Attribute:'' must be a vector.Attribute 'n_dims == 1Unexpected type ((for attribute ''.n_elements > 0onnxruntime::common::Status onnxruntime::ml::GetNumberOfElementsAttrsOrDefault(const onnxruntime::OpKernelInfo&, const std::string&, onnx::TensorProto_DataType, size_t&, onnx::TensorProto&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_helper.ccproto.data_type() == proto_type' has one dimension but is empty./onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double]N11onnxruntime2ml21TreeEnsembleRegressorIdEEN11onnxruntime2ml21TreeEnsembleRegressorIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMaxIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMaxIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMinIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorMinIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorSumIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_17TreeAggregatorSumIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_21TreeAggregatorAverageIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_21TreeAggregatorAverageIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_24TreeAggregatorClassifierIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS4_24TreeAggregatorClassifierIfffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMaxIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMaxIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMinIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorMinIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorSumIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_17TreeAggregatorSumIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_21TreeAggregatorAverageIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_21TreeAggregatorAverageIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_24TreeAggregatorClassifierIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIddfE10ComputeAggINS4_24TreeAggregatorClassifierIddfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMaxIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMaxIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMinIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorMinIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorSumIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_17TreeAggregatorSumIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_21TreeAggregatorAverageIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_21TreeAggregatorAverageIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_24TreeAggregatorClassifierIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlffE10ComputeAggINS4_24TreeAggregatorClassifierIlffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMaxIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMaxIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMinIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorMinIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorSumIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_17TreeAggregatorSumIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_21TreeAggregatorAverageIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_21TreeAggregatorAverageIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_24TreeAggregatorClassifierIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIiffE10ComputeAggINS4_24TreeAggregatorClassifierIiffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMaxIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorMinIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_17TreeAggregatorSumIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_21TreeAggregatorAverageIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIfffE10ComputeAggINS1_24TreeAggregatorClassifierIfffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_nodes_falsenodeinodes_values_as_Must provide classlabels_strings or classlabels_int64s but not both.onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.ccclasslabels_strings_.empty() ^ classlabels_int64s_.empty()kernel_shape_[dim] > 0/onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&)Pad should be smaller than kernel.pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()momentumis_spatial_Training mode only supports spatial BNonnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double]Divide by zerostatic void SafeIntExceptionHandler::SafeIntOnDivZero()VALIDSAME_UPPERSAME_LOWERUnknown AutoPadType Stringonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string&)A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.hauto_pad == AutoPadType::NOTSEToutput_shapeInput features_per_batch[] != number of classlabels[Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsT* OrtValue::GetMutable() [with T = std::vector, float> >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::map, float>; _Alloc = std::allocator, float> >; reference = std::map, float>&; size_type = long unsigned int]T* OrtValue::GetMutable() [with T = std::vector >]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::map; _Alloc = std::allocator >; reference = std::map&; size_type = long unsigned int]Unsupported pooling size. inferred output shape:/onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.ccInput dimension cannot be less than 3.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:Invalid number of outputs for BN trainingonnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = double]!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::common::Status onnxruntime::BatchNorm::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.ccoutput and sum shape must matchbool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*)/onnxruntime_src/onnxruntime/core/util/math_cpu.ccdims[d_i] < d_maxgroup count is <= 0 group: X: filter_number: num_input_channels: PrepareForCompute*out_size >= 0Invalid input shape: dim_size > 0DoConvTransposeX num_dims does not match W num_dims.filter number not equal to input channel number.Input channels is not divisible by group./onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h`InlinedVector::at(size_type)` failed bounds checkvoid onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) constvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const onnxruntime::TensorShapeVector&, const onnxruntime::TensorShapeVector&, const onnxruntime::TensorShapeVector&, const onnxruntime::TensorShapeVector&, int64_t, onnxruntime::ConvAttributes::ConvPadVector*, onnxruntime::TensorShapeVector*, bool) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.ccN11onnxruntime7DropoutIddEEN11onnxruntime7DropoutIdfEEN11onnxruntime7DropoutIfdEEN11onnxruntime7DropoutIffEEN11onnxruntime13ConvTransposeIfEEN11onnxruntime4ConvIfEEN11onnxruntime9BatchNormIdEEN11onnxruntime9BatchNormIfEEN11onnxruntime9MaxUnpoolEN11onnxruntime2ml8ZipMapOpEclasslabels_strifff?0A>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 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. FlattenLpNormalizationratio input should have a single value.float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.hratio_tensor->Shape().Size() == 1ratio must be in the range [0, 1)0.0f <= ratio_value && ratio_value < 1.0fgsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = bool]X and mask should have the same shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double]!mask || mask->Shape() == X_shapeonnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double]float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float]onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float]The rank of input tensor must be >= axisvirtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.hgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&)info.GetAttr("axis", &axis_).IsOK()onnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.hop_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo&) [with T = double]size_ % 2 == 1alpha_ > 0.0fbeta_ > 0.0fonnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.hinfo.GetAttr("size", &size).IsOK()info.GetAttr("beta", &beta_).IsOK()No kernel shape is set.ceil_modecount_include_padkernel_shape[dim] > 0onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper&, const std::string&, int)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.hinfo.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.hinfo.GetAttr("p", &p_).IsOK()onnxruntime::LpPoolV18::LpPoolV18(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.hSize of X.shape()[axis:] == and bias size of ComputeImpl. Size of scale and bias (if provided) must match this. Got scale size of /onnxruntime_src/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cconnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.ccX->Shape().NumDimensions() == 4/onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cckernel_shape num_dims is not compatible with X num_dims.N11onnxruntime9LpPoolV18IfEEN11onnxruntime4PoolIfNS_6LpPoolEEEN11onnxruntime9MaxPoolV8EN11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEEN11onnxruntime14AveragePoolV19IfEEN11onnxruntime8PoolBaseEN11onnxruntime4PoolIfNS_11AveragePoolEEEN11onnxruntime3LRNIfEEN11onnxruntime8functors4PowxIfEEN11onnxruntime6LpNormIdEEN11onnxruntime6LpNormIfEE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIdfEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIddEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_12_GLOBAL__N_111ComputeImplIffEENS_6common6StatusEPNS_15OpKernelContextElfbEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime13LayerNormImplEN11onnxruntime9LayerNormEN11onnxruntime12InstanceNormIfEEN11onnxruntime7FlattenE  t@pstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Alloc = std::allocator; reference = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr&]Unsupported pooling size : Unsupported kernel dimension : ShrinkTfIdfVectorizerR->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizeonnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.ccpooled_shapepooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scale_ > 0/onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float]info.GetAttr("spatial_scale", &spatial_scale_).IsOK()onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.hop_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()center_point_box only support 0 or 1onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h0 == center_point_box_ || 1 == center_point_box_static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccboxes 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.boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SelectedIndex; _Alloc = std::allocator; reference = onnxruntime::SelectedIndex&]void std::priority_queue<_Tp, _Sequence, _Compare>::pop() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Sequence = std::vector; _Compare = std::less]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_queue.hvirtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) constoutput != nullptrmode is requiredTFTFIDFmode: min_gram_length is requiredimpl_->min_gram_length_ > 0max_gram_length >= max_skip_countmax_skip_count is requiredimpl_->max_skip_count_ >= 0ngram_counts <= ngram_indexes is defined.No attribute with name: Attribute: but is of type: must be of equal size but ngram_indexes size: Got weights of size: pool_stringsRequested attribute: is expected to have type: !pool_strings.empty()pool_int64s-grams(items % ngram_size == 0)p.first->second->id_ == 0min_gram_lengthonnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDFimpl_->weighting_criteria_ != kNoneRequired min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_max_skip_count must be non-negative: Non-empty ngram_counts is requiredstatus.IsOK() && !impl_->ngram_counts_.empty()min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()Non-empty ngram_indexes is requiredstatus.IsOK() && !impl_->ngram_indexes_.empty()Negative ngram_indexes values are not allowedstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })impl_->weights_.size() == impl_->ngram_indexes_.size()pool_strings must not be empty if specifiednon-empty pool_int64s is required if pool_strings not providedstatus.IsOK() && !pool_int64s.empty()n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_itemsNumber of items must compose whole Duplicate ngram detected, size: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = gsl::details::span_iterator; Map = std::unordered_map > >; size_t = long unsigned int]size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::__cxx11::basic_string; ForwardIter = __gnu_cxx::__normal_iterator >*, std::vector > > >; Map = std::unordered_map >, std::unique_ptr > >, std::hash >, std::equal_to > >; size_t = long unsigned int]Input shape must have either [C] or [B,C] dimensions with B > 0.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::TfIdfVectorizer::Impl; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::TfIdfVectorizer::Impl&]avgInvalid mode of value sampling_ratio_ >= 0RoiAlignBasewill be different. specified. It should be either avg or maxonnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.hSampling ratio should be >=0, but it was The 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 N11onnxruntime12RoiAlignBaseEN11onnxruntime8RoiAlignIfEEN11onnxruntime21NonMaxSuppressionBaseEN11onnxruntime17NonMaxSuppressionE*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE3_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE2_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE1_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE0_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUlmRN3gsl4spanIfLm18446744073709551615EEEE_*ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE4_N11onnxruntime15TfIdfVectorizerEN11onnxruntime6ShrinkEN11onnxruntime7RoiPoolIfEEN11onnxruntime17AveragePool3DTaskIfEEN11onnxruntime17AveragePool2DTaskIfEEN11onnxruntime17AveragePool1DTaskIfEEN11onnxruntime10Pool3DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool2DTaskIfNS_6LpPoolEEEN11onnxruntime10Pool1DTaskIfNS_6LpPoolEEEN11onnxruntime12LpPool3DTaskIfEEN11onnxruntime12LpPool2DTaskIfEEN11onnxruntime12LpPool1DTaskIfEEN11onnxruntime13MaxPool3DTaskIhEEN11onnxruntime13MaxPool2DTaskIhEEN11onnxruntime13MaxPool1DTaskIhEEN11onnxruntime13MaxPool3DTaskIaEEN11onnxruntime13MaxPool2DTaskIaEEN11onnxruntime13MaxPool1DTaskIaEEN11onnxruntime13MaxPool3DTaskIdEEN11onnxruntime13MaxPool2DTaskIdEEN11onnxruntime13MaxPool1DTaskIdEEN11onnxruntime13MaxPool3DTaskIfEEN11onnxruntime13MaxPool2DTaskIfEEN11onnxruntime13MaxPool1DTaskIfEE_center_point_boxGetThresholdsFrostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; reference = onnxruntime::{anonymous}::PreCalc&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc; _Alloc = std::allocator >; reference = onnxruntime::{anonymous}::PreCalc&; size_type = long unsigned int]Null 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 matchConvIntegerT4Optional op must have a TypeProto in the 'type' attribute if the attribute is presentonnxruntime::Optional::Optional(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.hattr->has_tp()onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/dynamicquantizelinear.ccx_ptr != nullptronnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = signed char]/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/qlinearconv.ccprepacked_buffers[0].get() == nullptronnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector >&, int, bool&) [with ActType = unsigned char]PropagateInputOrtValueToFirstOutput/onnxruntime_src/onnxruntime/core/providers/cpu/optional/optional_ops.ccOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptabletypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::OpKernelInfo; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::OpKernelInfo&]can only be of type(tensor) or (seq(tensor))The TypeProto attribute in the Optional op Must be a scalar or 1D tensor or size 1.virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/conv_integer.ccIsScalarOr1ElementVector(X_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/matmul_integer.ccIsScalarOr1ElementVector(a_zero_point)MatmulInteger : B zero point is not validIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_GEMM_QUANT_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_GEMM_QUANT_DATA_PARAMS&; size_type = long unsigned int]QLinearConv : input zero point must be a scalar or 1D tensor of size 1static void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = signed char; int64_t = long int; uint8_t = unsigned char]IsScalarOr1ElementVector(X_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidIsValidQuantParam(W_zero_point, M)QLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.W_zero_point_data[i] == W_zero_point_valueQLinearConv : input scale must be a scalar or 1D tensor of size 1static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = signed char; int64_t = long int]IsScalarOr1ElementVector(X_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)static void onnxruntime::QLinearConv::ComputeOffset(onnxruntime::OpKernelContext*, int64_t, ActType&, ActType&, uint8_t&) [with ActType = unsigned char; int64_t = long int; uint8_t = unsigned char]static std::vector onnxruntime::QLinearConv::ComputeOutputScale(onnxruntime::OpKernelContext*, int64_t) [with ActType = unsigned char; int64_t = long int]ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime11QLinearConvIhE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime11QLinearConvIaE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime11QLinearConvIaEEN11onnxruntime11QLinearConvIhEEN11onnxruntime17MatMulIntegerBaseEN11onnxruntime13MatMulIntegerEZN11onnxruntime20ParQuantizeLinearStdIhEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime24GetQuantizationParameterIhLb0ELb0ELi0EEEvPKflRfRT_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime21DynamicQuantizeLinearIhEEN11onnxruntime11ConvIntegerEN11onnxruntime18OptionalGetElementEN11onnxruntime18OptionalHasElementEN11onnxruntime8OptionalE*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEbPKlPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime8RoiAlignIdEE@p@ @`@Trying to use OptionalGetElement on an optional type OrtValue which contains no x_zero_point must be null or a scalar or 1D tensor or size 1.void onnxruntime::PrepareForQDQ(const TensorShape&, const Tensor&, const Tensor*, int64_t, int64_t&, int64_t&, int64_t&)/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear.cczero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)Unsupported input type.onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short int]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2FNUZ]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FNUZ]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FN]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]DequantizeLinear into BFLOAT16 is not implemented yet.onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short int]DequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int]DequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2FNUZ]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E5M2]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FNUZ]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::Float8E4M3FN]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]ZN11onnxruntime20ParQuantizeLinearStdIhEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_12Float8E4M3FNEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJS1_NS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_12Float8E4M3FNEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJS1_NS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E4M3FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNES1_NS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E4M3FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNES1_NS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_10Float8E5M2EEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZES1_NS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_10Float8E5M2EEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZES1_NS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E5M2FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ES1_EEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_RKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearSatINS_14Float8E5M2FNUZEEENSt9enable_ifIXsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ES1_EEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfRKSD_bPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdItEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdItEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIsEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIsEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime14QuantizeLinearIsEEN11onnxruntime14QuantizeLinearItEEN11onnxruntime14QuantizeLinearINS_14Float8E5M2FNUZEEEN11onnxruntime14QuantizeLinearINS_10Float8E5M2EEEN11onnxruntime14QuantizeLinearINS_14Float8E4M3FNUZEEEN11onnxruntime14QuantizeLinearINS_12Float8E4M3FNEEEN11onnxruntime14QuantizeLinearIhEEN11onnxruntime14QuantizeLinearIaEEN11onnxruntime16DequantizeLinearIsEEN11onnxruntime16DequantizeLinearItEEN11onnxruntime16DequantizeLinearINS_14Float8E5M2FNUZEEEN11onnxruntime16DequantizeLinearINS_10Float8E5M2EEEN11onnxruntime16DequantizeLinearINS_14Float8E4M3FNUZEEEN11onnxruntime16DequantizeLinearINS_12Float8E4M3FNEEEN11onnxruntime16DequantizeLinearIiEEN11onnxruntime16DequantizeLinearIhEEN11onnxruntime16DequantizeLinearIaEEp@`@????????????????oooooooooooooooo77777777@@@@wwwwwwwwwwwwwwww::::;;;;;;;;````QLinearMatMulQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/quantization/quantize_linear_matmul.ccIsScalarOr1ElementVector(a_offset)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_REQUANT_OUTPUT_PROCESSOR&; size_type = long unsigned int]N11onnxruntime10ReduceMeanIiEEN11onnxruntime10ReduceMeanIfEEN11onnxruntime9ReduceMaxIbEEN11onnxruntime9ReduceMaxIhEEN11onnxruntime9ReduceMaxIaEEN11onnxruntime9ReduceMaxIdEEN11onnxruntime9ReduceMaxIlEEN11onnxruntime9ReduceMaxIiEEN11onnxruntime9ReduceMaxIfEEN11onnxruntime15ReduceLogSumExpIlEEN11onnxruntime15ReduceLogSumExpIdEEN11onnxruntime15ReduceLogSumExpIiEEN11onnxruntime15ReduceLogSumExpIfEEN11onnxruntime12ReduceLogSumIlEEN11onnxruntime12ReduceLogSumIiEEN11onnxruntime12ReduceLogSumIfEEN11onnxruntime8ReduceL2IlEEN11onnxruntime8ReduceL2IiEEN11onnxruntime8ReduceL2IfEEN11onnxruntime8ReduceL1IlEEN11onnxruntime8ReduceL1IiEEN11onnxruntime16ReduceKernelBaseILb1EEEN11onnxruntime12ReduceKernelILb1EEEN11onnxruntime8ReduceL1IfEE27MLAS_QGEMM_OUTPUT_PROCESSOR35MLAS_QGEMM_REQUANT_OUTPUT_PROCESSORN11onnxruntime13QLinearMatMulEZN11onnxruntime20ParQuantizeLinearStdIaEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKNS_9MLFloat16EPSD_mSJ_SD_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20ParQuantizeLinearStdIaEENSt9enable_ifIXntsrSt17integral_constantIbXscbsrN5boost4mp116detail13mp_count_implINS_8TypeListIJNS_12Float8E4M3FNENS_14Float8E4M3FNUZENS_10Float8E5M2ENS_14Float8E5M2FNUZEEEET_E4typeE5valueEE5valueEvE4typeEPKfPSD_mfSD_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime6ArgMinIiEEN11onnxruntime6ArgMinIfEEN11onnxruntime6ArgMaxIdEEN11onnxruntime6ArgMaxIhEEN11onnxruntime6ArgMaxIaEEN11onnxruntime6ArgMaxIiEEN11onnxruntime16ReduceKernelBaseILb0EEEN11onnxruntime12ReduceKernelILb0EEEN11onnxruntime6ArgMaxIfEEN11onnxruntime15ReduceSumSquareIlEEN11onnxruntime15ReduceSumSquareIdEEN11onnxruntime15ReduceSumSquareIiEEN11onnxruntime15ReduceSumSquareIfEEN11onnxruntime9ReduceSumIdEEN11onnxruntime9ReduceSumIlEEN11onnxruntime9ReduceSumIiEEN11onnxruntime9ReduceSumIfEEN11onnxruntime10ReduceProdIlEEN11onnxruntime10ReduceProdIiEEN11onnxruntime10ReduceProdIfEEN11onnxruntime9ReduceMinIbEEN11onnxruntime9ReduceMinIhEEN11onnxruntime9ReduceMinIaEEN11onnxruntime9ReduceMinIdEEN11onnxruntime9ReduceMinIlEEN11onnxruntime9ReduceMinIiEEN11onnxruntime9ReduceMinIfEEN11onnxruntime10ReduceMeanIdEEvoid onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cclast_loop_red_size > 0last_loop_size > 0projected_index.size() > 0must be overloaded.void onnxruntime::ValidateMustBeOverloaded()Only works on matrices with two dimensions.void onnxruntime::ValidateFastReduceKR(const gsl::span&, const Tensor&)fast_shape[0] == output.Shape().Size()fast_shape.size() == 2Output size mismatch.void onnxruntime::ValidateFastReduceRK(const gsl::span&, const Tensor&)fast_shape[1] == output.Shape().Size()Only works on matrices with three dimensions.void onnxruntime::ValidateFastReduceKRK(const gsl::span&, const Tensor&)fast_shape[0] * fast_shape[2] == output.Shape().Size()fast_shape.size() == 3void onnxruntime::ValidateFastReduceRKR(const gsl::span&, const Tensor&)Reduction on all axes, output size should be 1.void onnxruntime::ValidateNoTransposeReduce(int64_t)count == 1Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:void onnxruntime::ValidateKeepDims(const TensorShape&, int64_t)An axes tensor must be a vector tensor.bool onnxruntime::CommonFastReduceCopy(OpKernelContext*, TensorShapeVector&, bool)axes_tensor->Shape().NumDimensions() == 1Axes input and attribute should not both be present for reduction.bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]axes.empty()bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSum]ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorSumIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorSumIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorSumIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorSumIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorSumIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorSumIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorSumIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorSumIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime6ArgMinIdEEbool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]ZN11onnxruntime16ReduceAggregatorIllE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFlPS7_EESF_IFvRlSG_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIddE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFdPKdEESF_IFvRdSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIiiE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFiPKiEESF_IFvRiSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIffE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFfPKfEESF_IFvRfSH_lEEEUlllE_bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorLogSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL1]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL1]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorL1]forward'. Must be one of 'forward', 'reverse', or 'bidirectional'.Invalid 'direction' argument of 'onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const std::string&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.hGRU operator does not support double yetInvalid data type for GRU operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cchidden_sizeactivation_alphaactivation_betaclipclip_ > 0.flayout_ == 0onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.hinfo.GetAttr("direction", &direction).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activation_func_names.size() == static_cast(num_directions_) * 2Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.N11onnxruntime12DeepCpuGruOpEZN11onnxruntime16ReduceAggregatorIaaE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFaPKaEESF_IFvRaSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIhhE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFhPKhEESF_IFvRhSH_lEEEUlllE_ZN11onnxruntime16ReduceAggregatorIbbE19CommonFastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolESt8functionIFbPKbEESF_IFvRbSH_lEEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorMaxIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorMaxIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorMaxIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorMaxIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorMaxIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRaPKalE0_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKaE_ZN11onnxruntime19ReduceAggregatorMaxIaE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIaE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRhPKhlE0_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKhE_ZN11onnxruntime19ReduceAggregatorMaxIhE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIhE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRbPKblE0_ZN11onnxruntime19ReduceAggregatorMaxIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKbE_ZN11onnxruntime19ReduceAggregatorMaxIbE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIbE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMaxIbE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRfPKflE0_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKfE_ZN11onnxruntime19ReduceAggregatorMinIfE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIfE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRiPKilE0_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKiE_ZN11onnxruntime19ReduceAggregatorMinIiE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIiE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRlPS7_lE0_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPS7_E_ZN11onnxruntime19ReduceAggregatorMinIlE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIlE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRdPKdlE0_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKdE_ZN11onnxruntime19ReduceAggregatorMinIdE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIdE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRaPKalE0_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKaE_ZN11onnxruntime19ReduceAggregatorMinIaE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIaE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRhPKhlE0_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKhE_ZN11onnxruntime19ReduceAggregatorMinIhE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIhE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlRbPKblE0_ZN11onnxruntime19ReduceAggregatorMinIbE13FastReduceRKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlPKbE_ZN11onnxruntime19ReduceAggregatorMinIbE13FastReduceKRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIbE12FastReduceRKERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime19ReduceAggregatorMinIbE12FastReduceKRERKNS_6TensorERKN3gsl4spanIKlLm18446744073709551615EEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIbEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIdEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIbEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIfEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIalEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIalEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIhlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIhlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_N3gsl4spanIKlLm18446744073709551615EEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_PrePack/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccInput X must have 3 dimensions only. 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 W must have shape {}. Actual:Input R must have shape {Input B must have shape {Input initial_c must have shape {Input P must have shape {Can not get unpacked span from prepacked weightsgsl::span onnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan() const [with T = float]!is_prepacked_const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = gsl::details::span_iterator; size_t = long unsigned int]cur + size <= endT* onnxruntime::rnn::detail::SafeRawPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]offset + size <= size_t(span.size())T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [with T = float; typename gsl::span::iterator = gsl::details::span_iterator; size_t = long unsigned int]onnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.hinfo.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)RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end()Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got std::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&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; reference = onnxruntime::rnn::detail::ActivationFuncs::Entry&]void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)C + (M * ldc - (ldc - N)) <= C_endQuantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)A + (M * K) <= A_endweights.quant_para_affineleakyreluthresholdedreluscaledtanhhardsigmoidsoftsignsoftplusInvalid activation function of void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string&))(float*, int, float, float)Invalid LSTM merge activation function of void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string&))(const float*, float*, const float*, float*, int, float, float)Nested parallelism not supportedonnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*)/onnxruntime_src/onnxruntime/core/common/threadpool.cc!current_parallel_section.has_value()void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with 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_end!ps_onnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.hactivation_func_names.size() == static_cast(num_directions_) * 3/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.ccstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator; const_reference = const onnxruntime::rnn::detail::ActivationFuncs::Entry&; size_type = long unsigned int]LSTM operator does not support double yetInvalid data type for LSTM operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSORFffffEPFffffEN11onnxruntime3RNNIfEEN11onnxruntime8LSTMBaseEN11onnxruntime13DeepCpuLstmOpEZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPiE_ A7.A%%~3a*~3a*77A\3A\3JCy7JCy7 ': ':Y;Y;Ӡ5Ӡ5֕8֕8;;Y;Y;??Must have valid 'axis' attributeonnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concatbase.hnew_axisGot nullptr for sequence input.virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccint64_t onnxruntime::GetSeqIdx(const Tensor&)/onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccUnsupported 'dtype' value: virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) constK  8 X x  8 X x KK Invalid sequence index () specified for sequence of size (T onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/utils.hUnsupported input data type of tensor->Shape().Size() == 1Unsupported bit size.T onnxruntime::bit_reverse(T, unsigned int) [with T = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/dft.ccConcatFromSequenceSequenceLengthSequenceAtSequenceEmptySequenceInsertSequenceEraseSequenceConstructSplitToSequenceCloneTensoronnxruntime::Tensor onnxruntime::CloneTensor(const Tensor&, OpKernelContext*, const DataTransferManager&)Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (num_inputs >= 1virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) constViolation of the requirment that all input tensors must have the same data type.Invalid data type for split tensor int64_t onnxruntime::GetScalarSplitInput(const Tensor&)onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) constvoid onnxruntime::GetSplitSizesInput(const Tensor&, InlinedVector&)Invalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })Split should be > 0split_scalar > 0split_size_sum () != split_dim_size (dft_bluestein_z_chirpdiscrete_fourier_transformsignal shape must end in 1 (real) or 2 (real, imaginary).onnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = float; U = float]signal_components == 1 || signal_components == 2If both frame_length and window are set, then the size of the window must be equal to the frame_length.Ensure that the dft size is smaller than the signal.onnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = float; U = std::complex]Unsupported 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.virtual onnxruntime::common::Status onnxruntime::STFT::Compute(onnxruntime::OpKernelContext*) constonnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = double; U = double]onnxruntime::common::Status onnxruntime::short_time_fourier_transform(OpKernelContext*, bool, bool) [with T = double; U = std::complex]frame_length == window_lengthwindow_size < signal_sizeshort_time_fourier_transformdft_length must be a scalar value.dft_length must be greater than zero.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.onnxruntime::common::Status onnxruntime::discrete_fourier_transform(OpKernelContext*, int64_t, bool, bool)N11onnxruntime4STFTEN11onnxruntime3DFTEN11onnxruntime15SplitToSequenceEN11onnxruntime17SequenceConstructEN11onnxruntime13SequenceEraseEN11onnxruntime14SequenceInsertEN11onnxruntime13SequenceEmptyEN11onnxruntime10SequenceAtEN11onnxruntime14SequenceLengthEN11onnxruntime10ConcatBaseEN11onnxruntime18ConcatFromSequenceEZN11onnxruntime10IAllocator13MakeUniquePtrIhEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPhE_*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE11ComputeImplIhEEvRKN3gsl4spanIKfLm18446744073709551615EEERKNS6_IKiLm18446744073709551615EEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfLm18446744073709551615EEESN_SN_SN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_*ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE11ComputeImplIfEEvRKN3gsl4spanIKfLm18446744073709551615EEERKNS6_IKiLm18446744073709551615EEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfLm18446744073709551615EEESN_SN_SN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?-DT!@-DT!-DT! @@I@AffineGrid : Input theta tensor dimension is not 3AffineGrid : Invalidate size - length of size should be 4 or 5.stoullCol2ImT onnxruntime::signal::get_scalar_value_from_tensor(const onnxruntime::Tensor*) [with T = float]Attribute to is not set.onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.ccAttribute saturate is only used for cast to float 8 types.strides_.empty()dilations_.empty()pads_.empty()onnxruntime::Col2Im::Col2Im(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.hsrc and dst types must matchonnxruntime::common::Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool*, Tensor&, std::ptrdiff_t, const TensorShapeVector&, const TensorShape&, const Tensor&, std::ptrdiff_t, const TensorShapeVector&) [with EnabledDataTypes = TypeList, std::allocator > >; std::ptrdiff_t = long int; TensorShapeVector = absl::lts_20240116::InlinedVector >]/onnxruntime_src/onnxruntime/core/framework/copy.hdst.DataType() == src.DataType()/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.ccsize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.onnxruntime::common::Status onnxruntime::Col2Im::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/col2im.ccdilations_.size() == image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.pads_.size() == 2 * image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.strides_.size() == image_dim_numbervirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.ccaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()N11onnxruntime6ConcatEN11onnxruntime8CompressEN11onnxruntime6Col2ImIfEE*N11onnxruntime12_GLOBAL__N_14CastEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_ZNK11onnxruntime10AffineGridIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10AffineGridIfE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime10AffineGridIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10AffineGridIdE7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime10AffineGridIdEEN11onnxruntime10AffineGridIfEEN11onnxruntime15MelWeightMatrixEN11onnxruntime14BlackmanWindowEN11onnxruntime13HammingWindowEN11onnxruntime26VariableOutputDataTypeBaseEN11onnxruntime10HannWindowE O8>d! ? ף=> >?@@@onnxruntime::StridedCopy >(concurrency::ThreadPool*, std::__cxx11::basic_string*, const TensorShapeVector&, const TensorShape&, const std::__cxx11::basic_string*, const TensorShapeVector&)::counter.current_offset == lastsrc and dst must have same shape and not be rank 0.void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = std::__cxx11::basic_string; TensorShapeVector = absl::lts_20240116::InlinedVector >]dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()copy shape must have non-negative sizetotal_num_elements_to_copy >= 0onnxruntime::StridedCopy(concurrency::ThreadPool*, unsigned int*, const TensorShapeVector&, const TensorShape&, const unsigned int*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = unsigned int; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::StridedCopy(concurrency::ThreadPool*, long unsigned int*, const TensorShapeVector&, const TensorShape&, const long unsigned int*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = long unsigned int; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::StridedCopy(concurrency::ThreadPool*, short unsigned int*, const TensorShapeVector&, const TensorShape&, const short unsigned int*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = short unsigned int; TensorShapeVector = absl::lts_20240116::InlinedVector >]onnxruntime::StridedCopy(concurrency::ThreadPool*, unsigned char*, const TensorShapeVector&, const TensorShape&, const unsigned char*, const TensorShapeVector&)::void onnxruntime::StridedCopy(concurrency::ThreadPool*, T*, const TensorShapeVector&, const TensorShape&, const T*, const TensorShapeVector&) [with T = unsigned char; TensorShapeVector = absl::lts_20240116::InlinedVector >]last >= firsttypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long int; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = long int&; std::size_t = long unsigned int]invalid expand shapeZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime6ExpandINS_9MLFloat16EEEN11onnxruntime6ExpandIbEEN11onnxruntime6ExpandImEEN11onnxruntime6ExpandIjEEN11onnxruntime6ExpandItEEN11onnxruntime6ExpandIhEEN11onnxruntime6ExpandIlEEN11onnxruntime6ExpandIiEEN11onnxruntime6ExpandIsEEN11onnxruntime6ExpandIaEEN11onnxruntime6ExpandIdEEN11onnxruntime6ExpandIfEEZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyIhEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyItEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyImEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE0_ZN11onnxruntime11StridedCopyIjEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKS4_SC_EUlllE_ZN11onnxruntime11StridedCopyINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKSA_SI_EUlllE0_ZN11onnxruntime11StridedCopyINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEvPNS_11concurrency10ThreadPoolEPT_RKN4absl12lts_2024011613InlinedVectorIlLm6ESaIlEEERKNS_11TensorShapeEPKSA_SI_EUlllE_@@GatherElementsEyeLike : Input tensor dimension is not 2Missing/Invalid 'axis' attribute valueonnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gatherbase.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.hint64_t onnxruntime::GetIndex(size_t, const T*, int64_t) [with T = int; int64_t = long int; size_t = long unsigned int]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.ccGatherElements op: Unsupported tensor type, size:void onnxruntime::core_impl(const Tensor*, const Tensor*, Tensor*, int64_t, concurrency::ThreadPool*) [with Tin = int; int64_t = long int]GatherElements op: Out of range value in index tensorint64_t onnxruntime::GetIndex(size_t, const T*, int64_t) [with T = long int; int64_t = long int; size_t = long unsigned int]void onnxruntime::core_impl(const Tensor*, const Tensor*, Tensor*, int64_t, concurrency::ThreadPool*) [with Tin = long int; int64_t = long int]GatherElements 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 outputbilinearbicubicpadding_modereflectionpadding_mode "" not supported, expect linear, nearest or cubiconnxruntime::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.h" not supported, expect bilinear, nearest or bicubic" not supported, expect zeros, border or reflectiononnxruntime::GridSample::GridSample(const onnxruntime::OpKernelInfo&) [with T = double]for input dimension of grid dimensions must be , expect Last dimension of grid: Grid batch size grid_dims[0] == Nmode_ != Cubiconnxruntime::common::Status onnxruntime::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = double]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/grid_sample.ccstatic_cast(grid_dims.NumDimensions()) == data_dims + 2grid_dims[grid_dims.NumDimensions() - 1] == data_dimsOnly 4-D or 5-D tensor is supportedinput_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5 does not match input batch size Only support GridSample Cubic mode in 4-D cases.Only support GirdSample in 4-D or 5-D cases./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.ccindices element out of data bounds, idx= must be within the inclusive range [Gather Tind type not supported in this build.GatherNDBase PrepareForCompute: Input count mismatchvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.ccinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorindices tensor must has rank larger than 0indices tensor data type not supportedinvalid index found, index = N11onnxruntime10GridSampleIdEEN11onnxruntime10GridSampleIfEEZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_*ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_N11onnxruntime12GatherNDBaseEN11onnxruntime8GatherNDE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIhKhEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIlEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEKSK_EEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clImKmEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIhKhEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clItKtEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clIjKjEEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZZNS_L9core_implIiEEvPKNS_6TensorES6_PS4_lPS1_ENKUlPT_PT0_E_clINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEKSK_EEDaSA_SC_EUlmE_EEvS8_lOS9_lEUllE_N11onnxruntime14GatherElementsEZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_N11onnxruntime10GatherBaseEN11onnxruntime6GatherEN11onnxruntime7EyeLikeEZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE0_ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE_ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE0_konnxruntime::common::Status onnxruntime::GridSample::Compute(onnxruntime::OpKernelContext*) const [with T = float]Failed to obtain detect_positiveonnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.ccFailed to obtain detect_negativeNull input ptrOneHotUnable to get an allocator/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.honnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false]onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true]X input is required!failed to get first output!onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = float]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cconnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = long int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = int]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext*) const [with T = bool]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.ccNo valid axes are specified. This is not handled now.constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = absl::lts_20240116::InlinedVector >; _Dp = std::_Optional_base >, false, false>]ComputeMeanVarianceNormalization2DX and Y must both have M * N elements.N11onnxruntime8OneHotOpIlNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEEN11onnxruntime8OneHotOpIfllEEN11onnxruntime8OneHotOpIlllEEN11onnxruntime7NonZeroIfEEN11onnxruntime7NonZeroIlEEN11onnxruntime7NonZeroIiEEN11onnxruntime7NonZeroIhEEN11onnxruntime7NonZeroIbEEN11onnxruntime25MeanVarianceNormalizationEN11onnxruntime5IsNaNINS_14Float8E5M2FNUZEEEN11onnxruntime5IsNaNINS_10Float8E5M2EEEN11onnxruntime5IsNaNINS_14Float8E4M3FNUZEEEN11onnxruntime5IsNaNINS_12Float8E4M3FNEEEN11onnxruntime5IsNaNINS_9MLFloat16EEEN11onnxruntime5IsNaNIdEEN11onnxruntime5IsNaNIfEEN11onnxruntime5IsInfEN11onnxruntime10IdentityOpILb0EEEN11onnxruntime10IdentityOpILb1EEEZNK11onnxruntime10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10GridSampleIfE7ComputeEPNS_15OpKernelContextEEUllE_ZNK11onnxruntime10GridSampleIdE7ComputeEPNS_15OpKernelContextEEUllE0_ZNK11onnxruntime10GridSampleIdE7ComputeEPNS_15OpKernelContextEEUllE_@@?@  ||||||||@p@@?@Invalid 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 elementsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = Eigen::internal::TensorBlockScratchAllocator::Allocation; _Alloc = std::allocator::Allocation>; reference = Eigen::internal::TensorBlockScratchAllocator::Allocation&; size_type = long unsigned int]Depth is negative./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.ccreflectedgewrapInvalid 'mode' attribute valueonnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/padbase.hInvalid 'pads' attribute valuePads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.ccpads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Axes tensor should be a 1D tensor Pads tensor size should be equal to twice the input dimension count Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorvalue_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, MLDataType)Input tensor has no dimensionsonnxruntime::common::Status onnxruntime::PadImpl(OpKernelContext*, const PadsVector&, const PadsVector&, const Mode&, T) [with T = unsigned char; PadsVector = absl::lts_20240116::InlinedVector >]'pads' has wrong number of valuesaxes_tensor_dims.size() == 1pads_size == 2 * data_rankdata_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZeroN11onnxruntime7PadBaseEN11onnxruntime3PadEN11onnxruntime8OneHotOpIlfiEEN11onnxruntime8OneHotOpIlffEEN11onnxruntime8OneHotOpIlifEEN11onnxruntime8OneHotOpIfffEEN11onnxruntime8OneHotOpIiffEEN11onnxruntime8OneHotOpIifiEEN11onnxruntime8OneHotOpIlflEEN11onnxruntime8OneHotOpIfNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElEE>. Value must be in range [0,Invalid sequence length: Attribute shape is not set.onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h. Must be 0 or 1Invalid batch_axis of batch_axis < 2Invalid time_axis of time_axis < 2batch_axis != time_axisonnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.hinfo.GetAttr("batch_axis", &batch_axis).IsOK()info.GetAttr("time_axis", &time_axis).IsOK()time_axis and batch_axis must have different values but both are gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int]sequence_lens shape must be {batch_size}. Got:gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = short int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int]virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc. batch_size=Unknown tensor type of 6Lfʥ쨜P쪜|L6L6LF onnxruntime::common::Status onnxruntime::PadImpl(OpKernelContext*, const PadsVector&, const PadsVector&, const Mode&, T) [with T = unsigned int; PadsVector = absl::lts_20240116::InlinedVector >]onnxruntime::common::Status onnxruntime::PadImpl(OpKernelContext*, const PadsVector&, const PadsVector&, const Mode&, T) [with T = long unsigned int; PadsVector = absl::lts_20240116::InlinedVector >]A shape tensor must be a vector tensor.virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) constshapeTensor->Shape().NumDimensions() == 1ParseScalesData/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsamplebase.hscales size should be greater than 0.all values in axes should be less than rank of the datamode attribute is . It can only be (default) or antialiasUpsampleBasestretchkeep_aspect_ratio_policynot_largernot_smallerkeep_aspect_ratio of [] is not supported!extrapolation_valuepytorch_half_pixeltf_half_pixel_for_nntf_crop_and_resizehalf_pixel_symmetriccoordinate_transform_mode:[yet this opset round_prefer_floorround_prefer_ceilnearest_mode:[cubic_coeff_aexclude_outside and anti-aliasing is set to onnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo&)info.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string&)when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)onnxruntime::AspectRatioPolicy onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicy(const std::string&)onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string&)`tf_half_pixel_for_nn` is deprecated since opset 13, model uses the deprecated attributeonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string&)exclude_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 _^__ikkkkN11onnxruntime17ReverseSequenceOpEN11onnxruntime8UpsampleIhEEN11onnxruntime6ResizeIhEEN11onnxruntime8UpsampleIaEEN11onnxruntime6ResizeIaEEN11onnxruntime8UpsampleIiEEN11onnxruntime6ResizeIiEEN11onnxruntime12UpsampleBaseEN11onnxruntime8UpsampleIfEEN11onnxruntime6ResizeIfEEN11onnxruntime7ReshapeEN11onnxruntime9Reshape_1E addmulonnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) [with EnabledDataTypes = onnxruntime::TypeList, std::allocator > >]/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.ccScatterinput tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. , indices shape: updates shape: , data shape: 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'.ScatterDataScatterElements 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'.data type is different from updates typeIndices and updates must have the same rankIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=Indices type is not supported. vs . Input rank=Indices dim= at pos= is greater than input dim=/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = N11onnxruntime5ShapeEZNK11onnxruntime23ScatterNDDispatchTargetINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIbEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetINS_8BFloat16EEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetINS_9MLFloat16EEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIhEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIaEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetItEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIsEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIjEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIiEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetImEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIlEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIdEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_ZNK11onnxruntime23ScatterNDDispatchTargetIfEclEPNS_15OpKernelContextEPNS_11concurrency10ThreadPoolENS_9ScatterND9ReductionEEUlllE0_N11onnxruntime9ScatterNDEN11onnxruntime7ScatterINS_8TypeListIJfdlmijstahNS_9MLFloat16ENS_8BFloat16EbNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEEEESpaceDepth ops require a 4-D input. Provided rank: DepthToSpace requires input depth to be a multiple of (block_size * blok_size)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccUnsupported input type in DepthToSpace op: SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeUnsupported input type in SpaceToDepth op: TileMissing or invalid starts and ends attributeonnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.hhas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes!has_axes || attr_axes_.size() == attr_starts_.size()Unexpected element size of output == output_endvoid* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*)onnxruntime::SliceImpl >(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&):: >&)>/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.cconnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>onnxruntime::SliceImpl(OpKernelContext*, const Tensor&, SliceOp::PrepareForComputeMetadata&)::&)>Attribute blocksize is not set.onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.hinfo.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo&)blocksizeInvalid value in 'split' attribute. All values must be > 0onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&, uint32_t)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.hstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })If 'num_outputs' is specified, the 'split' input should not be provided.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' tensorTile doesn't have an implementation yet for the type: virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc'axes' has duplicates'axes' has an axis outside of the tensor dimension countCannot slice scalarsFillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D array'step' value cannot be 0Starts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got The split tensor must be a vector tensor.virtual onnxruntime::common::Status onnxruntime::SplitImpl::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.ccsplit_tensor->Shape().NumDimensions() == 1. Size of dimension being split is Input cannot be split evenly on selected axis. Input shape=Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was Invalid num_outputs value of Axis= NumOutputs= Input shape=Axes input is nullaxes_tensor != nullptrDimension of input must be 1 instead of . shape=input_shape[i] == 1virtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.hstatic onnxruntime::TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShapeVector&)N11onnxruntime4TileEN11onnxruntime11SqueezeBaseEN11onnxruntime7SqueezeEN11onnxruntime8Split_18EN11onnxruntime10Split_1_13EN11onnxruntime9SplitBaseEN11onnxruntime9SplitImplEN11onnxruntime12DepthToSpaceEN11onnxruntime14SpaceDepthBaseEN11onnxruntime12SpaceToDepthEN11onnxruntime7Slice10EN11onnxruntime9SliceBaseEN11onnxruntime6Slice1EN11onnxruntime4SizeE*>std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; const_reference = const std::vector&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = long int; _Alloc = std::allocator; const_reference = const long int&] is outside range. is repeated.onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.hv >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value onnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.hinfo.GetAttr("upper", &temp).IsOK()k should be a 1-D or 0-D tensor.virtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.ccIsScalarOr1ElementVector(k)Missing/Invalid 'axes' attribute valueonnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.hinfo.GetAttrs("axes", axes_).IsOK()Transpose not implemented for empty tensors.void onnxruntime::DoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const std::string*, std::string*)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc(local_source >= source) && (local_source < source + num_blocks)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)bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = long unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char](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*) [with T = unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = short unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span&, const uint8_t*, uint8_t*) [with T = unsigned char; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char]Transpose of element size not supported in this build. Size=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)num_axes > 0Mismatched data types between input and output Tensors. virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const does not align with rank of input data: perm size: does not match input rank: [ perm: onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, Prepare&) const/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.ccAn axes tensor must be a scalar or a 1-D tensor.axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensorUniqueUnsupported tensor type of 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: N11onnxruntime13UnsqueezeBaseEN11onnxruntime9UnsqueezeEN11onnxruntime6UniqueEN11onnxruntime5TriluEN11onnxruntime13TransposeBaseEN11onnxruntime9TransposeE Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: 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./onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.ccRank of input and output tensor should be same.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 modeBaseComputeUpsampleNearestInvalid roi input index.Only one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.N11onnxruntime23BilinearParamsAntiAliasIiEEN11onnxruntime24TriLinearParamsAntiAliasIfEEN11onnxruntime21FilterParamsAntiAliasIfEEN11onnxruntime22BiCubicParamsAntiAliasIfEEN11onnxruntime24TriLinearParamsAntiAliasIiEEN11onnxruntime21FilterParamsAntiAliasIiEEN11onnxruntime22BiCubicParamsAntiAliasIiEE@A@J @@constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = float; long unsigned int _Nm = 4; reference = float&; size_type = long unsigned int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/arrayZN11onnxruntime19HandleExtrapolationIhiEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIfLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIfLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIfLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIfLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIfEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIiLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIiLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIiLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIiLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIiEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIaEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIaLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIaLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIaLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIaLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIaEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime20NhwcUpsampleBilinearIhLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIhLb0EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime20NhwcUpsampleBilinearIhLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime27NhwcUpsampleBilinearIntegerIhLb1EEEviiiiiiffRKSt6vectorIfSaIfEEfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime16UpsampleBilinearIhEEviiiiiiffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEERKPFfffffffEPNS_11concurrency10ThreadPoolEEUllE_N11onnxruntime23BilinearParamsAntiAliasIfEE55Input contains invalid utf8 charsWCHAR_T__pos < this->_M_lenbasic_string_view::substrconstexpr const std::basic_string_view<_CharT, _Traits>::value_type& std::basic_string_view<_CharT, _Traits>::operator[](size_type) const [with _CharT = char; _Traits = std::char_traits; const_reference = const char&; size_type = long unsigned int]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/string_view%s: __pos (which is %zu) > __size (which is %zu)GetAttrInvalid regex pattern: re_.ok()/onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.hT onnxruntime::OpNodeProtoHelper::GetAttr(const std::string&) const [with T = std::__cxx11::basic_string; Impl_t = onnxruntime::ProtoHelperNodeContext; std::string = std::__cxx11::basic_string]onnxruntime::RegexFullMatch::RegexFullMatch(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/text/regex_full_match.ccSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedFailed to construct locale with name::Please, install necessary language-pack-XX and configure localesonnxruntime::string_normalizer::Locale::Locale(const std::string&)::/onnxruntime_src/onnxruntime/core/providers/cpu/text/string_normalizer.ccEmpty stopwords not allowed!sw.empty()p.secondwstr != wconv_errorattribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&)attribute case_change_action is not setattribute case_change_action has invalid valueDuplicate stopwords not allowedStopword contains invalid utf8 chars/build/Release/_deps/re2-src/re2/re2.ccInvalid RE2: N11onnxruntime7contrib8CropBaseEN11onnxruntime7contrib4CropIfEEN11onnxruntime11StringSplitEN11onnxruntime16StringNormalizerEN11onnxruntime12StringConcatEN11onnxruntime14RegexFullMatchEN11onnxruntime5WhereINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime5WhereIdEEN11onnxruntime5WhereIfEEN11onnxruntime5WhereIlEEN11onnxruntime5WhereIiEEN11onnxruntime5WhereIhEEZN11onnxruntime28ComputeInterpolationAtLevel1IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel1IhiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime19HandleExtrapolationIffEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IffEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime19HandleExtrapolationIifEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IifEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime19HandleExtrapolationIaiEEvllllfN3gsl4spanIT_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime28ComputeInterpolationAtLevel2IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime28ComputeInterpolationAtLevel2IaiEEvlllllN3gsl4spanIKT_Lm18446744073709551615EEENS2_IS3_Lm18446744073709551615EEERKNS_21FilterParamsAntiAliasIT0_EERKNS_25FilterParamsBaseAntiAliasIS8_EEPNS_11concurrency10ThreadPoolEEUllE_ZN11onnxruntime10IAllocator13MakeUniquePtrIaEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmbPNS_6StreamES4_IFvRSB_RNS_11synchronize12NotificationEEEEUlPaE_Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder () needs to be greater than or equal to the leftBorder (/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h input dimensions instead) + bottomBorder (Input's height () + rightBorder (Input's width () + scale_[0] () + scale_[1] (Null crop_size_ptrNumber of dimensions for crop size should be exactly 1Bias size () does not match the number of channels (CropAndResizeExpandDimsTransposeMatMulSparseToDenseMatMul specified. It should be either bilinear or nearestonnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("scale", &scale_).IsOK()onnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo&) [with T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()An axis tensor must be a scalar tensor.virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.haxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAxis must be within range []. Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()Create'is defined.No attribute with name:'unknown kernel type/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.ccAttribute name and type don't match for '/onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.h/onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float] vs inner_B: Expecting 2xValues == indicesCOO k index: COO m index: Outer size must be M + 1/onnxruntime_src/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: Expecting COO 2-D indices shapeCOO indices must be 2-D, got: is out of bounds of lhs_right: is out of bounds of out_left: Expecting the same number NNZ == size of Inner indicesCurrently support only COO and CSR(x64) formatsN11onnxruntime7contrib19SparseToDenseMatMulEN11onnxruntime7contrib9LayerNormILb1EEEN11onnxruntime7contrib9LayerNormILb0EEE*ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime7contrib7InverseEN11onnxruntime7contrib11ImageScalerIfEEN11onnxruntime8functors10ScaledTanhIfEEN11onnxruntime8functors18ParametricSoftplusIfEEN11onnxruntime8functors4SeluIfEEN11onnxruntime8functors15ThresholdedReluIfEEN11onnxruntime8functors4TanhIfEEN11onnxruntime8functors8SoftsignIfEEN11onnxruntime8functors7SigmoidIfEEN11onnxruntime8functors4ReluIfEEN11onnxruntime8functors8SoftplusIfEEN11onnxruntime8functors9LeakyReluIfEEN11onnxruntime8functors11HardSigmoidIfEEN11onnxruntime8functors3EluIfEEN11onnxruntime8functors4CeluIfEEN11onnxruntime7contrib9FusedGemmIfEEN11onnxruntime7contrib14FusedConvFloatEN11onnxruntime7contrib10ExpandDimsEN11onnxruntime7contrib6AffineIfEEN11onnxruntime7contrib5ScaleIfEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPKiPS1_EUllE_EEvSM_lOS8_lEUllE_N11onnxruntime7contrib13CropAndResizeIfEEextrapolation_va/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.ccInvalid assumption of output element sizesizeof(uint32_t) == output_element_byteskeysinput_num_bytes % 4 == 0invalid channel countchannels_ > 0onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.hinfo.GetAttr("channels", &channels_).IsOK()info.GetAttr("channels_last", &channels_last_).IsOK()virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc(X_shape[1] % MlasNchwcGetBlockSize()) == 0X_shape.size() == 4onnxruntime::contrib::ReorderInput::ReorderInput(const onnxruntime::OpKernelInfo&)do_rotaryrotary_embedding_dimqkv_hidden_sizespast_present_share_bufferonnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&, bool)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.hinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Second input of MatMulFpQ4 must be a 1D blob!virtual onnxruntime::common::Status onnxruntime::contrib::MatMulFpQ4::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_fpq4.ccblob_shape.NumDimensions() == 1Right hand side of MatMulFpQ4 must be a 2D matrix!Operator MatMulFpQ4 not yet supported on this hardware platform.Quantized and packed blob size differ from expected!std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_Q4_GEMM_DATA_PARAMS; _Alloc = std::allocator; reference = MLAS_Q4_GEMM_DATA_PARAMS&; size_type = long unsigned int]b_shape.NumDimensions() == 2buf_size > 0(size_t)blob_len == buf_sizevirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) constX_rank == 4(channels % 4) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) constchannels_ <= X_shape[1]onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) constX_shape.NumDimensions() == 4MatMulFpQ4MaxpoolWithMaskMurmurHash3QAttention/onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&)pool_attrs_.kernel_shape.size() == 2scales_.size() == 4' for NCHWc UpsampleUnsupported mode 'onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&)info.GetAttrs("scales", scales_).IsOK()scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode 'transformation_mode_ == TransformationMode::ASYMMETRIConnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&)ApplyAttention/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.hExpect to have present state output when past state input is givenonnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.cc4D mask in attention cpu kernel is not supported/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccinput zero point must be a scalar or 1D tensor of size 1.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator; reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&; size_type = long unsigned int]Input W_zero_point, 4*W_scaleDynamicQuantizeLSTM : Weight point must be constantRecurrent} for per-tensor/layer quantization or shape {} for per-channel quantization. Actual:Weight zero point must be zeroN11onnxruntime7contrib19DynamicQuantizeLSTMEZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolEEUlllE_ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiN3gsl4spanIKlLm18446744073709551615EEES4_biiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolES6_EUlllE0_ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiN3gsl4spanIKlLm18446744073709551615EEES4_biiiiiS6_S6_S4_S4_PNS_11concurrency10ThreadPoolES6_EUlllE_N11onnxruntime7contrib13AttentionBaseEN11onnxruntime7contrib16AttentionCPUBaseEN11onnxruntime7contrib10QAttentionIfEE*ZNK11onnxruntime7contrib13NchwcUpsample7ComputeEPNS_15OpKernelContextEEUllE_*ZNK11onnxruntime7contrib12ReorderInput7ComputeEPNS_15OpKernelContextEEUllE_N11onnxruntime7contrib16NchwcAveragePoolEN11onnxruntime7contrib13NchwcPoolBaseEN11onnxruntime7contrib12NchwcMaxPoolEN11onnxruntime7contrib9NchwcConvEN11onnxruntime7contrib12ReorderInputEN11onnxruntime7contrib13NchwcUpsampleEN11onnxruntime7contrib13ReorderOutputEN11onnxruntime7contrib11MurmurHash3EN11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEEN11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEEN11onnxruntime7contrib15MaxpoolWithMaskEN11onnxruntime7contrib10MatMulFpQ4Eunknown errERR Pack bad n=%d(%d) .maskflag.zeroflag.maskendComputeCommon/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccMatmulInteger : b zero point is not validonnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext*, const uint8_t*, const onnxruntime::TensorShape&, float, uint8_t, bool, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*) constIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)block_sizeonnxruntime::contrib::MatMulBnb4::MatMulBnb4(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_bnb4.ccStatus::OK() == info.GetAttr("K", &K_)Status::OK() == info.GetAttr("N", &N_)Status::OK() == info.GetAttr("block_size", &block_size_)Status::OK() == info.GetAttr("quant_type", &quant_type_)Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported.quant_type_ == FP4 || quant_type_ == NF4onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_integer16.ccA != nullptr && B != nullptrT onnxruntime::OpNodeProtoHelper::GetAttr(const std::string&) const [with T = long int; Impl_t = onnxruntime::ProtoHelperNodeContext; std::string = std::__cxx11::basic_string]bitsnbits_ == 4Only 4b quantization is supported for MatMulNBits op, additional bits support is planned.onnxruntime::contrib::MatMulNBits::MatMulNBits(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.ccMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) constIsScalarOr1ElementVector(a_zero_point_tensor)MatMulBnb4MatMulInteger16MatMulNBitsvoid onnxruntime::contrib::DequantizeBlockwiseBnb4(T*, const uint8_t*, const T*, int32_t, int32_t, int32_t, int32_t, onnxruntime::concurrency::ThreadPool*) [with T = float; uint8_t = unsigned char; int32_t = int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dequantize_blockwise_bnb4.hquant_type == FP4 || quant_type == NF4only block size 16, 32, 64, 128, 256 are supported.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = NS_SQNBITS_GEMM_DATA_PACKED_PARAMS; _Alloc = std::allocator; reference = NS_SQNBITS_GEMM_DATA_PACKED_PARAMS&; size_type = long unsigned int]constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = {anonymous}::Operations; long unsigned int _Nm = 2; const_reference = const {anonymous}::Operations&; size_type = long unsigned int]N6bestla6kernel12jit_injector16eltwise_injectorEN5Xbyak13CodeGeneratorEN5Xbyak9AllocatorEN5Xbyak13MmapAllocatorEN5Xbyak9CodeArrayEN5Xbyak5ErrorEZN11onnxruntime10IAllocator13MakeUniquePtrISt4byteEESt10unique_ptrIT_St8functionIFvPS4_EEESt10shared_ptrIS0_EmbPNS_6StreamES5_IFvRSC_RNS_11synchronize12NotificationEEEEUlPS2_E_N6bestla7storage4gemm15IActivationBaseEN6bestla7storage4gemm21IActivationKBlockBaseEN6bestla8parallel10IThreadingEN6bestla7storage4gemm22StorageQuantActivationEN6bestla7storage4gemm13StorageReduceEN6bestla7storage4gemm25StorageWeightKBlockNFloatEN6bestla7storage4gemm19StoragePackedWeightEN6bestla12ORTThreadingEN6bestla7storage13ISerializableEN6bestla7storage4gemm11IWeightBaseEN6bestla7storage13ISerialObjectEN6bestla7storage4gemm17IWeightKBlockBaseEN6bestla7storage20ObjectOptionalBufferILi64EEEN6bestla7storage4gemm21ObjectQuantCorrectionEN6bestla7storage19ObjectAlignedBufferILi64EEEN6bestla7storage4gemm27StorageWeightKBlockNIntegerEN11onnxruntime7contrib11MatMulNBitsEN11onnxruntime7contrib15MatMulInteger16IssiEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi256ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi256ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi128ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi128ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi64ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi64ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi32ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi32ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi16ELi1EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib23DequantizeBlockwiseBnb4IfLi16ELi0EEEvPT_PKhPKS5_iiPS1_EUllE_EEvSB_lOS5_lEUllE_N11onnxruntime7contrib10MatMulBnb4EN11onnxruntime7contrib20MatMulIntegerToFloatEN11onnxruntime7contrib24MatMulIntegerToFloatBaseEN11onnxruntime7contrib21DynamicQuantizeMatMulEbad addressingcode is too bigbad scaleesp can't be indexbad combinationbad size of registerimm is too bigbad alignlabel is redefinedlabel is too farlabel is not foundcode is not copyablebad parametercan't protectoffset is too bigMEM size is not specifiedbad mem sizebad st combinationover local labelunder local labelcan't allocbad protect modebad pNumbad tNumbad vsib addressingcan't convertlabel is not set by L()label is already set by L()bad label stringerr munmapopmask is already setrounding is already setk0 is invalidevex is invalidinvalid broadcastinvalid opmask with memoryinvalid zeroinvalid rip in AutoGrowinvalid mib addressx2APIC is not supportedsame regs are invalidcan't use 64bit disp(use (void*))T_SHORT is not supported in AutoGrowsae(suppress all exceptions) is invalider(embedded rounding) is invalid920k2ʾM?5=qx=$>|>:>>?9??;*??>?*>>**B@b@fGemmRunThread Block:(%d,%d) Thread in use:%d of %d, Nx%d Cache Size:%zu used:%zu GEMM MStep:%d NStep:%d KStep:%d .nloop.colloop.proc48.rowloop1.colend.proc32.rowloop2.rowloop34h;P;46`O]TS$R].kloop.kbloop.unkbloop.kendN6bestla5xbyak13JitAvx512vnniEN6bestla8parallel4gemm13SchedulerBaseINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEEEN6bestla4gemm4code6kblock15Avx512vnniN16P4ILi48ELi4EEEN6bestla8parallel4gemm16SchedulerKBlockSINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEEEN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EEEN6bestla5xbyak10JitAmxtileEN6bestla5xbyak10JitAmxint8EN6bestla8parallel4gemm13SchedulerBaseINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEEEN6bestla4gemm4code6kblock12Amxint8N16P4IaaLi48ELi16EEEN6bestla8parallel4gemm16SchedulerKBlockSINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEEEN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EEEN6bestla8parallel4gemm15SchedulerKBlockINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEEEN6bestla8parallel4gemm13SchedulerBaseINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEEEN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEL8BTLA_ISA2EEEN6bestla8parallel11Scheduler2DEN6bestla8parallel4gemm15SchedulerKBlockINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEEEN6bestla8parallel4gemm13SchedulerBaseINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEEEN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEL8BTLA_ISA4EEEN6bestla5xbyak7JitBaseEN6bestla5xbyak6JitAvxEN6bestla5xbyak7JitAvx2EN6bestla5xbyak10JitAvx512fEN6bestla6kernel3jit10DequanS8FP18MicroKernelAVX512FEA:0    constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = bestla::gemm::code::Avx512fN16P1<48, 8>; long unsigned int _Nm = 8; reference = bestla::gemm::code::Avx512fN16P1<48, 8>&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = bestla::gemm::code::kblock::Avx512vnniN16P4<48, 4>; long unsigned int _Nm = 4; reference = bestla::gemm::code::kblock::Avx512vnniN16P4<48, 4>&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = bestla::gemm::code::Avx2N8P1<24, 4>; long unsigned int _Nm = 4; reference = bestla::gemm::code::Avx2N8P1<24, 4>&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = bestla::gemm::code::kblock::Amxint8N16P4; long unsigned int _Nm = 1; reference = bestla::gemm::code::kblock::Amxint8N16P4&; size_type = long unsigned int]N6bestla4gemm4code12AmxConfigureEN6bestla6kernel3jit18JitMemcpy2DAvx512fEN6bestla6kernel3jit15JitMemcpy2DAvx2EN6bestla4gemm4code8Avx2N8P1ILi24ELi4EEEN6bestla4gemm4code12Avx512fN16P1ILi48ELi8EEE*ZNK6bestla12ORTThreading12parallel_forERKSt8functionIFviEEEUllE_ZN6bestla6kernel3jit18JitMemcpy2DAvx512f8generateEiRSt6vectorINS0_12jit_injector16eltwise_injectorESaIS5_EENS1_17DataConvertConfigEEUlNS1_16MemcpyStoreParamIN5Xbyak3ZmmEEEE1_ZN6bestla6kernel3jit18JitMemcpy2DAvx512f8generateEiRSt6vectorINS0_12jit_injector16eltwise_injectorESaIS5_EENS1_17DataConvertConfigEEUlNS1_16MemcpyStoreParamIN5Xbyak3ZmmEEEE_ZN6bestla6kernel3jit15JitMemcpy2DAvx28generateEiRSt6vectorINS0_12jit_injector16eltwise_injectorESaIS5_EENS1_17DataConvertConfigEEUlNS1_16MemcpyStoreParamIN5Xbyak3YmmEEEE1_ZN6bestla6kernel3jit15JitMemcpy2DAvx28generateEiRSt6vectorINS0_12jit_injector16eltwise_injectorESaIS5_EENS1_17DataConvertConfigEEUlNS1_16MemcpyStoreParamIN5Xbyak3YmmEEEE_N6bestla5xbyak10JitAvxvnniEN6bestla8parallel4gemm13SchedulerBaseINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEEEN6bestla4gemm4code6kblock11AvxvnniN8P4ILi24ELi2EEEN6bestla8parallel4gemm16SchedulerKBlockSINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEEEN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEL8BTLA_ISA3EEE  ==@000    @@@@@@@@constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = bestla::gemm::code::kblock::AvxvnniN8P4<24, 2>; long unsigned int _Nm = 2; reference = bestla::gemm::code::kblock::AvxvnniN8P4<24, 2>&; size_type = long unsigned int].read.end.unkloopZZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEL8BTLA_ISA2EE17packNbitsWeightQ4EiibPKhiPKfS9_PvPNS_8parallel10IThreadingEENKUlvE_clEvEUliE_ZZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEL8BTLA_ISA4EE17packNbitsWeightQ4EiibPKhiPKfS9_PvPNS_8parallel10IThreadingEENKUlvE_clEvEUliE_ZZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEL8BTLA_ISA3EE17packNbitsWeightQ4EiibPKhiPKfS9_PvPNS_8parallel10IThreadingEENKUlvE_clEvEUliE_ZZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE17packNbitsWeightQ4EiibPKhiPKfS9_PvPNS_8parallel10IThreadingEENKUlvE_clEvEUliE_ZZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE17packNbitsWeightQ4EiibPKhiPKfS9_PvPNS_8parallel10IThreadingEENKUlvE_clEvEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm16SchedulerKBlockSINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEEENS_7wrapper4gemm17LauncherIntKBlockIL8BTLA_ISA3ES6_NS_10prologue_a4gemm27ActivationF32KBlockQuantizeENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm24AccumulatorWriteBackFp32EEEEEvRT0_RKNSM_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla10prologue_a4gemm24ActivationKBlockQuantizeINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEL8BTLA_ISA3EfE8quantizeERKNS1_29ParamActivationKBlockQuantizeIfEEiiPNS_8parallel10IThreadingEEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm16SchedulerKBlockSINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEEENS_7wrapper4gemm17LauncherIntKBlockIL8BTLA_ISA5ES6_NS_10prologue_a4gemm27ActivationF32KBlockQuantizeENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm24AccumulatorWriteBackFp32EEEEEvRT0_RKNSM_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla10prologue_a4gemm24ActivationKBlockQuantizeINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EfE8quantizeERKNS1_29ParamActivationKBlockQuantizeIfEEiiPNS_8parallel10IThreadingEEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm16SchedulerKBlockSINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEEENS_7wrapper4gemm17LauncherIntKBlockIL8BTLA_ISA7ES6_NS_10prologue_a4gemm27ActivationF32KBlockQuantizeENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm24AccumulatorWriteBackFp32EEEEEvRT0_RKNSM_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla10prologue_a4gemm24ActivationKBlockQuantizeINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EfE8quantizeERKNS1_29ParamActivationKBlockQuantizeIfEEiiPNS_8parallel10IThreadingEEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm13SchedulerBaseINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEEENS_7wrapper4gemm12LauncherBaseIL8BTLA_ISA2ES6_NS_10prologue_a4gemm14ActivationBaseENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm24AccumulatorWriteBackFp32EEEEEvRT0_RKNSM_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm15SchedulerKBlockINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEEENS_7wrapper4gemm14LauncherKBlockIL8BTLA_ISA2ES6_NS_10prologue_a4gemm23ActivationKBlockBaseF32ENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm21CompFp32BlockEpilogueENSJ_24AccumulatorWriteBackFp32EEEEEvRT0_RKNSN_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla10prologue_a4gemm20ActivationKBlockBaseINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEL8BTLA_ISA2EfE6reduceERKNS1_25ParamActivationKBlockBaseIfEEiiiPNS_8parallel10IThreadingEEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm13SchedulerBaseINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEEENS_7wrapper4gemm12LauncherBaseIL8BTLA_ISA4ES6_NS_10prologue_a4gemm14ActivationBaseENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm24AccumulatorWriteBackFp32EEEEEvRT0_RKNSM_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla8parallel7GemmRunINS0_4gemm15SchedulerKBlockINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEEENS_7wrapper4gemm14LauncherKBlockIL8BTLA_ISA4ES6_NS_10prologue_a4gemm23ActivationKBlockBaseF32ENS_10prologue_b4gemm20WeightKBlockNIntegerENS_8epilogue4gemm21CompFp32BlockEpilogueENSJ_24AccumulatorWriteBackFp32EEEEEvRT0_RKNSN_5ParamEPNS0_10IThreadingEEUliE_ZN6bestla10prologue_a4gemm20ActivationKBlockBaseINS_4gemm16SCoreRowNAvx512fILi48ELi8EEEL8BTLA_ISA4EfE6reduceERKNS1_25ParamActivationKBlockBaseIfEEiiiPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm22ICoreRowNAvxvnniKBlockILi24ELi2EEEL8BTLA_ISA3EE12unpackWeightEiiPNS_7storage4gemm27StorageWeightKBlockNIntegerEPfiPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE12unpackWeightEiiPNS_7storage4gemm27StorageWeightKBlockNIntegerEPfiPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE12unpackWeightEiiPNS_7storage4gemm27StorageWeightKBlockNIntegerEPfiPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm13SCoreRowNAvx2ILi24ELi4EEEL8BTLA_ISA2EE12unpackWeightEiiPNS_7storage4gemm27StorageWeightKBlockNIntegerEPfiP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a10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE6reduceIfEEviiiPKfiPT_iPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE14compressWeightEiiPKaiPa10BTLA_DTYPEPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE13reorderWeightEiiPKaiPaPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE2_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE1_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE0_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm25ICoreRowNAvx512vnniKBlockILi48ELi4EEEL8BTLA_ISA5EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE6reduceINS_5utils4bf16EEEviiiPKfiPT_iPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE6reduceIfEEviiiPKfiPT_iPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE14compressWeightEiiPKaiPa10BTLA_DTYPEPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE13reorderWeightEiiPKaiPaPNS_8parallel10IThreadingEEUliE_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE2_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE1_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE0_ZN6bestla10prologue_b4gemm20WeightKBlockNIntegerINS_4gemm24ICoreRowNAmxint8SSKBlockILi48ELi16EEEL8BTLA_ISA7EE27setTransposeQuantCorrectionEiiPKaPKfPNS_7storage4gemm27StorageWeightKBlockNIntegerEPNS_8parallel10IThreadingEEUliE_0.NOZPNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN6bestla6device9CpuDeviceC4EvEUliE_iEEEEEEAQLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!Ѯ.׮Ѯ׮u׮f׮Z׮vdUIERR Pack::init bad n=%d QEmbedLayerNormalizationQLinearLeakyReluQlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; LookupTableArrayTransformer = std::function]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_lookup_table.ccIsScalarOr1ElementVector(tensor_x_scale)QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; LookupTableArrayTransformer = std::function]Each input must be (tensor, scale, zero_point) tuple!onnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_concat.ccinput_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @tensor_x_scale->IsDataType()Wrong input type encountered for zero point input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::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 @std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; const_reference = const std::vector&; size_type = long unsigned int]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccInvalid input shape. Only N can be zero. Got:onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]input_shape.Size() > 0 || N == 0onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]Input x_scale must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_global_average_pool.ccinput x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_x_zero_point)input y_scale must be a scalar or 1D tensor of size 1input y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)input index out of range/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qembed_layer_norm.cconnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(onnxruntime::OpKernelContext*) const [with T8Bits = signed char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_pool.ccQLinear Pooling unsupported pooling size!onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(onnxruntime::OpKernelContext*) const [with T8Bits = unsigned char]ZN11onnxruntime7contrib16dequantize_arrayIhEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib16dequantize_arrayIaEEvlPKT_fS2_PfPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib17QLinearPool3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIhNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc3DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc2DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib17QLinearPool1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib21QLinearPoolNhwc1DTaskIaNS_11AveragePoolEEEN11onnxruntime7contrib18QLinearAveragePoolEZN11onnxruntime7contrib23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ZN11onnxruntime7contrib23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIhEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIhEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIaEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE0_ZN11onnxruntime7contrib27ComputeQLinearGlobalAvgPoolIaEENS_6common6StatusEPKT_fS4_PS4_fS4_lllbPNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib24QLinearGlobalAveragePoolE*ZNK11onnxruntime7contrib13QLinearConcat7ComputeEPNS_15OpKernelContextEEUlfE_*ZN11onnxruntime7contrib13QLinearConcatC4ERKNS_12OpKernelInfoEEUlfE_N11onnxruntime7contrib13QLinearConcatE*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_*ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_N11onnxruntime7contrib10QLinearMulIhEEN11onnxruntime7contrib10QLinearMulIaEEN11onnxruntime7contrib10QLinearAddIhEEN11onnxruntime7contrib10QLinearAddIaEEZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ZN11onnxruntime7contrib14QLinearSigmoidIhEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ZN11onnxruntime7contrib14QLinearSigmoidIaEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ZN11onnxruntime7contrib16QLinearLeakyReluIhEC4ERKNS_12OpKernelInfoEEUlfE_ZN11onnxruntime7contrib16QLinearLeakyReluIaEC4ERKNS_12OpKernelInfoEEUlfE_N11onnxruntime7contrib14QLinearSigmoidIhEEN11onnxruntime7contrib14QLinearSigmoidIaEEN11onnxruntime7contrib17QLinearLookupBaseIhEEN11onnxruntime7contrib16QLinearLeakyReluIhEEN11onnxruntime7contrib17QLinearLookupBaseIaEEN11onnxruntime7contrib16QLinearLeakyReluIaEE*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfhEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib12_GLOBAL__N_115ComputeInternalIfaEENS_6common6StatusEPNS_15OpKernelContextEfEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib18EmbedLayerNormBaseEN11onnxruntime7contrib15QEmbedLayerNormIfEEN11onnxruntime7contrib11NhwcMaxPoolIhEEN11onnxruntime7contrib11NhwcMaxPoolIaEE/C #<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 match/onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.ccNode input should be a scalar. Got shape of inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)onnxruntime::contrib::QLinearWhere::QLinearWhere(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_where.ccinput_def_count == kExpected_input_countWrong input type encountered for zero point input def of x, y, ztensor_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()There must be onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = false]epsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = true]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = false]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = true]void onnxruntime::contrib::transformers::BeamSearch::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search.ccparameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperinfo.GetAttr("encoder", &proto).IsOK()info.GetAttr("decoder", &proto).IsOK()init_decoderencoderopset must be existed in attributes of QlinearSoftmaxonnxruntime::contrib::QLinearSoftmax::QLinearSoftmax(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_softmax.ccvoid onnxruntime::contrib::{anonymous}::BuildLookupTableIfFixed(const onnxruntime::OpKernelInfo&, std::vector&, size_t, bool)tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedInput string contains invalid utf8 charsstd::__cxx11::basic_string<_CharT, _Traits, _Alloc>::const_reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::operator[](size_type) const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; const_reference = const char&; size_type = long unsigned int]Input string contains invalid utf8 chars: Match contains invalid utf8 chars: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&]QLinearSoftmaxQLinearWhereTATBTCTYZTYSampleOpSkipSimplifiedLayerNormalizationTokenizerWhisperBeamSearchInput scale is not float for quantized input x @ 2virtual onnxruntime::common::Status onnxruntime::contrib::QLinearWhere::Compute(onnxruntime::OpKernelContext*) constInput scale is not float for quantized input y @ 5tensor_y_scale->IsDataType()Input scale is not float for quantized output z @ 7tensor_z_scale->IsDataType()/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/quant_gemm.ccQGemm : 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 1CheckInputsSetupSubgraphExecutionInfo should only be called once for each subgraph.virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)init_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_ == nullptrgpt_subgraph_ == nullptrSetupSubgraphExecutionInfomarkattribute mark is not setpad_valuemincharnummincharnum_ > 0separatorstokenexp!tokenexp.empty()separators must not be empty!separators.empty()Can not digest separators: Can not digest tokenexp: onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.ccattribute pad_value is not setattribute mincharnum is not setattribute mincharnum must have a positive valueEither one of the separators OR tokenexp attributes required but none is setExpecting a non-empty tokenexpmincharnum is too big for char level tokenezation!char_tokenezation_ || mincharnum_ < 2std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]N11onnxruntime7contrib12transformers22WhisperEncoderSubgraphEN11onnxruntime7contrib12transformers22WhisperDecoderSubgraphEN11onnxruntime7contrib12transformers17WhisperBeamSearchEN11onnxruntime7contrib12transformers27WhisperBeamSearchParametersEN11onnxruntime7contrib12transformers21IGenerationParametersEN11onnxruntime7contrib12transformers20BeamSearchParametersEN11onnxruntime7contrib12transformers10ISequencesEN11onnxruntime7contrib12transformers9SequencesEN11onnxruntime7contrib12transformers20ILogitsProcessorListEN11onnxruntime7contrib12transformers14IConsoleDumperEN11onnxruntime7contrib12transformers19LogitsProcessorListEN11onnxruntime7contrib12transformers22CpuTensorConsoleDumperEN11onnxruntime7contrib12transformers12GenerateBaseEN11onnxruntime11controlflow18IControlFlowKernelEN11onnxruntime7contrib12transformers10BeamSearchEN11onnxruntime7contrib12transformers17T5EncoderSubgraphEN11onnxruntime7contrib12transformers8SubgraphEN11onnxruntime7contrib12transformers17T5DecoderSubgraphEN11onnxruntime7contrib9TokenizerEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib13SkipLayerNormIdLb1EEEN11onnxruntime7contrib13SkipLayerNormIdLb0EEEN11onnxruntime7contrib13SkipLayerNormIfLb1EEEN11onnxruntime7contrib13SkipLayerNormIfLb0EEEN11onnxruntime7contrib8SampleOpIfEEN11onnxruntime7contrib5QGemmE*ZNK11onnxruntime7contrib12QLinearWhere7ComputeEPNS_15OpKernelContextEEUlfE_*ZN11onnxruntime7contrib12QLinearWhereC4ERKNS_12OpKernelInfoEEUlfE_N11onnxruntime7contrib12QLinearWhereE*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIaEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_*ZN11onnxruntime7contrib17QlinearSoftmaxCPUIhEENS_6common6StatusEmmPKT_PS4_PKffS4_PNS_11concurrency10ThreadPoolEEUlllE_N11onnxruntime7contrib14QLinearSoftmaxE @o@decoder_output_c/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_base.hGenerateNextTokenmin_lengthmax_lengthnum_beamsnum_return_sequenceslength_penalty'num_return_sequences' has to be smaller or equal to 'num_beams'./onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_t5.hparameters->sequence_length == 1, vocab_size is no_speech_token_id is out of range, it is onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_impl_whisper.hparameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizedecoder subgraph: output_cross_qk could only work with has_decoder_masked_attentiondecoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with past_present_share_bufferdecoder_subgraph_.past_present_share_buffer_Must specify input cross_qk_layer_headinput_tensor_cross_qk_layer_head != nullptronnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = onnxruntime::MLFloat16]onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const onnxruntime::FeedsFetchesManager&, const onnxruntime::FeedsFetchesManager&) [with T = onnxruntime::MLFloat16]decoder_session_stateencoder_session_stateModel type is not supported.Subgraph SessionState was not found for 'decoder' attribute.virtual onnxruntime::common::Status onnxruntime::contrib::transformers::BeamSearch::Compute(onnxruntime::OpKernelContext*) constCreateFeedsFetchesManager must be called prior to execution of graph.decoder_feeds_fetches_manager_init_run_decoder_session_stateinit_run_decoder_feeds_fetches_manager_past_present_share_buffer mode must be same for init decoder and decoder subgraphesinit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::BeamSearchParameters; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::BeamSearchParameters&]Subgraph SessionState was not found for 'encoder' attribute.encoder_feeds_fetches_manager_typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::T5EncoderSubgraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::T5EncoderSubgraph&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::contrib::transformers::WhisperEncoderSubgraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::contrib::transformers::WhisperEncoderSubgraph&]N11onnxruntime7contrib12transformers11IBeamScorerEN11onnxruntime7contrib12transformers16BeamSearchScorerEFN11onnxruntime6common6StatusEPNS_6StreamEiiiiiiPKiiPKfPfiS5_N3gsl4spanIS4_Lm18446744073709551615EEEEPFN11onnxruntime6common6StatusEPNS_6StreamEiiiiiiPKiiPKfPfiS5_N3gsl4spanIS4_Lm18446744073709551615EEEEFN11onnxruntime6common6StatusEiPNS_6StreamEP8OrtValueRSt10unique_ptrIPfSt8functionIFvPS7_EEEiiPKiS7_iSt10shared_ptrINS_10IAllocatorEEEPFN11onnxruntime6common6StatusEiPNS_6StreamEP8OrtValueRSt10unique_ptrIPfSt8functionIFvPS7_EEEiiPKiS7_iSt10shared_ptrINS_10IAllocatorEEEFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS5_SB_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS5_SB_EFN11onnxruntime6common6StatusEPNS_6StreamERK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS4_biEPFN11onnxruntime6common6StatusEPNS_6StreamERK8OrtValueiSt10shared_ptrINS_10IAllocatorEERS4_biEFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iN3gsl4spanIKiLm18446744073709551615EEESH_SH_iiibiibbRNS_7contrib12transformers9SequencesEPKNSJ_14IConsoleDumperEEPFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iN3gsl4spanIKiLm18446744073709551615EEESH_SH_iiibiibbRNS_7contrib12transformers9SequencesEPKNSJ_14IConsoleDumperEEFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiSt10shared_ptrINS_10IAllocatorEERS5_SB_SB_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiSt10shared_ptrINS_10IAllocatorEERS5_SB_SB_EFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiRN3gsl4spanIiLm18446744073709551615EEESt10shared_ptrINS_10IAllocatorEERS5_SF_SF_EPFN11onnxruntime6common6StatusEPKNS_6TensorEPK8OrtValueiiRN3gsl4spanIiLm18446744073709551615EEESt10shared_ptrINS_10IAllocatorEERS5_SF_SF_EFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iRS8_bN3gsl4spanIKiLm18446744073709551615EEESI_SI_iiibiibEPFN11onnxruntime6common6StatusESt10shared_ptrINS_10IAllocatorEEPNS_6StreamERKSt6vectorI8OrtValueSaIS8_EERSA_iRS8_bN3gsl4spanIKiLm18446744073709551615EEESI_SI_iiibiibEFN11onnxruntime6common6StatusEN3gsl4spanIiLm18446744073709551615EEENS3_IKiLm18446744073709551615EEEPNS_6StreamEiEPFN11onnxruntime6common6StatusEN3gsl4spanIiLm18446744073709551615EEENS3_IKiLm18446744073709551615EEEPNS_6StreamEiEFN11onnxruntime6common6StatusEN3gsl4spanIfLm18446744073709551615EEENS3_IKfLm18446744073709551615EEEPNS_6StreamEiEPFN11onnxruntime6common6StatusEN3gsl4spanIfLm18446744073709551615EEENS3_IKfLm18446744073709551615EEEPNS_6StreamEiEFvPN11onnxruntime7contrib12transformers16IBeamSearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEiiPNS_6StreamEEPFvPN11onnxruntime7contrib12transformers16IBeamSearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEiiPNS_6StreamEEFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPNS6_11IBeamScorerEPKNS6_21IGenerationParametersEiPNS_6StreamEPKNS6_14IConsoleDumperEEPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers16IBeamSearchStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPNS6_11IBeamScorerEPKNS6_21IGenerationParametersEiPNS_6StreamEPKNS6_14IConsoleDumperEEFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EPFN11onnxruntime6common6StatusEPKNS_6TensorEijbbSt10shared_ptrINS_10IAllocatorEEPNS_6StreamEPNS_11concurrency10ThreadPoolERS2_SD_EFN11onnxruntime6common6StatusEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS5_SaIS5_EERSt10unique_ptrIcSt8functionIFvPcEEESt10shared_ptrINS_10IAllocatorEESK_RK13OrtMemoryInfoEPFN11onnxruntime6common6StatusEPNS_6StreamESt16initializer_listI8OrtValueERSt6vectorIS5_SaIS5_EERSt10unique_ptrIcSt8functionIFvPcEEESt10shared_ptrINS_10IAllocatorEESK_RK13OrtMemoryInfoEN11onnxruntime7contrib12transformers11GptSubgraphEN11onnxruntime7contrib12transformers14BeamSearchBaseIfEEN11onnxruntime7contrib12transformers14BeamSearchBaseINS_9MLFloat16EEEN11onnxruntime7contrib12transformers13BeamSearchGptIfEEN11onnxruntime7contrib12transformers13BeamSearchGptINS_9MLFloat16EEEN11onnxruntime7contrib12transformers12BeamSearchT5IfEEN11onnxruntime7contrib12transformers12BeamSearchT5INS_9MLFloat16EEEN11onnxruntime7contrib12transformers17BeamSearchWhisperIfEEN11onnxruntime7contrib12transformers17BeamSearchWhisperINS_9MLFloat16EEEmodel_typeearly_stoppingeos_token_idpad_token_iddecoder_start_token_idno_repeat_ngram_sizevirtual void onnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributes(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_parameters.ccmodel_type == IGenerationParameters::kModelTypeWhispertranslate_token_idtranscribe_token_idstart_of_lm_token_idno_speech_token_idno_timestamps_token_idbeginning_timestamp_token_idvirtual void onnxruntime::contrib::transformers::BeamSearchScorer::Process(onnxruntime::contrib::transformers::ISequences&, gsl::span&, gsl::span&, gsl::span&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/beam_search_scorer.ccnext_scores.size() == next_tokens.size()next_scores.size() == next_indices.size()Batch can only be done if all beams have been generatedbeam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)static_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()beam_idx == num_beams_void onnxruntime::contrib::transformers::GreedySearch::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search.ccparameters_.model_type == IGenerationParameters::kModelTypeGptvoid onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)input_features shall have 3 dimensions. Got decoder_input_ids shall have 2 dimensions. Got extra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor_dims.size() == 2extra_decoder_tensor first dim not same as batch_size. Got extra_decoder_tensor_dims[0] == batch_size) shall be greater than input sequence length (max_length <= kMaxSequenceLengthnum_beams shall be a positive integer no more than num_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got ) shall be be no more than num_beams (num_beams >= num_return_sequencesrepetition_penalty shall be greater than 0, got logits_processor shall be a non-negative integer, got dims.size() == 3decoder_dims.size() == 2, expecting max_length (max_length > sequence_length) shall be no more than num_return_sequences >= 1num_return_sequences (repetition_penalty > 0.0flogits_processor >= 0virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Finalize(onnxruntime::contrib::transformers::ISequences&, gsl::span&, onnxruntime::Tensor*, onnxruntime::Tensor*)void onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span&, gsl::span&)output_sequences != nullptrtop_k <= beams_used_BeamSearch op: An implementation for the input type onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/generation_device_helper.ccinput_ids_shape.NumDimensions() == 2onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IBeamSearchState*, onnxruntime::contrib::transformers::ISequences*, onnxruntime::AllocatorPtr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, onnxruntime::contrib::transformers::IBeamScorer*, const onnxruntime::contrib::transformers::IGenerationParameters*, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]logits_shape.NumDimensions() == 3std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = OrtValue; _Alloc = std::allocator; const_reference = const OrtValue&; size_type = long unsigned int]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(onnxruntime::AllocatorPtr, onnxruntime::Stream*, const std::vector&, std::vector&, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, onnxruntime::contrib::transformers::Sequences&, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]last_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(onnxruntime::AllocatorPtr, onnxruntime::Stream*, const std::vector&, std::vector&, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, onnxruntime::contrib::transformers::Sequences&, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr]void onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputs(const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]element_type == DataTypeImpl::GetType()onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputs(const onnxruntime::Tensor*, const OrtValue*, int, int, gsl::span&, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&, OrtValue&)onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = int; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]is_kv_cacheonnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(onnxruntime::Stream*, const OrtValue&, int, onnxruntime::AllocatorPtr, OrtValue&, bool, int) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]input_features_shape.NumDimensions() == 3original_decoder_input_ids_shape.NumDimensions() == 2start_token_id >= 0onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const onnxruntime::Tensor*, const OrtValue*, int, onnxruntime::AllocatorPtr, OrtValue&, OrtValue&) [with T = onnxruntime::MLFloat16; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]onnxruntime::common::Status onnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogits(const OrtValue&, onnxruntime::contrib::transformers::IGreedySearchState*, onnxruntime::contrib::transformers::ISamplingState*, onnxruntime::contrib::transformers::ISequences*, onnxruntime::AllocatorPtr&, onnxruntime::concurrency::ThreadPool*, onnxruntime::contrib::transformers::ILogitsProcessorList*, const onnxruntime::contrib::transformers::IGenerationParameters*, bool, int, onnxruntime::Stream*, const onnxruntime::contrib::transformers::IConsoleDumper*) [with T = float; OrtValue = OrtValue; onnxruntime::AllocatorPtr = std::shared_ptr]/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_cpu_helper.hGreedySearchProcessLogitsGreedySearchvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)Not ImplementedN11onnxruntime7contrib12transformers22GreedySearchParametersEN11onnxruntime7contrib12transformers12GreedySearchESt4lessIfESt15binary_functionIffbESt7greaterIfEZN11onnxruntime10IAllocator13MakeUniquePtrINS_7contrib12transformers14BeamHypothesesEEESt10unique_ptrIT_St8functionIFvPS6_EEESt10shared_ptrIS0_EmbPNS_6StreamES7_IFvRSE_RNS_11synchronize12NotificationEEEEUlPS4_E_ZN11onnxruntime10IAllocator13MakeUniquePtrINS_7contrib12transformers15HypothesisScoreEEESt10unique_ptrIT_St8functionIFvPS6_EEESt10shared_ptrIS0_EmbPNS_6StreamES7_IFvRSE_RNS_11synchronize12NotificationEEEEUlPS4_E_(knCPU beam search current not support output cross /onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_base.htemperaturetop_pmin_tokens_to_keeppresence_penaltycustomvoid onnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_parameters.ccvoid onnxruntime::contrib::transformers::Sampling::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling.ccSeed must be >= 0seed >= 0void onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputs(onnxruntime::OpKernelContext*)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/sampling_parameters.cc/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/greedy_search_impl_gpt.hvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::GreedySearch::Compute(onnxruntime::OpKernelContext*) constvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::Sampling::Compute(onnxruntime::OpKernelContext*) constSamplingvirtual onnxruntime::common::Status onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)N11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_18SamplingParametersEEEN11onnxruntime7contrib12transformers18SamplingParametersEN11onnxruntime7contrib12transformers8SamplingEFvPN11onnxruntime7contrib12transformers18IGreedySearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEPNS_6StreamEEPFvPN11onnxruntime7contrib12transformers18IGreedySearchStateIfEERN3gsl4spanIiLm18446744073709551615EEEPNS_6StreamEEFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers18IGreedySearchStateIfEEPNS6_14ISamplingStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPKNS6_21IGenerationParametersEbiPNS_6StreamEPKNS6_14IConsoleDumperEEPFN11onnxruntime6common6StatusERK8OrtValuePNS_7contrib12transformers18IGreedySearchStateIfEEPNS6_14ISamplingStateIfEEPNS6_10ISequencesERSt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEPNS6_20ILogitsProcessorListEPKNS6_21IGenerationParametersEbiPNS_6StreamEPKNS6_14IConsoleDumperEEN11onnxruntime7contrib12transformers16ILogitsProcessorIfEEN11onnxruntime7contrib12transformers24TimestampLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PresencePenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers26TemperatureLogitsProcessorIfEEN11onnxruntime7contrib12transformers24MinLengthLogitsProcessorIfEEN11onnxruntime7contrib12transformers30PrefixVocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers24VocabMaskLogitsProcessorIfEEN11onnxruntime7contrib12transformers28NoRepeatNGramLogitsProcessorIfEEN11onnxruntime7contrib12transformers32RepetitionPenaltyLogitsProcessorIfEEN11onnxruntime7contrib12transformers16GreedySearchBaseIfNS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptIfNS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers16GreedySearchBaseINS_9MLFloat16ENS1_22GreedySearchParametersEEEN11onnxruntime7contrib12transformers15GreedySearchGptINS_9MLFloat16ENS1_22GreedySearchParametersEEE@DecoderMaskedSelfAttentionDecoderMaskedMultiHeadAttentiononnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span >, gsl::span >, const onnxruntime::OrtValueNameIdxMap&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_base.ccconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::_Optional_base]past_sequence_lengthbeam_widthSetupSetup must be called before CreateInitialFeedsonnxruntime::common::Status onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const std::vector&, int, int, gsl::span&, OrtValue&, const OrtValue*, std::vector&, const onnxruntime::contrib::GenerationDeviceHelper::CreateGptInputsFunc&, const onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, onnxruntime::Stream*, int, bool)/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_gpt.ccsession_state_ != nullptrCreateInitialFeedsonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu]Validateposition_idspast_0present_0Invalid 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)subgraph input 1 shall be named as position_ids, got: subgraph input 2 shall be named as attention_mask, got: subgraph input 3 shall be named as past_0, got: subgraph 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 input 1 shall be named as present_0, got: subgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph input 1 (position_ids) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 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_statesencoder_attention_mask/onnxruntime_src/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: decoder 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_states/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_encoder.ccnumber of outputs expected to be 2 + 4 * layers, got:encoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder 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: encoder subgraph output 2 shall be named as present_key_self_0, got: encoder 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 typeexpect 3 inputs, got:expect >=6 outputs, got:encoder_input_idsdecoder_input_idspresent_key_self_0present_value_self_0/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_decoder.ccnumber of outputs expected to be first_present_output_index_When outputing cross qk, number of outputs expected to be first_present_output_index_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. + 2 * layers, got: + 3 * layers, got:/onnxruntime_src/onnxruntime/contrib_ops/cpu/transformers/subgraph_whisper_encoder.ccencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typeexpect 2 inputs, got:Input tensor to Unique op should be 1DConv filter size does not match embedding_size attribute.Conv kernal size 1 does not match conv_window_size attribute .Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2./onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : Unsupported typeN11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4CeluIfEEEEN11onnxruntime8functors4TanhIdEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEEN11onnxruntime8functors7SigmoidIdEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEEN11onnxruntime8functors4ReluIiEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIiEEEEN11onnxruntime8functors4ReluIaEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIaEEEEN11onnxruntime8functors4ReluIdEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEEN11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEEN11onnxruntime7contrib17WordConvEmbeddingEN11onnxruntime7contrib6UniqueIfEEOO@%~3a*A\3JCy7Y;7h9999 ': ': ': ':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Failed to create output tensor for If output CopyTensorAsyncstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const OrtDevice*; _Alloc = std::allocator; reference = const OrtDevice*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; reference = const onnx::TypeProto*&]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Celu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu]info == nullptr'If' node has virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc outputs which doesn't match the subgraph's onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)num_subgraph_outputs == static_cast(num_outputs)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::FeedsFetchesManager&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = OrtValue; _Alloc = std::allocator; reference = OrtValue&]Inconsistent shape in loop output for output. void onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.ccinfo.GetAttr("body", &proto).IsOK()virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&)Graph in 'body' attribute of Loop should have onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&)static_cast(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputsinfo_ == nullptr inputs. Found: but has 'Loop' node has onnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::__cxx11::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<8>::Info; _Dp = std::default_delete::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<8>::Info&]Invalid scan input: has batch size of has length of dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Loopstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const OrtValue*; _Alloc = std::allocator; const_reference = const OrtValue* const&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&; size_type = long unsigned int]' attribute.virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_If nodes condition input must have exactly one elementSubgraph SessionState was not found for 'typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::If::Info; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::If::Info&]Failed to create output tensor for Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedfeeds_fetches_manager_ConcatenateLoopOutputSubgraph SessionState was not found for 'body' attribute.virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) consttypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Loop::Info; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Loop::Info&]'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: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]All scan outputs MUST be tensorsvoid onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector&, std::vector&)last_outputs[j + 1].IsTensor()std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; const_reference = const onnx::TypeProto* const&; size_type = long unsigned int]Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.void onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 8]info.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8]feeds_fetches_manager_ && info_Invalid entries in sequence_lens. Max sequence length was ValidateInput did not match batch size of sequence_lens length of outputs but Scan expects Subgraph in 'body' produces AllocateOutputTensorsN11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorEFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEPFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mEN11onnxruntime4LoopE*ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceR8OrtValueRbE_N11onnxruntime2IfEAA-rB-rC9@>@;?;?;?;?1111^9^9^9^9giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>Scan<8> spec does not support transpose of output. This should never be called.Failed to create output tensor for output #/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.ccAllocateFinalBufferMismatch between expected shape and shape from first output is not compatible with CreateFeedsFetchesManager inputs but Scan was only given The subgraph in 'body' requires onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool)num_variadic_inputs == num_subgraph_inputsonnxruntime::common::Status onnxruntime::Scan::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const std::string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::__cxx11::basic_string]/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<9>::Info; _Dp = std::default_delete::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<9>::Info&]Number of entries in '' was but expected Invalid values in 'void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const std::string&, onnxruntime::TensorShapeVector&, size_t)directions.size() == num_entries'. 0 == forward. 1 == reverse.OrtValue& onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = OrtValue&]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.hposition_ >= 0 && position_ < sequence_length_If shape was concrete we shouldn't be using a custom allocatoronnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&)!is_concrete_shape_AllocateFinalOutputonnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh]onnxruntime::common::Status onnxruntime::ElementWiseKernel::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus]virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccscan_input_directionsscan_output_directionsscan_input_axesscan_output_axesvoid onnxruntime::Scan::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 9]Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_output_axes' was gsl::narrow_cast(output_axes_.size()) == num_scan_outputsAttnLSTMSetupInputsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]Attempt to retrieve final output before it was set.const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const/onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.hInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()final_output_mlvalue_. Output tensor rank was TransposeOutputSubgraph must have the shape set for all outputs but did not.AllocateOutputstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; reference = onnxruntime::scan::detail::LoopStateVariable&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; reference = onnxruntime::OrtValueTensorSlicer::Iterator&; size_type = long unsigned int]typename std::enable_if<(! std::is_const::value), T&>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const::value), T&>::type = const OrtValue&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::scan::detail::OutputIterator; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::scan::detail::OutputIterator&]Misuse of LoopStateVariable. Attempt to move beyond end of sequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_IterateSequenceOutput OrtValue has not been created for loop state variable output onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector >&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator; reference = onnxruntime::scan::detail::LoopStateVariable&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; reference = onnxruntime::OrtValueTensorSlicer::Iterator&]p_mlvalueCreateLoopStateVariablesonnxruntime::common::Status onnxruntime::Scan::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9]Invalid value in scan_input_axes for input onnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector&). Input tensor rank was but input '' dimension output_mlvalueonnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.hstatic_cast(activation_func_names.size()) == num_directions_ * 3N11onnxruntime7contrib16AttentionWrapperIfEEN11onnxruntime7contrib19IAttentionMechanismIfEEN11onnxruntime7contrib17BahdanauAttentionIfEEN11onnxruntime7contrib17DeepCpuAttnLstmOpEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9QuickGeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib9QuickGeluIfEEN11onnxruntime7contrib4GeluIfEEN11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEEN11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEE*ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK9OrtDeviceRSD_RbE_*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlPvmE0_*ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_N11onnxruntime4ScanILi9EEE*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlPvmE0_*ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlRKN3gsl4spanIKmLm18446744073709551615EEERKNS_6TensorERSB_PNS_6StreamEE_FN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEPFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llEFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEPFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llEN11onnxruntime4ScanILi8EEEN11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorE#?5?5? constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = std::unique_ptr >; long unsigned int _Nm = 3; const_reference = const std::unique_ptr >&; size_type = long unsigned int]Input 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 0Input 'x' is expected to have 3 or 4 dimensions, got Input 'position_ids' is expected to have 0, 1, or 2 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 num_heads must be provided if rotary_embedding_dim is rotary_embedding_dim must be less than or equal to Input 'position_ids' dimension 0 should be of size Input 'position_ids' dimension 1 should be of size Input 'cos_cache' dimension 0 should be same as head_size / 2 or rotary_embedding_dim / 2, got Input 'cos_cache' dimension 1 should be same as /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/rotary_embedding.ccUpdating cos_cache and sin_cache in RotaryEmbedding is not currently supporteddimensions, got head_sizebatch_size, got max_sequence_length, got input_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 The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:Attention mechanism memory shape error! Expected: {Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{Attention query layer weight shape error! Expected:{Attention v weight shape error! Expected:{Attention layer weight shape error! Expected: {}, actural: found!], while , am_attn_size}, Got:}, Got: }. Got: , aw_attn_size}. Got:ValidateInputsBifurcationDetectorRotaryEmbedding/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.ccRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding./onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cconnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false]use_approximationmin_ngram_sizemin_ngram_size_ > 0max_ngram_sizemax_ngram_size_ > 0onnxruntime::contrib::BifurcationDetector::BifurcationDetector(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bifurcation_detector.hinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()max_ngram_size_ >= min_ngram_size_onnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBase(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::MultiHeadAttention::MultiHeadAttention(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/multihead_attention.cconnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlock(const onnxruntime::OpKernelInfo&)/onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/ngram_repeat_block.hinfo.GetAttr("ngram_size", &ngram_size_).IsOK()virtual onnxruntime::common::Status onnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) constinput_ids_dims.size() == 2scores_dims.size() == 2scores_dims[0] == batch_sizeonnxruntime::contrib::NGramRepeatBlock::Compute(onnxruntime::OpKernelContext*) const::token_id < vocab_sizenum_heads > 0num_heads must be provided if rotary_embedding_dim is specifiedonnxruntime::contrib::RotaryEmbedding::RotaryEmbedding(const onnxruntime::OpKernelInfo&) [with T = float]onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.hinfo.GetAttr("metric", &metric).IsOK()onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo&) [with T = double]virtual onnxruntime::common::Status onnxruntime::contrib::BifurcationDetector::Compute(onnxruntime::OpKernelContext*) constsrc_tokens_len >= prev_suffix_match_idx_datapred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)MaybeTransposeToBNSHAndAddBiasAddBiasReshapeAddBiasTransposePacked 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 kvInput 'bias' is expected to have 1 dimension, got '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' is expected to have 4 dimensions, got Input 'relative_position_bias' dimension 0 should be 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 N11onnxruntime7contrib5CDistIdEEN11onnxruntime7contrib5CDistIfEEZNK11onnxruntime7contrib15RotaryEmbeddingIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib15RotaryEmbeddingIfEEZNK11onnxruntime7contrib16NGramRepeatBlock7ComputeEPNS_15OpKernelContextEEUlllE0_N11onnxruntime7contrib16NGramRepeatBlockEZN11onnxruntime7contrib16AddBiasTransposeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_ZN11onnxruntime7contrib14AddBiasReshapeIfEENS_6common6StatusEPKNS_6TensorEPKT_R8OrtValueiiiiiiPNS_15OpKernelContextEEUlllE2_N11onnxruntime7contrib18MultiHeadAttentionIfEEN11onnxruntime7contrib8BiasGeluIfLb1EEEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib14EmbedLayerNormIfEEN11onnxruntime7contrib19BifurcationDetectorEZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_N11onnxruntime7contrib8BiasGeluIfLb0EEEZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_N11onnxruntime7contrib9AttentionIfEEy"=y"=*BL?*BL?mask_filter_valurotary_embeddingCudaOpenVINO_GPUHipWebGPU_BufferCudaPinnedHipPinnedSpecified device is not supported.CopyTensorsoutput_paddingunimplemented activation: activation_params count mismatchcudaMallochipMalloctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::synchronize::Notification; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::synchronize::Notification&]data_transfer registered is nullptr.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]Tensor size mismatchThere's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.ccConvTransposeWithDynamicPads is not in (0, Real memory steps void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span&, const gsl::span&) [with T = float]/onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.ccmem_steps <= max_memory_steps_ && mem_steps > 0int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const/onnxruntime_src/onnxruntime/core/framework/bfc_arena.hp_int < base_int + memory_size_p_int >= base_intonnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(ChunkHandle)/onnxruntime_src/onnxruntime/core/framework/bfc_arena.cch < chunks_.size()void onnxruntime::BFCArena::InsertFreeChunkIntoBin(ChunkHandle)!c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(ChunkHandle)!c->in_use() && (c->bin_num != kInvalidBinNum)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binvirtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&) constsrc.SizeInBytes() == dst.SizeInBytes()expected a registered ONNX typestatic void onnxruntime::data_types_internal::MapTypeHelper::Set(onnx::TensorProto_DataType, const onnx::TypeProto*, onnx::TypeProto&)/onnxruntime_src/include/onnxruntime/core/framework/data_types.hvalue_proto != nullptrstatic 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&)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&)/onnxruntime_src/onnxruntime/core/framework/data_types.ccN4NN|NOnly ONNX MLDataType can be registeredvoid onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)We do not expect duplicate registration of types for: 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&) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) constvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const nmemb= size= alignment=/onnxruntime_src/onnxruntime/core/framework/allocator.ccRegionForCould not find Region for onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::FindChunkPtr(BinNum, size_t, size_t, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(Bin::FreeChunkSet*, const std::set::iterator&)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long unsigned int; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = long unsigned int&; std::size_t = long unsigned int]void onnxruntime::BFCArena::SplitChunk(ChunkHandle, size_t)!chunk->in_use()void onnxruntime::BFCArena::Merge(ChunkHandle, ChunkHandle)!c1->in_use() && !c2->in_use() && c1->stream == c2->streamc2->prev == h1onnxruntime::BFCArena::ChunkHandle onnxruntime::BFCArena::Coalesce(ChunkHandle)!c->in_use()void onnxruntime::BFCArena::DeallocateRawInternal(void*)void onnxruntime::BFCArena::FreeAndMaybeCoalesce(ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)h != kInvalidChunkHandleBFCArenaCreating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy: Creating bins of max chunk size with following configs: initial_chunk_size_bytes: initial_growth_chunk_size_bytes: max_power_of_two_extend_bytes: onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int, int64_t)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)CreateAllocator/onnxruntime_src/onnxruntime/core/framework/allocator_utils.ccReceived invalid value of arena_extend_strategy ReserveReserving memory in BFCArena for virtual void* onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end() BFC Arena shrunk by bytes. Could not find Region for: entry != regions_.end()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = void*; _Alloc = std::allocator; reference = void*&] The total allocated bytes is now void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void*) Chunk Free c->bin_num == bin_numDumpMemoryLogAllocator:: Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: Config value is longer than maximum length: /onnxruntime_src/onnxruntime/core/framework/config_options.ccAddConfigEntryConfig with key [] already exists with value []. It will be overwrittenN11onnxruntime12OptionalTypeINS_6TensorENS_14Float8E5M2FNUZEEEN11onnxruntime12OptionalTypeINS_6TensorENS_10Float8E5M2EEEN11onnxruntime12OptionalTypeINS_6TensorENS_14Float8E4M3FNUZEEEN11onnxruntime12OptionalTypeINS_6TensorENS_12Float8E4M3FNEEEN11onnxruntime12OptionalTypeINS_6TensorENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorENS_9MLFloat16EEEN11onnxruntime12OptionalTypeINS_6TensorEmEEN11onnxruntime12OptionalTypeINS_6TensorEjEEN11onnxruntime12OptionalTypeINS_6TensorEdEEN11onnxruntime12OptionalTypeINS_6TensorElEEN11onnxruntime12OptionalTypeINS_6TensorEsEEN11onnxruntime12OptionalTypeINS_6TensorEtEEN11onnxruntime12OptionalTypeINS_6TensorEhEEN11onnxruntime12OptionalTypeINS_6TensorEaEEN11onnxruntime12OptionalTypeINS_6TensorENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime12OptionalTypeINS_6TensorEbEEN11onnxruntime12OptionalTypeINS_6TensorEfEEN11onnxruntime12OptionalTypeINS_6TensorEiEEN11onnxruntime18SequenceTensorTypeINS_14Float8E5M2FNUZEEEN11onnxruntime18SequenceTensorTypeINS_10Float8E5M2EEEN11onnxruntime18SequenceTensorTypeINS_14Float8E4M3FNUZEEEN11onnxruntime18SequenceTensorTypeINS_12Float8E4M3FNEEEN11onnxruntime18SequenceTensorTypeINS_8BFloat16EEEN11onnxruntime18SequenceTensorTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime18SequenceTensorTypeIbEEN11onnxruntime18SequenceTensorTypeIlEEN11onnxruntime18SequenceTensorTypeIiEEN11onnxruntime18SequenceTensorTypeItEEN11onnxruntime18SequenceTensorTypeIsEEN11onnxruntime18SequenceTensorTypeIhEEN11onnxruntime18SequenceTensorTypeIaEEN11onnxruntime18SequenceTensorTypeIdEEN11onnxruntime18SequenceTensorTypeIfEEN11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEEN11onnxruntime13NonTensorTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_St4lessIS7_ESaISt4pairIKS7_S7_EEEEEN11onnxruntime7MapTypeISt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES7_St4lessIS7_ESaISt4pairIKS7_S7_EEEEEN11onnxruntime10TensorTypeINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN4onnx16TypeProto_OpaqueESt14default_deleteIN11onnxruntime16StreamAwareArenaEESt14default_deleteIN11onnxruntime8BFCArenaEESt14default_deleteIN11onnxruntime10IAllocatorEEN11onnxruntime8BFCArenaEN11onnxruntime16StreamAwareArenaESt19_Sp_counted_deleterIPN11onnxruntime16StreamAwareArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEEe/framework/bfc_Config key is empty or longer than maximum lengtinvalid index doubleuint8uint16uint32uint64bfloat16Float8E4M3FNFloat8E4M3FNUZFloat8E5M2Float8E5M2FNUZ[\[[\\ \0\@\P\`\p\\\[[\\\\[lN ]],]<]L]\]l]]]]] ^,^lNlNL^l^^^\sparse tensor type N^^^__$_4_T_t_____NN`4`T`t`^ is not currently registered or supportedMLDataType for: CleanUp/onnxruntime_src/onnxruntime/core/framework/device_stream_collection.ccstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]Invalid DataTypeImpl TypeProto definitiononnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)fN0fNfN`fNfNHonnxruntime::Stream* onnxruntime::DeviceStreamCollectionImpl::GetStream(size_t) conststream_idx < num_streams_const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const/onnxruntime_src/onnxruntime/core/framework/execution_frame.hort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_allocator is nullptrallocator.get() != nullptronnxruntime::StreamAwareArena* onnxruntime::AsStreamBasedAllocator(AllocatorPtr)/onnxruntime_src/onnxruntime/core/framework/execution_frame.ccnode_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, gsl::span)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() Requested shape:OrtValue shape verification failed. Current shape:onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, int, const onnxruntime::TensorShape*, OrtValue*&, const onnxruntime::Node&)shape && tensor.Shape() == *shapeshape && sp_tensor.DenseShape() == *shape entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains IExecutionProvider constructor must be called with true for use_metadef_id_creatorvirtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, onnxruntime::HashValue&) const/onnxruntime_src/onnxruntime/core/framework/execution_provider.ccmetadef_id_generator_/onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.ccCaught exception while destructing CustomOpsLoader with message: Unloading DSO ~ExLibLoaderFailed to unload DSO: does not match actual shape of VerifyOutputSizesExpected shape from model of void onnxruntime::ExecutionFrame::TraceFree(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::OrtValuePatternPlanner; _Dp = std::_Optional_base]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&; size_type = long unsigned int]TraceFreeTraceFree for ort_value_idx=ReleaseMLValueImplCandidate for fallback CPU execution: but expected feed size: Get feed size: void onnxruntime::IExecutionFrame::Init(gsl::span, gsl::span, const std::unordered_map&, const std::function&)>&, gsl::span)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Could not find OrtValue with idx 'constexpr const _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() const [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::_Optional_base]onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::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.Tensor shape cannot contain any negative valuestd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; const_reference = const onnxruntime::MemoryPattern&; size_type = long unsigned int]AllocateMLValueTensorSelfOwnBufferHelper, block in memory pattern size is: , fall back to default allocation behavioronnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtDevice&, const onnxruntime::TensorShape&)alloc && alloc.get() != nullptrTraceAllocation for ort_value_idx=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.AllocateMLValueTensorPreAllocateBufferFor ort_value with index: but the actual size is: Failed to get allocator for TraceAllocateAllocateAsPerAllocationPlanInvalid allocation kind: AllocateReusedOrtValueIfNotAllocatedHelperstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]*ZN11onnxruntime14ExecutionFrameC4EN3gsl4spanIKiLm18446744073709551615EEENS2_IK8OrtValueLm18446744073709551615EEES4_S7_RKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS5_RbEESt4hashImESt8equal_toImESaISt4pairIKmSL_EEEPKNS_22DeviceStreamCollectionERKNS_12SessionStateEEUlRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEE_N11onnxruntime14ExecutionFrameEN11onnxruntime11synchronize12NotificationEN11onnxruntime17DummyNotificationEZN11onnxruntime19data_types_internal16DataTypeRegistryC4EvEUlPKNS_12DataTypeImplEE_N11onnxruntime12OptionalTypeINS_9TensorSeqENS_14Float8E5M2FNUZEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_10Float8E5M2EEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_14Float8E4M3FNUZEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_12Float8E4M3FNEEEN11onnxruntime12OptionalTypeINS_9TensorSeqENS_8BFloat16EEEN11onnxruntime12OptionalTypeINS_9TensorSeqEdEEN11onnxruntime12OptionalTypeINS_9TensorSeqElEEN11onnxruntime12OptionalTypeINS_9TensorSeqEsEEN11onnxruntime12OptionalTypeINS_9TensorSeqEtEEN11onnxruntime12OptionalTypeINS_9TensorSeqEhEEN11onnxruntime12OptionalTypeINS_9TensorSeqEaEEN11onnxruntime12OptionalTypeINS_9TensorSeqENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN11onnxruntime12OptionalTypeINS_9TensorSeqEbEEN11onnxruntime12OptionalTypeINS_9TensorSeqEfEEN11onnxruntime12OptionalTypeINS_9TensorSeqEiEEe/framework/ex_lex_lib_loader.cce/framework/fallback_cpu_capabil/onnxruntime_src/onnxruntime/core/framework/func_kernel.hCreate state function failed. Return value:/onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxs (node:'' ep:''). Error mapping feeds: Error mapping output names: InlineNodesKernel found kernel (node_version: Op type ( domain ( kernel is not supported in Kernel not found in the supported version range However the types are incompatible. Encountered following errors: (kernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions. in KernelRegistryManagerfound duplicated provider API VERSION Failed to find op_id: ResolveKernelTypeStr' for node type '/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver.ccFailed to find args for kernel type string '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.Kernel type string already exists for formal parameter name '', but the existing argument with that formal parameter name has a different formal parameter type string.RegisterNodeOpSchemaOp schema must be available.RegisterGraphNodeOpSchemas/onnxruntime_src/onnxruntime/core/framework/kernel_registry.ccbool onnxruntime::{anonymous}::MatchKernelDefTypes(const onnxruntime::Node&, const std::unordered_map, std::vector >&, const onnxruntime::IKernelTypeStrResolver&, std::string&)static_cast(first_arg_idx) < actual_inputs.size()This op has been implemented only for the following types ( but the node in the model has the following type ( However the types are incompatible. MatchKernelDefTypestype_proto != nullptr),Op with name ( and type (std::unordered_set onnxruntime::GetCpuPreferredNodes(const GraphViewer&, const IExecutionProvider::IKernelLookup&, gsl::span)/onnxruntime_src/onnxruntime/core/framework/fallback_cpu_capability.ccORT 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 nodekernel_info != nullptrGetCpuPreferredNodesfloat8e4m3fnfloat8e4m3fnuzfloat8e5m2float8e5m2fnuzstd::visit: variant is valuelessThe node is not placed on any Execution Provider. Failed to find kernel for GetCapabilityForEP!provider_type_.empty()Node '' OpType: with domain:, but was not selected cannot be used with this model due to its ONNX opset not being supported by the layout transformer.provider_type must be specified.onnxruntime::KernelLookup::KernelLookup(onnxruntime::ProviderType, gsl::span >, const onnxruntime::IKernelTypeStrResolver&)/onnxruntime_src/onnxruntime/core/framework/kernel_lookup.htypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IndexedSubGraph; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IndexedSubGraph&] was inserted using the NHWC format as requested by 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.PartitionOnnxFormatModelImplInitializer initializer != nullptrstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; reference = const onnxruntime::NodeArg*&; size_type = long unsigned int] is not found or is not constant initializer.onnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&)/onnxruntime_src/onnxruntime/core/graph/function.ccThe Function Style fusion is deprecated.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ComputeCapability; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ComputeCapability&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::GraphViewer&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IExecutionProvider::FusedNodeAndGraph; _Alloc = std::allocator; reference = onnxruntime::IExecutionProvider::FusedNodeAndGraph&] did not return correct number of compiled functionsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::NodeComputeInfo; _Alloc = std::allocator; reference = onnxruntime::NodeComputeInfo&; size_type = long unsigned int]InlineFunctionsAOTImplstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnxruntime::IExecutionProvider; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnxruntime::IExecutionProvider]PartitionOrtFormatModelImplstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::PartitionOrtFormatModelImpl(const {anonymous}::PartitionParams&, KernelRegistryManager&, IExecutionProvider&)::CompilationEntry; _Alloc = std::allocator; reference = onnxruntime::PartitionOrtFormatModelImpl(const {anonymous}::PartitionParams&, KernelRegistryManager&, IExecutionProvider&)::CompilationEntry&]single_node_compute_func should have 1 element.No provider specified.PartitionOnnxFormatModelPartitionep.context_enableep.context_file_path_ctx.onnxCreateEpContextModelFailed to generate EP context model since the file exist already.*ZN11onnxruntimeL27PartitionOrtFormatModelImplERKNS_12_GLOBAL__N_115PartitionParamsERNS_21KernelRegistryManagerERNS_18IExecutionProviderEEUlRNS_11FuncManagerERKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISE_EEE_*ZN11onnxruntimeL28PartitionOnnxFormatModelImplERNS_5GraphERNS_11FuncManagerERNS_21KernelRegistryManagerERNS_14KernelRegistryERNS_18IExecutionProviderENS_16GraphPartitioner4ModeERiRKSt8functionIFNS_6common6StatusES1_RbS9_RKSD_IFvRKS0_EEEESM_EUlS3_RKNS_12OpKernelInfoERSt10unique_ptrINS_8OpKernelESt14default_deleteISV_EEE0_N11onnxruntime18IExecutionProvider13IKernelLookupEN11onnxruntime12KernelLookupEN11onnxruntime14FunctionKernelE*ZN11onnxruntime20GetCpuPreferredNodesERKNS_11GraphViewerERKNS_18IExecutionProvider13IKernelLookupEN3gsl4spanIKmLm18446744073709551615EEEEUlRKNS_7NodeArgEmE0_TempSpace allocator not found늻‹ɋЋ׋ދ勻싻O5Attribute name and type don't matchNo attribute with this name is defined.Received nullptr for nameReceived nullptr for OrtValueBuffer containing the initializer must be owned by the user.PopulateKernelCreateInfostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; reference = onnxruntime::MemoryPattern&]LoadKernelTypeStrResolverFromBuffer/onnxruntime_src/onnxruntime/core/framework/kernel_type_str_resolver_utils.ccFailed to verify KernelTypeStrResolver flatbuffers data.AddLayoutTransformationRequiredOpsToKernelTypeStrResolverktsrL  HDx P``  :Identity:14`$<:DequantizeLinear:10 , NH 80lH:QuantizeLinear:19<  :Identity:19640P com.microsoft:DequantizeLinear:1t8d\@ :Squeeze:11,   :Transpose:130VT :Identity:1x`$com.microsoft:NhwcMaxPool:1@ :Transpose:1B@ :Unsqueeze:11hP(P (pcom.microsoft:QLinearConv:1 T3"$@w_scale8hpX`x T48 :Unsqueeze:13 FD4 :Gather:11lXp :Squeeze:1@ :Unsqueeze:1B@ :Identity:13hP :Identity:16 V8 :Gather:13.,H$8\:QuantizeLinear:10|0XDLL :DequantizeLinear:19 24x$X@ :Squeeze:13axesxhL`:QuantizeLinear:13y_scale(@f8h,Tcom.microsoft:QuantizeLinear:1 T1 T2@ :Gather:1(Tind Pv8t$H:DequantizeLinear:13x_scale y   T const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&)/onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.ccnullptr != type_protoonnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::/onnxruntime_src/onnxruntime/core/framework/node_index_info.cconnxruntime::NodeIndexInfo::Init > >(const onnxruntime::ValidNodes >&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&)::Execution frame was nullframe != nullptrOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::Stream*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&)/onnxruntime_src/onnxruntime/core/framework/op_kernel.ccint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_virtual OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&). Num args is Invalid arg_num of arg_num < arg_counts.size()virtual int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constvirtual OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)/onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.h!using_counters_current <= buffer_size_Can't slice a non-tensor OrtValue. Type was static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int]/onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.ccOrtValue has not been allocated so can't be sliced.Insufficient dimensions to slice on gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionort_value.IsTensor()ort_value.IsAllocated(). Shape:. Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_sizeonnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int]static onnxruntime::OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T&, size_t, size_t, int64_t, Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int]const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()OrtValue is TensorSequence type but has no element Tensor DataType.static std::unique_ptr OrtTypeInfo::FromOrtValue(const OrtValue&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_typeinfo.ccTensor types should have been handled alreadyThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typetensor_data_type != nullptrzNNzNzNڻٻzNٻNThis TypeProto does not have ValueCase setstatic std::unique_ptr OrtTypeInfo::FromTypeProto(const onnx::TypeProto&)The type is not tensor, sparse tensor, sequence, map or optional typepNݻ2N2NH2N0ݻP߻N`X` std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]failed at retrieving the removable attributesfor node '' ('').PruneRemovableAttributesremoved removable attributes '), among attributes: CreateGraphInfoSaveMLValueNameIndexMappingconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::GraphViewer; _Dp = std::_Optional_base]Done saving OrtValue mappings.N11onnxruntime28IStreamCommandHandleRegistryEN11onnxruntime31StreamCommandHandleRegistryImplEZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_Received OrtValue is not a tensor. Only tensors s are supported.e/framework/sesssession_state.ccPruneRemovableAtstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsThe kernel corresponding to the node 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 /onnxruntime_src/onnxruntime/core/framework/session_state_utils.cc) node with name 'Could not find an implementation for VerifyEachNodeIsAssignedToAnEpImplValidateCsrIndicesdense shape must 2-D. Got: Expecting inner index size: the same as values size: rows: Inner and Outer indices must either be both zero or non-zeroOuter index count must be rows + 1 or zero. Got: input with name is not used by any node.SaveInputOutputNamesToNodeMappingUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const std::string&)/onnxruntime_src/onnxruntime/core/framework/prepacked_weights_container.cconnxruntime::SessionState::PrepackConstantInitializedTensors(onnxruntime::InlinedHashMap, long unsigned int>&, const std::unordered_map, const OrtValue*>&)::allocator_for_caching.get() != nullptr doesn't have an implementation that can cache computed pre-packed weightsweights_to_be_filled_in.buffers_.size() > 0The op type of a node cannot be emptyonnxruntime::HashValue onnxruntime::PrePackedWeights::GetHash() const/onnxruntime_src/onnxruntime/core/framework/prepacked_weights.ccbuffers_.size() == buffer_sizes_.size()Using cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the container!op_type.empty()+ used in the node: which is of op type: void 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()void onnxruntime::DeviceStreamCollectionImpl::AddDeviceStream(size_t, std::unique_ptr)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::Stream*; _Alloc = std::allocator; reference = onnxruntime::Stream*&; size_type = long unsigned int]void onnxruntime::DeviceStreamCollectionImpl::SetDeviceStream(size_t, onnxruntime::Stream*)Only one node should produce an output. Existing entry for void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string&, const NodeInfo&)output_names_to_nodeinfo.empty()CopyData dst_size: Must have the same size. Got src_size: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; const_reference = const std::reference_wrapper&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::reference_wrapper; _Alloc = std::allocator >; const_reference = const std::reference_wrapper&; size_type = long unsigned int]the ort_value must contain a constructed sparse tensorstatic const onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue&)Sparse Tensor does not contain sparse datastatic onnxruntime::SparseTensor& onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue&)this tensor already has populated sparse_indicesMust contain Coo format. Got: onnxruntime::SparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constFormat() == SparseFormat::kCooExpecting to contain one index, got: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; const_reference = const onnxruntime::Tensor&; size_type = long unsigned int]format_data_.size() == 1UMust contain Csr format. Contains: onnxruntime::SparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constFormat() == SparseFormat::kCsrcExpecting two indices. Got: format_data_.size() == 2UCreateSubgraphSessionStateEntry exists in node for attribute void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string&, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()VerifyEachNodeIsAssignedToAnEpSome 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.Node placements All nodes placed on []. Number of nodes: Node(s) placed on [onnxruntime::common::Status onnxruntime::session_state_utils::ExtDataTensorProtoToTensor(const onnxruntime::Env&, const std::__cxx11::basic_string&, const onnx::TensorProto&, onnxruntime::Tensor&, onnxruntime::OrtCallback&)utils::HasExternalData(tensor_proto)ExtDataTensorProtoToTensorstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator; reference = onnxruntime::Tensor&; size_type = long unsigned int]DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires string tensor is not supported for copying between allocators, Got DeserializeTensorProtoFailed to copy tensor to SaveInitializedTensorsSaving initialized tensors.OrtValue index: Can not trace string tensor bytes for Deserialize tensor OrtValue indexes should have been populated.onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::__cxx11::basic_string&, const onnxruntime::GraphViewer&, const onnxruntime::AllocatorPtr&, const onnxruntime::OrtValueNameIdxMap&, const std::vector&, onnxruntime::ITensorAllocator&, const SaveTensorFunction&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&, const MemoryProfileFunction&)ort_value_name_idx_map.MaxIdx() > -1Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied ( from initializer_allocation_order not found among initialized tensorsentry != initialized_tensors_to_allocate.end()entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates Skipping entry for missing optional value at idx Using user supplied initializer with name (session.use_device_allocator_for_initializersDone saving initialized tensorsAllocateBufferValues size must be less than total buffer size: onnxruntime::SparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)Format() == SparseFormat::kUndefinedThis method should follow a call to constructor that supplies the allocatorallocator_ != nullptronnxruntime::SparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE0_*ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateEN3gsl4spanIKPKNS_7NodeArgELm18446744073709551615EEEEUlRS9_mE_N11onnxruntime19session_state_utils19ExtDataValueDeleterE*ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRNS_14InlinedHashMapIS6_mSaISt4pairIS7_mEEEERKNSI_IS6_9OrtDeviceSaISJ_IS7_SO_EEEEbEUlS8_iRK8OrtValueRKNS_11OrtCallbackEbbE0_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE9OrtDeviceSaISt4pairIKSI_SJ_EEEEEUlRKNS_7NodeArgEmE0_*ZN11onnxruntimeL36OuterScopeNodeArgLocationAccumulatorERKNS_23SequentialExecutionPlanERKNS_18OrtValueNameIdxMapERKNS_4NodeERKNS_11GraphViewerERNS_14InlinedHashMapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE9OrtDeviceSaISt4pairIKSI_SJ_EEEEEUlRKNS_7NodeArgEmE_*ZN11onnxruntime25PrepackedWeightsContainer20GetOrCreateAllocatorERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEUliE_VerifyEachNodeIsIsAssignedToAnEpInternal error.Mem pattern for initializer ' is not found' failedWeight buffer for initializer 'Get preallocated buffer for initializer 'Invalid TensorProtoTrace/onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h)(q(((''M''&r&)&%%N%))%$s$*$#FinalizePlanduplicated locationFailed to get allocator for location: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator; reference = onnxruntime::MemoryPattern&; size_type = long unsigned int]Tensor type is undefinedUnpackTensor: the pre-allocated size does not match the raw data size, expected f^f$f$fEfEf^fwff$fEfwf^fwfffEf$f$f$f$f) in proto) does not match the data size(corrupted protobuf data: tensor shape size(TensorProtoToTensor() tensor shape mismatch! can not be written into Tensor type string tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: TensorProto type kꄽPх7 쁽mkk9ӂRshape.Size() must >=0static size_t onnxruntime::Tensor::CalculateTensorStorageSize(onnxruntime::MLDataType, const onnxruntime::TensorShape&)tensor failed memory size calculationonnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr)tensor size overflowsize_t onnxruntime::Tensor::SizeInBytes() constonnxruntime::SparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&)Invalid dimension of dimension <= num_dims for SizeToDimension. Tensor has int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const/onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc for SizeFromDimension. Tensor has int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constInvalid tensor shape slice argument.onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= values_.size()the ort_value must contain a constructed tensor or sparse tensorOrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**)/onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.ccArgument is not a tensorMust contain BlockSparse format. Got: onnxruntime::SparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constFormat() == SparseFormat::kBlockSparseUnsupported indices_format passedconst onnxruntime::Tensor& {anonymous}::GetIndicesTensor(const OrtValue&, OrtSparseIndicesFormat)Expecting one index. Got: Destination should be emptyMust have the same shapeThis instance should not be emptyDestination must have a CPU allocator setX-device copy of strings not supportedSrc and Dst must be of the same typeCurrently do not support dims higher than 2 dimensions: Unable to convert strings tensor to a sparse tensor that not on CPU/onnxruntime_src/onnxruntime/core/framework/sparse_utils.ccDenseTensorToSparseCsrUnsupported element size: DenseTensorToSparseCooUnable to convert strings tensor to a sparse tensor that is not on CPUstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned char; _Alloc = std::allocator; reference = unsigned char&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = short unsigned int; _Alloc = std::allocator; reference = short unsigned int&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = unsigned int; _Alloc = std::allocator; reference = unsigned int&]Support 2-D matrices onlyInput must be of CSR formatouter_num == (rows + 1)SparseCsrToDenseTensorExpecting inner indices to be same as nnz. Got: onnxruntime::common::Status onnxruntime::sparse_utils::SparseCsrToDenseTensor(const onnxruntime::DataTransferManager&, const onnxruntime::SparseTensor&, const onnxruntime::AllocatorPtr&, const onnxruntime::AllocatorPtr&, onnxruntime::Tensor&)inner_num == src.Values().Shape().Size()Outer indices must be M + 1. Got: Input must be of COO formatSparseCooToDenseTensor > dense_size: Invalid index: Expecting indices to be equal the number of values or be twice as manyReadExternalDataForTensorUnpackTensorWithExternalDataImplnullptr == p_dataCopySparseDataSparse 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 int64onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, const onnxruntime::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:SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of Element_size of: ConstantNodeProtoToTensorProtoUnsupported attribute value type of onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&, const std::string&)Constant node: has no data attributes in 'Constant' node 'yO PyOp(yOyOTransposeSingleAxisOutwardsonnxruntime::common::Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool*, Tensor&, std::ptrdiff_t, const TensorShapeVector&, const TensorShape&, const Tensor&, std::ptrdiff_t, const TensorShapeVector&) [with EnabledDataTypes = TypeList, std::allocator > >; std::ptrdiff_t = long int; TensorShapeVector = absl::lts_20240116::InlinedVector >]/onnxruntime_src/onnxruntime/core/framework/transpose_helper.ccvoid onnxruntime::TransposeSingleAxisOutwards(gsl::span, const Tensor&, Tensor&, size_t, size_t, const TensorShape*, concurrency::ThreadPool*)*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE2_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE1_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_*ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_N4onnx17SparseTensorProtoEN11onnxruntime16ITensorAllocatorEN11onnxruntime29TensorAllocatorWithMemPatternEN11onnxruntime21SimpleTensorAllocatorE {DeviceBasedPartitionerFailed to find input name in the mapping: CalculateStaticCopyInfoForFeedcannot use operator[] with a string argument with nObO2O&OJOnOnOnO>OVOstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector >; _Alloc = std::allocator > >; reference = absl::lts_20240116::InlinedVector >&; size_type = long unsigned int]const OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::SessionState&, std::string_view)const OrtDevice& onnxruntime::utils::FindDeviceForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, std::string_view)exec_plan_ptrFindDeviceForValueInitializeFeedFetchCopyInfoCalculateStaticCopyInfoForFeedsbool 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()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap&, NodeIndex)/onnxruntime_src/onnxruntime/core/framework/allocation_planner.ccentry != kernel_create_info_map.cend() against size invalid value index: int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_forstatic size_t onnxruntime::PlannerImpl::GetElementSize(const std::string* const&)nullptr != tensor_type_baseonnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const onnxruntime::OrtValueName&)Can not find the node ComputeReuseCountThere is no location for this node arg in the outer scope location maponnxruntime::PlannerImpl::ComputeValueLocation()::found_in_outer_scope_location_map reused by as inputOnly tensors are supported for external outputs for now.onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan(size_t)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::PlannerImpl::OrtValueInfo; _Alloc = std::allocator; reference = onnxruntime::PlannerImpl::OrtValueInfo&; size_type = long unsigned int]!IsNonTensor(*node_output)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = absl::lts_20240116::InlinedVector, 1, std::allocator > >; _Alloc = std::allocator, 1, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 1, std::allocator > >&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = absl::lts_20240116::InlinedVector, 1, std::allocator > >; _Alloc = std::allocator, 1, std::allocator > > >; reference = absl::lts_20240116::InlinedVector, 1, std::allocator > >&; size_type = long unsigned int]virtual onnxruntime::common::Status onnxruntime::DeviceBasedPartitioner::PartitionGraph(const onnxruntime::GraphViewer&, const onnxruntime::ExecutionProviders&, std::vector > >&, onnxruntime::ExecutionOrder)" in node-stream mapFailed to find node "iter != node_stream_map.end()Failed to find allocator for device onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, onnxruntime::Stream*, std::vector*, std::vector*)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SparseSrcDstPair; _Alloc = std::allocator; reference = onnxruntime::IDataTransfer::SparseSrcDstPair&]Unsupported OrtValue type to copy between device.Unsupported OrtValue type.BatchOrCopyMLValuevoid onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*)id >= 0 && static_cast(id) < ort_value_info_.size()Should not have entry in kernel create info with nullptr for kernel_defonnxruntime::common::Status onnxruntime::PlannerImpl::ComputeValueLocation()Can not find the execution provider p_kernel_defComputeValueLocationstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]OrtDevice onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&, const onnxruntime::KernelCreateInfoMap&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = OrtDevice; _Alloc = std::allocator; reference = OrtDevice&]void onnxruntime::PlannerImpl::GeneratePlanForWeightsHelper(const onnxruntime::GraphViewer&, const onnxruntime::InitializedTensorSet&, const onnxruntime::KernelCreateInfoMap&, const std::string&, size_t, std::vector >&)specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()p_provideronnxruntime::common::Status onnxruntime::PlannerImpl::BuildExecutionPlan(const onnxruntime::ExecutionProviders&, const onnxruntime::IStreamCommandHandleRegistry&)execution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()trigger_point_it != node_to_trigger_points.end()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]BuildExecutionPlanCreateGraphPartitionerdevicesUse DeviceBasedPartition as defaultPartitioner type is not DeviceBasedPartitionerstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = signed char; _Alloc = std::allocator; reference = signed char&]Number of streams does not equal to number of device types!void onnxruntime::DeviceBasedPartitioner::Initialize()node_names_by_stream_.size() == device_types_.size()Caught exception when reading partition config file: iterator does not fit current valuecannot use insert() with >O=O >O=O=O=O=O=O=O=OSaveConfigCaught exception during saving DeviceBasedPartitioner config: OptimizeReusePlanForMultiStreamZN11onnxruntime11PlannerImpl24GenerateDeallocationPlanEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE2_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE1_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlRKNS_7NodeArgEmE0_ZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvEUlmE_ZZN11onnxruntime11PlannerImpl31OptimizeReusePlanForMultiStreamEvENKUlmE_clEmEUlmE_ZN11onnxruntime11PlannerImpl20ComputeValueLocationEvEUlRKNS_7NodeArgEmE_ZN11onnxruntime11PlannerImpl17ComputeReuseCountEvEUlRKNS_7NodeArgEmE_N8nlohmann6detail23output_adapter_protocolIcEEN8nlohmann6detail21output_string_adapterIcNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESt23_Sp_counted_ptr_inplaceIN8nlohmann6detail21output_string_adapterIcNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESaIvELN9__gnu_cxx12_Lock_policyE2EEN11onnxruntime17IGraphPartitionerEN11onnxruntime22DeviceBasedPartitionerEN11onnxruntime23SequentialExecutionPlan13ExecutionStepEN11onnxruntime11BarrierStepEN11onnxruntime12WaitOnEPStepEN11onnxruntime16LaunchKernelStepEN11onnxruntime24ActivateNotificationStepEN11onnxruntime21TriggerDownstreamStepEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::StreamExecutionContext::CountDownBarrier; _Alloc = std::allocator; reference = onnxruntime::StreamExecutionContext::CountDownBarrier&; size_type = long unsigned int], count: Set a barrier with id: WaitOnEPStep: wait on notification with id: Launch kernel with node id: ActivateNotificationStep: activate notification with id: TriggerDownstreamStep: trigger downstream of trigger point: 0``haaPa``@```ooglglglglglglglglglglglglglglglglglglglglglglglglglglglglglglglglglnglglglglglglglglglolglglllllllllll"omwkmwkwkwkwkwkwkwkwkcmwkwkwkwkwkwkwk3mwkwkwkwkwklwkwkwkwkwkwklwkl"rrrbrRrBrdvTvDv4v$vvvuuuuuuuutuduTuDu4u$uuutttttmmrmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmpmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r2r^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^p^ptttttttttttttttttgttttsmnnnnnmnnnnnnnnnnnnnnnmnmlfunc info for node: already exist.Can't use func with null ptrstd::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = std::unordered_map, onnxruntime::FuncManager::FuncInfo>; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = std::unordered_map, onnxruntime::FuncManager::FuncInfo>]Error parsing node:Error unexpected extra input in node:constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = unsigned char; long unsigned int _Nm = 400; const_reference = const unsigned char&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = char; long unsigned int _Nm = 512; reference = char&; size_type = long unsigned int]std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::const_reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::back() const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; const_reference = const char&]incomplete UTF-8 string; last byte: 0x\u%04x\u%04xinvalid UTF-8 byte at index : 0x!empty()\ufffd�L ${}{ []"bytes": [], "subtype": {"bytes":[],"subtype":null}constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = nlohmann::detail::dtoa_impl::cached_power; long unsigned int _Nm = 79; const_reference = const nlohmann::detail::dtoa_impl::cached_power&; size_type = long unsigned int]h|䔿$D̗{"]}op_namenode_indexactivation_sizeparameter_sizeoutput_sizeinput_type_shapeoutput_type_shapethread_scheduling_stats_kernel_time_fence_afterAttribute expected to have tensor or sparse tensor type[TypeInferenceError] x輿ؼ(hXHX8ȼTd使tĽԽX¿0¿P¿¿¿XXXXPÿstream /onnxruntime_src/onnxruntime/core/framework/execution_steps.cc activate notification with index has already been loaded.LoadExternalLibGetFuncsA dso with name Caught exception while loading custom ops with message: /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cconnxruntime::Stream* onnxruntime::StreamExecutionContext::GetDeviceStream(size_t)/onnxruntime_src/onnxruntime/core/framework/stream_execution_context.ccidx < device_stream_map_->NumStreams()WaitOnEPStep.wait_handle is nullvirtual onnxruntime::common::Status onnxruntime::WaitOnEPStep::Execute(onnxruntime::StreamExecutionContext&, size_t, onnxruntime::SessionScope&, const bool&, bool&) wait on Notification with id: wait_handle_std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::vector; _Alloc = std::allocator >; const_reference = const std::vector&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = std::atomic; _Dp = std::default_delete []>; typename std::add_lvalue_reference<_Tp>::type = std::atomic&; std::size_t = long unsigned int]std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; const_reference = const onnxruntime::SequentialExecutionPlan::ReleaseAction&; size_type = long unsigned int]void onnxruntime::StreamExecutionContext::RecycleNodeInputs(onnxruntime::NodeIndex)frame_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK() does not.entry != nullptr_fence_before node. Name:'' Status Message: ExecuteKernelAll implicit inputs should have OrtValue instances by now. onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&, onnxruntime::Stream*)/onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.hAsync Kernel Support is not implemented yet.onnxruntime::common::Status onnxruntime::ExecuteKernel(StreamExecutionContext&, NodeIndex, size_t, const bool&, SessionScope&)/onnxruntime_src/onnxruntime/core/framework/sequential_executor.ccNon-zero status code returned while running Exiting due to terminate flag being set to true.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]void onnxruntime::RunSince(size_t, StreamExecutionContext&, SessionScope&, const bool&, size_t)since == endGot null library handleFailed to unload library with error: Inputs 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 2 or 4 dimensionsInputs 2 (value) shall be 2 dimensionsNumber of attention headsrelative_position_biaspresentWhether 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)Constrain mask index to integer typescumulative_sequence_lengthPackedAttentionqueryPackedMultiHeadAttentionConstrain input and output to float tensors.Constrain mask, offset and sequence length to integer typescache_indirectionCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Need output the cross attention MatMul(Q, K)Constrain qk output types to float32 tensors.output_qkpast_keypast_valuepresent_keypresent_valueConstrain mask to integer typesNumber of attention heads for qNumber of attention heads for k and vleft_window_size for local attention (like Mistral). Default value is -1 meaning unused.Rotate using interleaved pattern. Default value is 0 (False).kv_num_headslocal_window_sizerotary_interleavedseqlens_ktotal_sequence_lengthcos_cachesin_cacheConstrain mask to int tensor.GroupQueryAttentionOne sided attention windows length W, or half of total window lengthglobal_biasglobalConstrain to integer typesq_weightkv_weightstatic_kvuse_pasthas_layer_statehas_key_padding_masknew_key_cachenew_value_cacheDecoderAttentionConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.Custom 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_dimConstrain input and output types to integer tensorsThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)Constrain input and output integer tensors typesConstrain input and output float tensors types.mask_index_typesegment_idsword_embeddingposition_embeddingsegment_embeddingembedding_sumMax distancemax_distanceDefault value is 0.is_bidirectionalbias_tablequery_lengthkey_lengthConstrain sequence_length to int tensors.inv_std_varinput_skip_bias_sumConstrain mean and inv_std_var to float tensors.The minimum NGram size for suffix matching.The maximum NGram size for suffix matching.src_tokenscur_tokensprev_suffix_match_idxpred_tokenssequence_token_countcumulated_seq_lenmax_seq_lenRemovePaddingConstrain sequence_token_count and token_offset to integer typesquery_layerquery_biasrel_poseco_aGatedRelativePositionBias*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Attention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_N4onnx26TensorShapeProto_DimensionE*ZN11onnxruntime18ScheduleDownstreamERNS_22StreamExecutionContextEmbRKbRNS_12SessionScopeEEUlvE_*ZN11onnxruntime14ExecuteThePlanERKNS_12SessionStateEN3gsl4spanIKiLm18446744073709551615EEENS4_IK8OrtValueLm18446744073709551615EEES6_RSt6vectorIS7_SaIS7_EERKSt13unordered_mapImSt8functionIFNS_6common6StatusERKNS_11TensorShapeERK9OrtDeviceRS7_RbEESt4hashImESt8equal_toImESaISt4pairIKmSR_EEERKNS_7logging6LoggerEPKNS_22DeviceStreamCollectionERKbbbEUlvE_N11onnxruntime23OpKernelContextInternalE*ZN11onnxruntime11FuncManager8GetFuncsERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERPKNS_15NodeComputeInfoEEUlPvE1_*ZN11onnxruntime11FuncManager8GetFuncsERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERPKNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_*ZN11onnxruntime11FuncManager8GetFuncsERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERPKNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_̼+̼+0001020304050607080910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697989900010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899   00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨= 2.Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.inputs are expected to have tensor type and output type should not be null. expected to have rank but has rank expected to have rank >Dimension mismatch in unification between Inputs 0 shall be 3 dimensions in whisper graphFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarInput axis is invalid: != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Incompatible dimensions'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesAlpha value.padding_idxscale_grad_by_freqTorchEmbeddingConstrain input and output types to all numeric tensors and bool tensors.residualConstrain input 'training_mode' types to boolean tensors.Constrain output 'mask' types to uint32 tensors.If true, check only for Inf, -Inf.Constrain the output to a boolean tensor.isinf_onlyIf true, check only for NaN.isnan_onlyIsAllFiniteThe id of the end-of-sequence tokenThe id of the token that indicates decoding starts.model 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 shapeConstrain input type to float or int tensors.The id of the padding tokenno repeat ngrams sizeearly stop or notrepetition_penaltyprefix_vocab_masklogits_processorsequences_scoresThe id of the token that indicates decoding starts (i.e. the start of transcription token id)The 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 timestampsIf nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.Constrain cross_qk to float32 tensors.The id of the translate taskThe id of the transcribe taskThe id of the first timestampMust be 2 for whisperdecoder_output_cross_qkcross_qk_layer_headextra_decoding_idsnon_speech_probsmodel 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 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 samplingModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartIf 1 custom sampling logicpresence_maskfiltered_logitsActivation function to use. Choose from relu, gelu, silu and identity. Default is reluNumber of top experts to select from expert poolConstrain input and output types to float or float16 tensors.activation_typerouter_probsfc1_experts_weightsfc2_experts_weightsfc1_experts_biasfc2_experts_biasMoEConstrain input0 and output types to float tensorsConstrain input and output types to float tensorsactivation_gammaInput/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 ignoredConstrain 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 typeblk_quant_typeB_shapeConstrain 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 multiplicationWhether 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_axissparse_tensor(float)sparse_tensor(double)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(uint64)sparse_tensor(uint32)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.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 arrayThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. crop_sizeWhether 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).Constrain type to result type.Constrain type for all input scales (scaleA, scaleB, scaleY).scaleAscaleBscaleYTRConstrain type to input A.Constrain type to input B.Constrain type to input C.GemmFloat8Three 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.Constrain input types to all tensor types.specify the dimension to unfoldspecify the sizespecify the step.UnfoldTensorConstrain indice type to int32 or int64initial_hQWMWmemory_seq_lensAWY_hlimitdeltaEfficientNMS_TRTanchorsnum_detectionsdetection_boxesdetection_scoresdetection_classesBackground class ID.background_classbox_codingBox IOU threshold value.Max detections to output.max_output_boxesplugin_versionscore_activationScore threshold value.MultilevelCropAndResize_TRTfeature_map_0feature_map_1feature_map_2feature_map_3patchesImage size.image_sizePooled size.pooled_sizePyramidROIAlign_TRTDisentangledAttention_TRTc2c_attentionc2p_attentionp2c_attentiondisentangled_attentionMaximum relative distance, k.spanfactorSnpepayload of the SNPE DLC file.DLCsnpe_versiontarget_devicenotesEPContextmain_contextep_cache_contextembed_modeep_sdk_versionhardware_architecturepartition_namesourcesize of each input featuresize of each output featureaccuracy_levelzero_pointsabsmaxOptional 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 seq_lens to integral tensors.Constrain input and output types.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).type used for stash mean/inv_std_varConstrain input X type to float 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.Encoding type for the boxes or anchors inputs.Version number of the TRT plugin.Activation function to apply to the scores input.Scaling 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.(Optional) SNPE version used to convert the model.(Optional) Target device like CPU, DSP, etc.(Optional) Some notes for the modelConstrain input and output types to uint8, uint16, float tensors.Usually 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.payload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.1: 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.(Optional) SDK version used to convert the model.(Optional) Hardware architecture.(Optional) partitioned graph name.(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainConstrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.number 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.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.Constrain input and output types to float/half_float/brain_float tensors.*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MatMulInteger16_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24Tokenizer_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25ExpandDims_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24FusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30MaxpoolWithMask_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23Sampling_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GreedySearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32WhisperBeamSearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25BeamSearch_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33DynamicTimeWarping_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27UnfoldTensor_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GridSample_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26IsAllFinite_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33BitmaskBiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26BiasDropout_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20Trilu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29TorchEmbedding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_22Inverse_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_24QuickGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_19Gelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_40GatedRelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29RestorePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28RemovePadding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27GemmFastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34BifurcationDetector_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31NGramRepeatBlock_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35RelativePositionBias_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23FastGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRKNS3_24FunctionBodyBuildContextERKS4_RNS3_13FunctionProtoEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30RotaryEmbedding_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DecoderAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34GroupQueryAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33MultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_46DecoderMaskedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41DecoderMaskedSelfAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39PackedMultiHeadAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30PackedAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_Allow inputs and outputs to be any kind of tenso tensor type. If the dtype attribute is not provided this must be a valid output to any tensor tInput can be of 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.{name}{additionalDocumentation}A_scaleA_zero_pointB_scaleB_zero_pointC_scaleC_zero_pointConstrain input and output types to 8 bit signed and unsigned tensors.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 dimensionInput 0 shall be 3 dimensionsbias shall be 1 dimensionOutput tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last.tensor rank too smallinvalid scales dimensioninvalid scales valueinputs 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.The epsilon value to use to avoid division by zeroThe 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.Constrain gamma and beta to float tensors.SkipGroupNormConstrain input X, skip, bias and output Y, S types to float tensors.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.Constrain input and output types to 8 bit tensors.w_scalew_zero_pointThe 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.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.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.The type of BFP - must match with the BFPType enumEach 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.Constrain the input to float and bfloat.Constrain shape and strides to uint64.bfp_typeblock_dimConstrain y to uint8.QuantizeBFPThe datatype to dequantize to.Constrain y to float and bfloat16.Constrain the input to uint8.DequantizeBFPConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.b_scaleConstrain input A, b_scale and output Y data type as float tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.data_scaledata_zero_pointreduced_scalereduced_zero_pointQLinearReduceMeanX_scaleX_zero_pointY_scaleY_zero_pointapply softmax to elements for dimensions axis,or all dims along with axis according to op-versionopset version of corresponding SoftMax.R_scaleR_zero_pointNumber of neurons in the hidden layerConstrain seq_lens to integer tensor.Constrain weights types to 8 bit tensors.T8TVConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).z_scalez_zero_pointConstrain 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.input_scaleweight_scaleinput_zero_pointweight_zero_pointCorresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)Constrain input and output types to int8 tensors.word_embedding_quantposition_embedding_quantgamma_quantbeta_quantword_embedding_scaleposition_embedding_scalesegment_embedding_scalegamma_scalebeta_scaleword_embedding_zero_pointposition_embedding_zero_pointsegment_embedding_zero_pointgamma_zero_pointbeta_zero_pointlayernorm_outmask_index_outcublasLt 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.cublasLt order of output matrix.Constrain Scale to float32 typesorder_inputorder_outputscale_inputConstrain to float 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)DequantizeWithOrdercublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix Y and optional matrix CConstrain bias and scales to float32order_AcublasLt order of matrix Border_Border_Yscale_Ascale_Bscale_Yscale_CQOrderedMatMulHidden layer sizes of Q, K, V paths in AttentioncublasLt order of weight matrixConstrain scales to float32 tensors.order_weightcublasLt order of global biasscale_Q_gemmscale_K_gemmscale_V_gemmscale_Q_weightscale_K_weightscale_V_weightQ_biasK_biasV_biasscale_QKT_gemmscale_QKT_softmaxscale_values_gemmQOrderedAttentioncublasLt 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.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.order_Xscale_XQOrderedLayerNormalizationcublasLt 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.Constrain scales to float32QOrderedGeluBe compatible with float version.order_global_weightscale_weightscale_biasscale_qkv_gemmscale_global_weightscale_global_gemmscale_outputQOrderedLongformerAttention/onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccd<ľD*ZN11onnxruntime7contrib11GetOpSchemaINS0_31DequantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QuantizeLinear_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_*ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_15Crop_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26GivenTensorFill_Onnx_ver10EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GivenTensorFill_Onnx_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28NhwcFusedConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvEUlRN4onnx8OpSchemaEE_*ZZN11onnxruntime7contrib21ConvOpSchemaGeneratorEvENKUlRN4onnx8OpSchemaEE_clES3_EUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QLinearConv_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_33QLinearAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_39QLinearGlobalAveragePool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26NhwcMaxPool_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE2_FvRN4onnx8OpSchemaEEPFvRN4onnx8OpSchemaEE*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_*ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_132RegisterNHWCSchemaWithActivationERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_132RegisterNCHWSchemaWithNHWCDomainERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_*ZN11onnxruntime18internal_nhwc_onnx12_GLOBAL__N_118RegisterNHWCSchemaERKSt8functionIFvON4onnx8OpSchemaEEES5_EUlRNS3_16InferenceContextEE_N4onnx16InferenceContextEN11onnxruntime7contrib20NhwcInferenceContextE*ZN11onnxruntime7contrib11GetOpSchemaINS0_28BiasSplitGelu_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28SkipGroupNorm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE0_*ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25GemmFloat8_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28CropAndResize_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_21Unique_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_18Pad_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_23GatherND_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26MurmurHash3_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34SparseToDenseMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36FusedMatMulActivation_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26FusedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_30TransposeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MatMulFpQ4_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_̼+KN Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDolDocumentation} std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::TypeProto; _Alloc = std::allocator; reference = onnx::TypeProto&; size_type = long unsigned int]]. Its actual value is: , 'axis' must be in [ or UNDEFINED. Got: expected to have: Graph attribute inferencing failed: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]No Graph instance was found for attribute std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = gsl::not_null; _Alloc = std::allocator >; reference = gsl::not_null&]Shape of quantized tensor must be 1D.Required attribute axis is missingAll inputs to Concat must have same rankaxis must be in [-rank, rank)First input does not have rank 2Second input does not have rank 2Can not get shape initializer data!Unsupported non-raw-data data type!Unsupported 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 dimensionsaxis must be in [-rank, rank-1]. input rank was Source and target must both be tensorsError merging shape info for output. '. Falling back to lenient merge. , or optional typed entities , or sparse tensorsMergeShapeInfo' source: target:Output:Tensor element type mismatch. Type mismatch. Current= Input=UpdateTypeAndShape . Got: Optional Type mismatch. Expected: Removable attributes were removed before the conversion is started.void onnxruntime::Node::ToProto(onnx::NodeProto&, bool) constconst onnxruntime::ConstPointerContainer::T* onnxruntime::ConstPointerContainer::at(size_t) const [with Container = std::vector; T = onnxruntime::NodeArg; size_t = long unsigned int]/onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.hstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::TypeProto; _Alloc = std::allocator; reference = onnx::TypeProto&]Cannot find a function builder that satisfies the requested opset version: op_type = index < data_.size(), opset_version = Invalid node indexes specified when adding edge.void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid source node arg slot specified when adding edge.Invalid destination node arg slot specified when adding edge.Argument type mismatch when adding edge.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]Invalid node indexes specified when removing edge.void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)Invalid source node arg slot specified when removing edge.Invalid destination node arg slot specified when removing edge.Argument mismatch when removing edge.] op_type [], could not find NodeArg fbs_node_arg_names cannot be nullLoadNodeArgsFromOrtFormat: Node [LoadAttributeOrtFormat/onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.ccNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null 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.Serialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missing:?x?h><2:9p7h6graph_proto cannot be nullgraph_proto != nullptr' Model is invalid.utils::HasName(sparse_tensor). Please, fix your model.node_argonnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, bool)Duplicate constant node sparse initializer name: '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 initializerThis 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.Graph state to be loaded into must be empty.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph ctor should have created NodeArg for initializer. Missing:) does not exist in the graph.This is an invalid model. Graph 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. Subgraph output (std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator; reference = long unsigned int&]This is an invalid model. Error: the graph is not acyclic.Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. inputs but subgraph has inputs and requires Node:Subgraph input missing type.InferAndVerifySubgraphTypesN4onnx15GraphInferencerEN11onnxruntime19GraphInferencerImplEN11onnxruntime5GraphEN4onnx24FunctionBodyBuildContextEN4onnx28FunctionBodyBuildContextImplE*ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35QLinearConvTranspose_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_41QOrderedLayerNormalization_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QOrderedMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_34DequantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QuantizeWithOrder_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25QAttention_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_20QGemm_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_27QLinearWhere_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28QLinearConcat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_29QLinearSoftmax_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_32QLinearReduceMean_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_35MatMulIntegerToFloat_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_36DynamicQuantizeMatMul_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_25MulInteger_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_28DequantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_*ZN11onnxruntime7contrib11GetOpSchemaINS0_26QuantizeBFP_Microsoft_ver1EEEN4onnx8OpSchemaEvEUlRNS3_16InferenceContextEE_ForThisAndAllSubgraphsstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::Graph*; _Alloc = std::allocator; reference = onnxruntime::Graph*&]This is an invalid model. Model input () does not have type information.Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer ' has element type does not match. _token_) input arg (' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types (InferAndVerifyTypeMatchNode () Op () output arg (Type Error: Type () of output arg () of node (This is an invalid model. Node () does not have type information set by parent node.This is an invalid model. Type Error: Type 'std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnx::TypeProto; _Alloc = std::allocator; const_reference = const onnx::TypeProto&; size_type = long unsigned int]) type inference failed, inferred types: ) does not match expected type (typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::FunctionTemplate; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::FunctionTemplate&]) is not a registered function/opThis is an invalid model. The sum of input arg count is not equal to size of input defs in node () is required but not specified.This is an invalid model. In Node, VerifyNodeAndOpMatchFatal error: ) attribute () : ("") -> (, Error PerformTypeAndShapeInferencing must be either specified in graph inputs or graph initializers.Error: Duplicate definition-site for (This is an invalid model. Error: two nodes with same node name (This is an invalid model. Error: Duplicate definition of name (InitInputsInitializersOutputsResolveShouldn't be possible to have NodeArgs that haven't been handled already.onnxruntime::common::Status onnxruntime::Graph::Resolve(const ResolveOptions&)outer_scope_node_args_consumed.empty() but different TensorProto.existing->second == &tensorAddInitializedTensor already has tensor with name void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&)sparse_tensor_names_ not in sync with name_to_initial_tensor_void onnxruntime::Graph::RemoveInitializedTensor(const std::string&)sparse_tensor_names_.count(tensor_name) == 0graph_proto_ is not in sync with name_to_initial_tensor_.!foundInput to set must exist.input->Exists()void onnxruntime::Graph::SetInputs(gsl::span)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator; reference = const onnxruntime::NodeArg*&]gsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]NoOp as it still has output edges.bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()nullptr != func_meta_defOuter scope node arg name ''was added but does not exist. void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) constFailed to convert dense initializer to sparseonnx::GraphProto onnxruntime::Graph::ToGraphProto() constCannot find NodeArgs for [Removing initializer 'Removing NodeArg 'void onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs(const std::unordered_set >*)initializer_node_arg != nullptrCleanUnusedInitializersAndNodeArgs'. 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 != nullptronnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const std::string&)Schema was not found for fused node. Domain:SetOpSchemaFromRegistryForNode(fused_node)typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::OpSchema; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx::OpSchema&]absl::container_internal::raw_hash_map<>::at SinceVersion:_if_' Unable to fetch: InlineIfSubgraph from graph: Initializer name: insert_result.secondNode's: Constant folded (inlined) for If node: Unable to constant fold If node: ' conflicts with graph initializer. Check name generation above.onnxruntime::common::Status onnxruntime::Graph::InlineIfSubgraph(bool, onnxruntime::Node&, const onnxruntime::logging::Logger&) is not If node's input or previous node output in this subgraph_inlfunc_InlineFunctionProtoInlineFunction in inlined subgraph: Node has no function body and cannot be inlined.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&)callnode.TryGetFunctionProto(inlined_fp)Number of actual parameters cannot exceed number of formal parametersvoid onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = false]/onnxruntime_src/onnxruntime/core/graph/function_utils.ccactuals.size() <= formals.size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >; reference = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >&]void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField >&, const google::protobuf::RepeatedPtrField >&) [with bool isOutput = true] conflicts with graph initializer. Check Specializing code.N4onnx16OpSchemaRegistryEN11onnxruntime20InferenceContextImplEstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]LoadInitializerOrtFormat*/_ORT_MEM_ADDR_/*Missing 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.LoadValueInfoOrtFormatNull type info for . Invalid ORT format model.Node index is out of rangeLoadEdgesFromOrtFormatinput index: input edgesoutput edgesDuplicate initializer (dense or ConstantNode): '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: is not the same as this node's index:FindPathFailed since multiple edges matched:void onnxruntime::GraphViewerToProto(const GraphViewer&, onnx::GraphProto&, bool, bool)/onnxruntime_src/onnxruntime/core/graph/graph_proto_serializer.ccInvalid ExecutionOrderconst std::vector& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const/onnxruntime_src/onnxruntime/core/graph/graph_viewer.ccitr != node_args.end()Attempting to get index by a name which does not exist:int onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node&, const std::string&, bool) Implicit input name cannot be safely updated to in one of the subgraphs.CanUpdateImplicitInputNameInSubgraphsAttempting to get an input that does not exist.const std::string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int)index >= 0 && static_cast(index) < inputs.size()Attempting to get an output that does not exist.const std::string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int)index >= 0 && static_cast(index) < outputs.size()ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet()/onnxruntime_src/onnxruntime/core/graph/model_load_utils.h is till opset ValidateOpsetForDomainONNX 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 void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map, int>&, const onnxruntime::logging::Logger&, bool, const std::string&, int) 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::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::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; }) == 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = onnxruntime::graph_utils::GraphEdge; _Alloc = std::allocator; reference = onnxruntime::graph_utils::GraphEdge&]Node must only have one used outputbool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&)Initializer with same name exists. Name:onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&)!graph.GetInitializedTensor(new_initializer.name(), existing)Some nodes are not included in the topological sort, graph have a cycle.void onnxruntime::Graph::KahnsTopologicalSort(const std::function&, const std::function&) constIndexedSubGraph contains values not present in the Graphonnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*)graph_->GetNode(idx) != nullptrMismatch between Graph and IndexedSubGraph. Input not found:Mismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: nodeargN11onnxruntime19PriorityNodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_N11onnxruntime11NodeCompareE*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_*ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_*ZN11onnxruntime5Graph49PopulateNodeArgToProducerConsumerLookupsFromNodesEvEUlRKNS_7NodeArgEbE_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_*ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEPFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEEALLOW_RELEASED_O_ONNX_OPSET_ONLYstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >; reference = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >&; size_type = long unsigned int]void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = onnxruntime::InlinedHashMap, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >]OrtThreadPoolParams { auto_set_affinity: allow_spinning: dynamic_block_base_: stack_size: affinity_str: set_denormal_as_zero: less than 0.Protobuf parsing failed.AttributeProto must have a name.std::pair, onnx::AttributeProto>, false, true>, bool> onnxruntime::utils::SetNodeAttribute(onnx::AttributeProto, onnxruntime::NodeAttributes&)/onnxruntime_src/onnxruntime/core/graph/node_attr_utils.ccutils::HasName(attribute)No opset registered for domain onnxruntime::function_utils::IOTypeConstraintHelper(const onnx::FunctionProto&, std::unique_ptr&, const onnxruntime::InlinedHashMap, int>&, const onnxruntime::InlinedHashMap, int>&)::static_cast(i) < node_op_schema->inputs().size()std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::pair, std::__cxx11::basic_string >; _Alloc = std::allocator, std::__cxx11::basic_string > >; reference = std::pair, std::__cxx11::basic_string >&; size_type = long unsigned int] in function opset imports.it != opset_imports.end()domain_version != -1Too many inputs for op TinToutSaveModel is less than 0.onnx.ModelProtoProtobuf serialization failed.SaveModelWithExternalInitializersonnx::GraphProto onnxruntime::Graph::ToGraphProtoWithExternalInitializers(const std::string&, const onnxruntime::PathString&, size_t) constToGraphProtoWithExternalInitializersstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = unsigned char; _Alloc = std::allocator; reference = unsigned char&; size_type = long unsigned int] is less than 0.SaveWithExternalInitializersexternal_stream.is_open()LoadOpIdentifierOrtFormat/onnxruntime_src/onnxruntime/core/graph/op_identifier_utils.ccLoadRuntimeOptimizationRecordFromOrtFormat/onnxruntime_src/onnxruntime/core/graph/runtime_optimization_record_container.ccAttempting to load runtime optimization records for a previously loaded optimizer: std::deque<_Tp, _Alloc>::const_reference std::deque<_Tp, _Alloc>::operator[](size_type) const [with _Tp = std::shared_ptr; _Alloc = std::allocator >; const_reference = const std::shared_ptr&; size_type = long unsigned int]all_tensor_types_ir4TAggregatedTypesinput_arg->Type() != nullptrstd::unique_ptr onnxruntime::function_utils::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, bool) is not found in model local functionsstd::unique_ptr onnxruntime::function_utils::CreateSchema(const std::string&, const std::string&, const std::unordered_map, const onnx::FunctionProto*>&, const std::unordered_map, int>&, const onnxruntime::SchemaRegistryManager&, const onnxruntime::logging::Logger&, bool), domain: The given function name: onnx_func_protoModelProto does not have a graph.onnxruntime::Model::Model(onnx::ModelProto&&, const onnxruntime::PathString&, const onnxruntime::IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&)Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unsupported model IR version: ONNX 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.Missing model IR version., max supported IR version: No graph was found in the protobuf.Failed to load model with error: *ZN11onnxruntime14function_utils12CreateSchemaERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES8_RKSt13unordered_mapIS6_PKN4onnx13FunctionProtoESt4hashIS6_ESt8equal_toIS6_ESaISt4pairIS7_SD_EEERKS9_IS6_iSF_SH_SaISI_IS7_iEEERKNS_21SchemaRegistryManagerERKNS_7logging6LoggerEbEUlRNSA_16InferenceContextEE_*ZN11onnxruntime14function_utilsL22IOTypeConstraintHelperERKN4onnx13FunctionProtoERSt10unique_ptrINS1_8OpSchemaESt14default_deleteIS6_EERKNS_14InlinedHashMapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEiSaISt4pairIKSH_iEEEESO_EUlRKNS1_9NodeProtoEE_*ZN11onnxruntimeL40SaveRuntimeOptimizationRecordToOrtFormatERN11flatbuffers17FlatBufferBuilderERKNS_25RuntimeOptimizationRecordERNS0_6OffsetINS_3fbs25RuntimeOptimizationRecordEEEEUlmE_N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io16FileOutputStreamEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io19ZeroCopyInputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io15FileInputStreamETAggregatedTypesReceived null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringbad mlas activation kindd%OQuant GEMM format: AIsSigned(), BIsSigned() is not supported on this devicestoiInvalid processor interval: processor_id > 0ReadThreadAffinityConfig, skip affinity settingProcessor id must consist of only digits: std::vector > onnxruntime::concurrency::ReadThreadAffinityConfig(const std::string&)/onnxruntime_src/onnxruntime/core/util/thread_utils.ccstd::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 start from 1: processor_from > 0 && processor_to > 0processor_from <= processor_tostd::all_of(processor_str.begin(), processor_str.end(), ::isdigit)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&]Found invalid processor id in affinity string: Failed to read affinities from affinity stringFound out-of-range processor id in affinity string: , thread_pool_size: unnamed_thread_poolNumber of affinities does not equal to thread_pool_size minus one, affinities: std::unique_ptr onnxruntime::concurrency::CreateThreadPoolHelper(onnxruntime::Env*, OrtThreadPoolParams)actual_num_affinities == static_cast(options.thread_pool_size) - 1custom join thread function not set*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_DGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z21MlasQ4GemmBatchDriverI24MLAS_Q4_GEMM_DATA_PARAMSEv19MLAS_BLK_QUANT_TYPEmmmmPKT_PN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi256ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi256ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi128ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi128ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi64ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi64ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi32ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi32ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi16ELi4ELb0EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_ZN18BlockwiseQuantizerIfLi16ELi4ELb1EE10dequantizeEPfPKhPKfS3_iiPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z19MlasSQNBitGemmBatchmmmmmm24MLAS_SQNBIT_COMPUTE_TYPEPK28MLAS_SQNBIT_GEMM_DATA_PARAMSPvPN11onnxruntime11concurrency10ThreadPoolEEUllE_*ZN12_GLOBAL__N_128InitializeWorkspace_CompInt8EmmmmmPK28MLAS_SQNBIT_GEMM_DATA_PARAMSPvmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_*Z13MlasGemmBatchRK28MLAS_GEMM_QUANT_SHAPE_PARAMSPK27MLAS_GEMM_QUANT_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_*Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_SGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@KAQ5@.N3;A8X} <2?~>++{16?:>=4?~?A ':Y;Ӡ5֕8;Ž:n/ f9;rB20Size of affinity string must be e between 1 and @K@K@K@K@@@@r1r1r1r1*=*=*=*=(*>(*>(*>(*>>>>>A A A A ;;;;EۼEۼEۼEۼ# =# =# =# =ppppu>u>u>u>7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>>constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::concurrency::ThreadPoolParallelSection; _Dp = std::_Optional_base]SUCCESSINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROROKSystemError[ONNXRuntimeError]cWK?3*!std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat; _Alloc = std::allocator; reference = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat&; size_type = long unsigned int]mu 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() 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 onnxruntime::TimePoint onnxruntime::profiling::Profiler::Start()enabled_onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const std::string&)/onnxruntime_src/onnxruntime/core/common/status.cccode != static_cast(common::OK)DistributionEnqueueDistributionWaitRevokeWaitProfiler not started yet{"main_thread": {"thread_pool_name": "", "thread_id": "", "block_size": [], "core": }, "sub_threads": {"num_run": }}std::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()LogStart must pair with LogEndstd::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()thread::id of a non-executing threadvoid onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::chrono::time_point > >; _Alloc = std::allocator > > >; reference = std::chrono::time_point > >&]!points_.empty()void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function&)n >= 0More work items than threadsn <= num_threads_ + 1void onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection(onnxruntime::concurrency::ThreadPoolParallelSection&, std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]/onnxruntime_src/include/onnxruntime/core/platform/EigenNonBlockingThreadPool.hvoid onnxruntime::concurrency::ThreadPoolTempl::RunInParallel(std::function, unsigned int, std::ptrdiff_t) [with Environment = onnxruntime::Env; std::ptrdiff_t = long int]ParsePathRootFailed to parse path root: Parse/onnxruntime_src/onnxruntime/core/common/path.ccstatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString&)ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjlEUljE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_*ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE0_*ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRKSt8functionIFvlEEEEvPS1_lOT_lEUllE_*Z19MlasExecuteThreadedPFvPvlES_lPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK4EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK2EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK1EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_Z11Q80BlkQuantI16MLAS_Q4TYPE_BLK0EvPvPKfmmmPN11onnxruntime11concurrency10ThreadPoolEEUllE_N@??j@?A@{Gz? Failed to load library with error: Failed to get symbol lib.soseq(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(float)))optional(seq(tensor(double)))optional(seq(tensor(string)))optional(seq(tensor(bool)))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(float8e5m2))optional(seq(tensor(bfloat16)))optional(seq(tensor(float16)))optional(seq(tensor(complex64)))optional(seq(tensor(complex128)))optional(tensor(float8e4m3fn))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2fnuz))cores_countcpuinfo_get_%s called before cpuinfo is initializedVIWEF is not a valid year_month_day file "" failed: Invalid fd was supplied: fstatReceived negative size from stat callcloserealpath' is required to be non-empty.Unrecognized type value case (value_info name: value_info' is required but missing.elem_typekey_typevalue_typeO OO O  data_type) to UNDEFINED is not allowedfloat_dataint32_datastring_dataraw_datadouble_datauint64_dataTensorProto ( tensor name: ) should not be empty., but the ') should be file inside the ) should be stored in , but it is not regular file.TensorProto (tensor name: STRING data (tensor name: setting data_type field (tensor name: Data of TensorProto ( tensor name: ) is stored externally and should not have data field.) is stored externally but doesn't have a location.Location of external TensorProto ( tensor name: ) should be a relative path, but it is an absolute path: ' points outside the directory, but it doesn't exist or is not accessible.) is 0-element but contains data!) should contain one and only one value field.) should not be stored in raw_data fieldUnrecognized data_type (tensor name: O ` ` ` ` ` ` ` p p ` ` ` ` ` sparse_tensor_proto) must have rank 1.Sparse tensor values () must have a dense-rank > 0Sparse tensor () must have INT64 type.Sparse tensor indices () has values, but NNZ is ) index value at position [] out of range [0, ] not in sorted order.] out of range.) must have rank 1 or 2.) has no index values.) dimensions are not positive.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] not in lexicographic sorted order.DeleteFolder(): nftw() failed with error: DeleteFolderLoadTypeInfoOrtFormatLoadTensorDimensionOrtFormatLoadTensorShapeOrtFormatLoadSequenceTypeOrtFormatLoadMapTypeOrtFormat is not supported currentlyNull tensor type info. Invalid ORT format model.dim_param value with no name. Invalid ORT format model.LoadTensorTypeAndShapeOrtFormatNull sequence type info. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.Null map type info. Invalid ORT format model.Null value type info in fbs::MapType. Invalid ORT format model.UnmapFilemunmap failed. error code: error msg: - error code: Failed to close file descriptor ReadFileIntoBufferfile_path == nullptroffset < 0length > buffer.size()lseekFile: , offset: , length: ReadFileIntoBuffer - unexpected end of file. MapFileIntoMemorymmapnftw_removeremove() failed. Error code: , path: ThreadMaincpu , mask: , error code: does not exist, skipping it for affinity settingpthread_setaffinity_np succeed for thread: constexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = std::vector; _Dp = std::_Optional_base, false, false>]pthread_setaffinity_np failed for thread: . Specify the number of threads explicitly so the affinity is not set.Negative thread index is not allowedonnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&)custom_create_thread_fn returned invalid handle.pthread_attr_init failed, error code: pthread_attr_setstacksize failed, error code: pthread_create failed, error code: index >= 0\/onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*)coreinit' of node: OpType: ==> Context: ' has been used as graph input names multiple times.Graph must be in single static assignment (SSA) form, however 'Tensor 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 ' is not output of any previous nodes.' has been used as output names multiple times.void std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::pop_back() [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator]Warning: Model contains experimental ops:Bad node spec for node. Name: attrAttribute (name: type field and data field mismatch in attribute ) should not contain more than one value field.) should refer to attribute in parent node.op_type, type: NodeProto (name: No opset import for domain '' appeared multiple times.ai.onnx.mlai.onnx.trainingNo Op registered for with domain_version of ) has input size not in allowed input sizes.) has output size not in allowed output sizes.) has more inputs ()'s input ) has more outputs ()'s output for operator Unrecognized attribute: has unknown expected type' is missing.Required attribute ') has zero input and zero output. is deprecated in domain_version of is marked single but has an empty string in the graphMismatched 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'OpRpRpR|SdSpRpRpRpRpRLSpR5SpRv_initialv_final_and_scan_outputsThe 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.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].std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; reference = const onnx::TensorProto*&]Graph attribute inferencing returned type information for std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator; reference = const onnx::TypeProto*&; size_type = long unsigned int]Loop 'body' subgraph outputs should all be tensors but output outputs. Expected was not a tensor.Scan input was notScan 'body' subgraph outputs should all be tensors but output ) is not equal to number of scan inputs (Number of scan input axes specified () is not equal to number of scan outputs (Number of scan output axes specified (N4onnx7checker15ValidationErrorEZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE26ScheduleOnPreferredWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionERN4absl12lts_2024011613InlinedVectorIiLm11ESaIiEEEjjSt8functionIFvjEEEUlvE_ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE21RunInParallelInternalERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjbSt8functionIFvjEEEUlvE_All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.ypes up to IRv4.Enable broadcastingIf set, defines the broadcast dimensions. but subgraphs produce If node has else= then=Mismatched type for output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output output = Cast (X_greater)X_random = RandomUniformLike (input)X_greater = Greater (X_random, input)Invalid data type Invalid rank for ) vs ( broadcasting: (Wrong op_type name for running propagation: sparse_valuevalue_intvalue_intsvalue_stringvalue_stringsOne 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.Input tensor must be 2-dimensionalInput tensor must be 1-dimensionalAll inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)Attribute '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.Attribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorAn 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.The value for the elements of the output tensor in sparse format.The value for the sole element for the scalar, int64, output tensor.The values for the elements for the 1D, int64, output tensor.The value for the sole element for the scalar, float32, output tensor.The values for the elements for the 1D, float32, output tensor.The value for the sole element for the scalar, UTF-8 string, output tensor.The values for the elements for the 1D, UTF-8 string, output tensor.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.The 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.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.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) }> } Constrain input/output to boolean tensors.Loop '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 tensor data type declared= inferred=Mismatched type: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.vJ LvJvJDKKvJvJJJseq(optional(map(sparse_tensor(Unsuported type proto value case. )PO )P )PdPP )P4R4SNDataTypeUtils::FromDataTypeString - Received invalid data type string ' should specify a shapeAttribute Negative values are not allowed in a shape specification*ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25BinaryBitwiseDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx25BinaryBitwiseDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23ImageDecoder_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_20Bernoulli_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ConstantOfShape_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_All Tensor and Sd Sequence typestypes to all numnumeric tensors. to high-precision numeric tenso 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) output = 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)List of tensors for X_ReduceMax = ReduceMax (input, axes) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) 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) weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)expanded_target = Unsqueeze (target, axes) 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_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_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_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 = Squeeze (weight_gather_temp_1, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) const_zeroconst_oneconst_ignore_indexconst_zero_floatconst_one_floatstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::TensorShapeProto*; _Alloc = std::allocator; reference = const onnx::TensorShapeProto*&]X_ReduceMax = ReduceMax (input)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. periodic X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) output = NegativeLogLikelihoodLoss (X_Log, labels, weights)output = NegativeLogLikelihoodLoss (X_Log, labels) 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) Shape3Dlog_prob = Identity (X_Log)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) } 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) } { 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) } Dimension 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.Constrain 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.Input can be of any tensor type.input and zero_point pair is expected to have same type.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.Rank of input Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions. does not match the equation indices.*ZN4onnx11GetOpSchemaINS_15STFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26MelWeightMatrix_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx29CosineSumWindowOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29CosineSumWindowOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14DFT_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEPFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE*ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx11GetOpSchemaINS_14Add_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE_*ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_A {name} window with length: size. The output has the shape: [si to all numeric to numeric tens to all tensors.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.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).Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeconst_zero_castedconst_one_castedexpanded_targetinput_gather_elementloss_NCddloss_N1ddloss_Nddweight_gatherloss_unweightedloss_sumweight_gather_sumconst_zero_target_typedexpanded_target_int64transform_targetssqueeze_maskweight_gather_tempweight_gather_temp_1input_gather_element_transformX_NCDX_NDCX_LogSMX_LogSM_NCDX_shapeX_Logstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::FunctionBodyHelper::AttributeProtoWrapper; _Alloc = std::allocator; reference = onnx::FunctionBodyHelper::AttributeProtoWrapper&] 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. 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. 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.Value of alpha default to 0.2Value of beta default to 0.5Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxWhether 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.Number of top elements to retrieveDilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.The 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. Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.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] = 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.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 to 1 along each spatial axis.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.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.'shape' input must be 1D tensorInvalid value for attribute axisInvalid value for attribute kMaxUnpool 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 tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specified*ZN4onnx11GetOpSchemaINS_14DFT_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_*ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_*ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcastip value of the Lp norm used to pool over the input data.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Constrain input and output types to float and 8 bit tensors.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.Additional 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.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.Stride along each spatial axis.dilation value along each spatial axis of the filter.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 padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.) for attribute 'axis'Invalid value(Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Attribute pads has incorrect sizeThe pads attribute cannot be used simultaneously with auto_pad attributeinput must have rank 3.Attribute pads must have an even size 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. Factor 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.Constrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.input_meaninput_varrunning_meanrunning_varThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0. { 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) } lambdIndicate 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 overConstrain input and output types to float tensors.Scaling parameter.The exponent.1-D tensor of floatsInput is ether string UTF-8 or int32/int64Maximum 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) { 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) } 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.image_shapeblock_shapeThe 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 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 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.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.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If 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.is_testsaved_meansaved_var 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. Constrain mean and variance types to float tensors. It allows all float type for U.(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.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.NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)Mean2D = ReduceMean (XU)MeanOfSquare = ReduceMean (Square)Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)SquareOfMean = Mul (Mean2D, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)StdDev = Sqrt (VarPlusEpsilon)Normalized = Div (Deviation, StdDev)NormalizedT = Cast (Normalized)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedT, Scale2D)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)NumReducedAxes = Neg (Axis1D)FloatEpsilonEpsilon = Cast (FloatEpsilon)XShape = Shape (X)Rank = Size (XShape)Zero1D = Constant()Axis1D = Constant()X2D = Flatten (X)XU = Cast (X2D)Square = Mul (XU, XU)Axes_1 = Constant()Deviation = Sub (XU, Mean2D)B2D = Flatten (B)Biased = Add (Scaled, B2D)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)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.ExponentX_RMEX_squaredE_XsquaredVarianceX_varianceProcessed_STD*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_11EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx26LpPoolOpSchemaGenerator_11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_11EPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_*ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_*ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_St15_Sp_counted_ptrIPN4onnx13FunctionProtoELN9__gnu_cxx12_Lock_policyE2EE*ZN4onnx11GetOpSchemaINS_29GroupNormalization_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_*ZN4onnx11GetOpSchemaINS_29LayerNormalization_Onnx_ver17EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Col2Im_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21DeformConv_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_*ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_to only numeric and output to alll tensor types.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 vic tensor types.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::Common::Status::State; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnx::Common::Status::State&](line: column: [ParseError at position Error context: Expected character infinfinitynanIdentifier expected but not found.Integer value expected, but not found.Expecting 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).String value expected, but not found.Unexpected literal type.Unhandled type: %dIstol and specified value's typeMismatch between expected type Unexpected attribute type.Empty list attribute value requires type annotation.Singleton attribute value cannot be specified as a list./ 44std::__shared_ptr_access<_Tp, _Lp, , >::element_type& std::__shared_ptr_access<_Tp, _Lp, , >::operator*() const [with _Tp = onnx::FunctionProto; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool = false; bool = false; element_type = onnx::FunctionProto]Duplicate type constraint namestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::OpSchema::TypeConstraintParam; _Alloc = std::allocator; reference = onnx::OpSchema::TypeConstraintParam&] 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 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) The 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).select_last_indexConstrain input and output types to high-precision and 8 bit numeric tensors.A 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).{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.Constrain input and output types to numeric and Boolean tensors. 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.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) } 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].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.The sequence output for the hidden is optional if 0. Default 0.output_sequenceOptionalHasElement 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.OptionalHasElement is expected to have 1 input.Input must be an optional-type value containing an element with type information.'axis' must be in [-rank(indices), rank(indices)-1]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.axes as an input and attribute cannot be specified at the same time.First input tensor must have rank 3*ZN4onnx16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_*ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_*ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_*ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZN4onnx17ReduceOpGeneratorEPKcS1_bbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEbEUlRS6_E_*ZZN4onnx17ReduceOpGeneratorEPKcS1_bbS1_St8functionIFbRKNS_24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEEEbENKUlRS6_E_clESD_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx25ReduceDocGenerator_opset1EPKcS1_iEUlRNS_8OpSchemaEE_*ZZN4onnx25ReduceDocGenerator_opset1EPKcS1_iENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_*ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_*ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_*ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_32DynamicQuantizeLinear_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_29OptionalGetElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_*ZN4onnx11GetOpSchemaINS_29OptionalHasElement_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Optional_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE0_One (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 nf not specified. type to all tensor or sequence The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead ostead of `True`.ONNX Schema (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty()): failed validating the check: Error parsing function body:Extra unparsed input unexpected.Mismatch between number of inferred and declared dimensions. inferred=Attribute dtype should be of integer type and specify a type.Input type for input at index 0 is null. Type info is expected. sum of split values=Invalid value of attribute 'axis'. Rank=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=new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=Input type was nullInput element type of Element type of tensor or sparse tensor input was unknown does not match existing output type of Output was expected to have tensor type. Got Element type of sequence input was unknownElement type of optional input was unknownKey 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 tensoraxis must be in [-rank, rank-1].(Optional) The data type of the tensors in the output sequence. The default type is 'float'.positionConstrain to any tensor type.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrain 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.Constrain output types to any tensor type.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 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..stepsA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedType 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.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).squeezedexpandedBlocks of [blocksize, blocksize] are moved.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.repeatsConstrain repeat's type to int64 tensors.roiThree 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. 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) ``` .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. 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.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.Constrain input `X` and output `Y` types to all tensor 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).(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).Constrain input to only numeric types.depth expected to have tensor or sparse tensor type. Got: unknownElement type of input 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 null expected to be a sequence typeGraph attribute inferencer for "body" not available expected to have type infoInvalid ``body`` argument. Expected a graphInput 0 expected but not providedExpected a sequence type for input 0std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; reference = onnx::OpSchema::FormalParameter&; size_type = long unsigned int]Expected 1 or more inputs.Expected 1 or more outputs.SequenceMap_loop_body_itercount_cond_in_cond_outSequenceMap__seqlen_cond_seqemptySequenceConstruct is expected to have at least 1 input. is null. Type info is expected.Input type for input at index 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=*ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_Stypes to any tenConstrain to all. It must be 4 for 2D or 5 for 5D.Length of input 'size' is output = Cast (input)k2x_shape = Shape (input_data)axes_input = Constant ()x_shape_alldims = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)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)x_shape_alldims2 = Shape (padded_input)x_shape2 = Gather (x_shape_alldims2, axes_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)padded_input = Pad (input_data, pads)x_shape2 = Shape (padded_input)output_data = Slice (padded_input, start_dims, end_dims)Unexpected axis value: . Expected range [ is referred to more than once.Axis output = Cast (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).inverse_indiceslength of each output. Values should be >= 0.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).List 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). 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.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". 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).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". 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).constant_valueConstrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.outputs...Which axis to split onList 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`.One float, indicates the value to be filled, default is 0paddingsNew shapetilesConstrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.The scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinearConstrain output types to bool, int32, int64, float16, float, double tensors.width_scaleheight_scaleThe 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'.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`Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]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.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). 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. 'step' cannot be 0 for SliceInput rank for starts and ends should be the same: (indices must be in [-rank, rank-1].Indices tensor must have rank >= 1'input' must have rank >= 2'sequence_lens' must have rank of 1 is out of bounds.*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21GridSample_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_24CenterCropPad_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Where_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21AffineGrid_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21GridSample_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE0_*ZN4onnx11GetOpSchemaINS_19CastLike_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver19EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_and output to only numeric types'X' and output 'Y' to all tensorDimension value inferred () is not equal to the existing dim value ( is undefined so it cannot be parsed.ParseData type mismatch for tensor: The type of tensor: . Expected:) and outputs (Mismatch between number of splits (The input is not evenly splittablerank 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 lengthAttribute value for pads is requiredAttribute pads has incorrect lengthBoth `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` tensorNumber of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.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".) does not match the number of axes (Number of elements of input 'sizes' () must be same as rank of input 'X' (Number of elements of input 'scales' (Input shape must have either [C] or [1,C] dimensions where C > 0Exactly one of 'cats_*' attributes must be provided.ZipMap input shape should be 1D or 2D.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeThe input tensor and grid tensor must have the same rank for GridSample. 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: Got input tensor rank: Got grid tensor rank: expected to have tensor or sparse tensor type: 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.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.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.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Node id for each node. Ids may restart at zero for each tree, but it not required to.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.'std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const onnx::SparseTensorProto*; _Alloc = std::allocator; reference = const onnx::SparseTensorProto*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = const onnx::TensorProto*; _Alloc = std::allocator; reference = const onnx::TensorProto*&; size_type = long unsigned int]Data for input already exists.type case mismatch. existing=Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension key type mismatch from MapProto. existing=type case unsupported. existing=N4onnx15shape_inference19GraphInferencerImplEN4onnx15shape_inference20InferenceContextImplE*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33TreeEnsembleRegressor_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver3EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_33ArrayFeatureExtractor_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22StringSplit_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25RegexFullMatch_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23StringConcat_Onnx_ver20EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE0_*ZN4onnx21KeepAspectRatioHelperENS_21KeepAspectRatioPolicyERKNS_16TensorShapeProtoERKSt6vectorIlSaIlEERS6_EUlffE_*ZN4onnx15PadDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_*ZZN4onnx15PadDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver15EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver18EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver16EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_22DataPropagationContextEE0_*ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_j*d)(''d'4'&,000l0,0/l/.,,,,, .,,,,,-,,,,|.t,t0\0<00/t//.t,t,t,t,t,-7::77X:7777999887<=DCCdCADBABAdA@@<=??$?<=<=<=<=>\>>=unk__type case unsupported for symbolic shape inference. inferred=6LPP_6LP6LP__6LP6LP8`_Inference error(s): , node name: (op_type:. No opset import for domain optype typestr: , has unsupported type: has inconsistent type has unsupported type Cannot infer type and shape for node name std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator; const_reference = const onnx::OpSchema::FormalParameter&]Container for generated shape data cannot be nullptr when enable_data_propagation option is set. were provided inputs but Graph has were provided.in initializers. Cannot find missing input: Cannot use the same name as both a subgraph initializer and subgraph input: The number of graph input cannot be smaller than the number of node inputGraph initializer names must appear after the actual inputs: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::NodeProto; _Alloc = std::allocator; reference = onnx::NodeProto&; size_type = long unsigned int]N4onnx19TensorProto_SegmentEN4onnx16TensorAnnotationEN4onnx22StringStringEntryProtoEN4onnx17TrainingInfoProtoEN4onnx12assert_errorEFvRN4onnx22DataPropagationContextEEPFvRN4onnx22DataPropagationContextEEN4onnx8internal14MutableVisitorEN4onnx8internal15AttributeBinderEN4onnx22DataPropagationContextEN4onnx15shape_inference26DataPropagationContextImplE   onnx.AttributePronnx.ValueInfoPronnx.TrainingInfonnx.StringStrinStringEntryProtoonnx.TensorAnnotonnx.TensorProtoL;,;L;L;d;;L;;;d;/build/Release/_deps/protobuf-src/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): /build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (backup_bytes_) == (0): CHECK failed: (count) >= (0): /build/Release/_deps/protobuf-src/src/google/protobuf/io/zero_copy_stream_impl.ccCHECK failed: !is_closed_: 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: (buffer_used_) == (buffer_size_): Xv::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Prog::Inst; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Prog::Inst&; std::size_t = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = int&; std::size_t = long unsigned int][libprotobuf %s %s:%d] %s unexpected errorstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::RuneRange; _Alloc = std::allocator; reference = re2::RuneRange&]LuLlLtLmLoNlMnMcNdPc/build/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./build/Release/_deps/re2-src/re2/regexp.ccRegexp not destroyed.Unexpected op in Regexp::Equal: ,    , L std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = re2::Regexp*; _Alloc = std::allocator; reference = re2::Regexp*&; size_type = long unsigned int]/build/Release/_deps/re2-src/re2/bitstate.cctypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Job; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Job&; std::size_t = long unsigned int]GrowStack() failed: njob_ = job_.size() = typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long unsigned int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = long unsigned int&; std::size_t = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = const char*; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = const char*&; std::size_t = long unsigned int]Unexpected opcode: G$l T /build/Release/_deps/re2-src/re2/compile.ccshould never happen/build/Release/_deps/re2-src/re2/dfa.ccunhandled opcode: T|TDeadState in RunStateOnByteNULL state in RunStateOnByte,,,,,StateSaver failed to restore state.typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray::IndexValue; _Dp = re2::PODArray::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray::IndexValue&; std::size_t = long unsigned int]/build/Release/_deps/re2-src/re2/nfa.ccstd::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::NFA::Thread; _Alloc = std::allocator; reference = re2::NFA::Thread&]unhandled in AddToThreadq8@ P/build/Release/_deps/re2-src/re2/parse.ccBad hex digit u%l$l$l$l$l$l$l$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%$$$$$$$$j%$$$$$$$$$$$$$$%$$$$$$%$$_%$$T%$$$/build/Release/_deps/re2-src/re2/simplify.ccCase not handled in ComputeSimple: &%%%%%%|%|%|%&D&%%%%%%%%&%Bad reference count DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is 8:8988777:88:8:8888888:9888o7i799899888888989@8@8'7789@888@8@8@8@8@8@88NumCapturesWalker::ShortVisit calledCompiler::Copy called!/build/Release/_deps/re2-src/re2/walker-inl.hvoid std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]/opt/rh/gcc-toolset-12/root/usr/include/c++/12/bits/stl_stack.hStack not empty.Unhandled in stepRepetitionWalker::ShortVisit calledCoalesceWalker::ShortVisit calledSimplifyWalker::ShortVisit calledRE2: invalid startpos, endpos pair. [Unexpected opcode in short circuit: startpos: endpos: text size: DFA out of memory: pattern length program size list count bytemap range SearchDFA inconsistencySearchOnePass inconsistencycontext does not contain textBad args: nsubmatch=SearchBitState inconsistencySearchNFA inconsistencyAnyvoid std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]Walk NULLstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; reference = re2::WalkState&] [truncated]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Frame; _Alloc = std::allocator; reference = re2::Frame&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = re2::Splice; _Alloc = std::allocator; reference = re2::Splice&; size_type = long unsigned int]unknown round: Concat of RE2: unexpected op: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Regexp*; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Regexp*&; std::size_t = long unsigned int]Malformed repeat Simplify case not handled: xxxx00xxxxxxxx8xNo ranges in char classMissing case in Compiler: l::pop() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray::IndexValue; _Dp = re2::PODArray::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray::IndexValue&; std::size_t = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; reference = re2::Prog::Inst&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = re2::Prog::Inst; _Alloc = std::allocator; reference = re2::Prog::Inst&; size_type = long unsigned int]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = short unsigned int; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = short unsigned int&; std::size_t = long unsigned int]Q11F0F0F0010std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; reference = re2::WalkState&]std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::const_reference std::__cxx11::basic_string<_CharT, _Traits, _Alloc>::front() const [with _CharT = char; _Traits = std::char_traits; _Alloc = std::allocator; const_reference = const char&]Unknown encoding \d\D\s\S\w\WError parsing 'Error compiling 'Bad call to ParseState::ParsePerlFlagspattern too large - compile failedtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::InstCond; _Dp = re2::PODArray::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::InstCond&; std::size_t = long unsigned int]/build/Release/_deps/re2-src/re2/onepass.cclcccllltvdvcckccccccccccccccccDjccccccccccccccccccccccccccc|h gcfcccccccccccccccccccccccccccc|kTdNʼnN3re214ToStringWalkerE  / / _ _ 00) ) ( ( G  ( ) / / _ _ 00Ƥpz|+0-g-o-p---GIlq:@[    ( * 9 < D F H J M U V X Z ] ] ` c f o w  ooppjjjj ª۪ߪ ^`|Pmpt`lnprs JMO`j,P0Y7?ywx  ??0?011111122*2G2P2P2`222223MMƤ(+6799wy@O55OOoo++<>||~~D D R R z | !!@!D!K!K!!!!!!!!!!!!!!!!!!!!" #!#|#|#####%%%%%%o&o&'''''')))))))*0+D+G+L+))bbdf \\^^^^``uu00 ![[jk>>@@ P$$   ?? 88iiŨΨ٨-0>7?ywx  ??^^``||~~uu   p p   O O y y ??446688mm@@ajt|D D R R z | !!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!@!D!J!M!O!O!!!!# #(#+#&$@$J$$$%g''''''))))))s+v++++,,P.Q.....////0000 0 06070>0?00011111122*2G2P2P2`222223MMƤ !(+69wy[[jk))@Obbdfii >>@@\\^^0S__      (([[{{::<<    E E } } ## # #)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.U.U.W.W.Y.Y.[.[.00 0 0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bbooWW  ? ? 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H P X -// "dg000011223W3foq ͩЩ٩ީߩ@ U X _ ` r x  ;;-0Fgi{oKUppQ T  *0-000 -@UW_PRA000068<>>@ACDFO 4 ' 0 9 .0/011122`2~2`|װנooooߦ8@ J....//0000!0)080;04MNmp      ( * 0 2 3 5 6 8 9 < < > B G H K M Q Q Y \ ^ ^ f v `eghj @Epsuwz}&*]afj EHMPWYY[[]]_}&!&!ee (*02359<DGHKMPPWW]cflpt0J!#$&*,_,-%-'-'-----HJMPVXXZ]`Z]|------------------  &(.'0.40484jp|;   578;FPY P U c f  O/++xx--@./// 578<<?? #$n$p$t$$C%,,,,37?P&)fjzV`xTV  9;>@DFFJPRq=+0;@HPQ`es GPY`S`mptx| @[`{tt~~  @@d e ??56  d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$')s+v++++.].//00000 00070<0?00000001111 2_22222X33MM!09..ϩϩ[[jk>?0RTfhk ;@[eppp6@MPY\_46G0484sz   * . ` d f o 0coo0484sz   * . ` d f o @S((MRu1/111 68EPl jjjjh8jLP~ 5 9 ? 1VY`~!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~   ?AJVoqPpP=@ptvDFF+0FKPY^_N3re214CoalesceWalkerEN3re214SimplifyWalkerEN3re26Regexp6WalkerIPS0_EEN3re26Regexp6WalkerINS_4FragEEEN3re28CompilerEN3re216RepetitionWalkerEN3re217NumCapturesWalkerEN3re26Regexp6WalkerIiEE09AFaf09AZ__azAZ !/:@[`{~ ~az!~09 AZaz09AZaz09AZ__az 09AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeChorasmianCoCopticCsCuneiformCypriotCypro_MinoanCyrillicDeseretDevanagariDives_AkuruDograDuployanEgyptian_HieroglyphsElbasanElymaicEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhitan_Small_ScriptKhmerKhojkiKhudawadiLaoLatinLepchaLimbuLinear_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_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangsaTangutTeluguThaanaThaiTibetanTifinaghTirhutaTotoUgariticVaiVithkuqiWanchoWarang_CitiYezidiYiZanabazar_SquareZlZpZsno errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )unexpected )trailing \invalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture group[: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:]INFOWARNINGFATALAZ ajkk lrss tz F y/279HJwxxy~Oa8  ~"3::+*;<==]>>(*?@?*ABCC=DDEEEGFOPP*QQ*RR*SS.TT2VW3YY6[[5\\O``3aaKcc1ee(ffDhh/ii-jjDkk)llAoo-qq)rr+uu*}})&C&*&'%EETpsvw{}t&%@?  @60]#~P/ 0122N3344M5=>>D?@ABBCIJJ<KOP_`bcc$d/1V0a```  З‰@@yy}}8`aa:bA '(/078?@EHMQQSSUUWWYY[[]]__`ghopqJruVvwdxyz{p|}~   &!&!*!*!!+!+!2!2!N!N!`!o!p!!!!$$$$,/,00,_,`,a,b,b, c,c,d,d,e,e,f,f,g,l,m,m,n,n,o,o,p,p,r,s,u,v,~,,,,,,,,-%-'-'-----@JKK=vLm"/2oy|}}u~Z0ZZZZZZZZçħħŧŧZƧƧuǧʧЧѧ֧٧SS`p0h!: AZ'((O(pz'|'''  @   @n_n `nn!""Cno argument for repetition operator00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899@B  []^-\\r\t\n\f\x%02x\x{%x}(){}[]*+?|.^$\/sys/devices/system/cpu/kernel_maxfailed to parse file %s: file is emptyfailed to parse file %s: "%.*s" is not an unsigned numberapicidw/CPUSOCAMDVIAIDTMMXAPUEngIntelCyrixGeodemodelRadeonRADEONfamilyMobileGenuine12-Core16-CoreSample,Sample:QuadCoreSix-CoreProcessorDual-CoreQuad-CoreTransmetaEight-CoreTriple-CoreEngineeringhP@failed to read file %s: insufficient buffer of size %zu/proc/cpuinfo/sys/devices/system/cpu/possible/sys/devices/system/cpu/presentfailed to allocate %zu bytes for descriptions of %u x86 logical processorsfailed to parse processor information from /proc/cpuinfofailed to allocate %zu bytes for descriptions of %u logical processorsfailed to allocate %zu bytes for mapping entries of %u logical processorsfailed to allocate %zu bytes for mapping entries of %u coresfailed to allocate %zu bytes for descriptions of %u coresfailed to allocate %zu bytes for descriptions of %u core clustersfailed to allocate %zu bytes for descriptions of %u physical packagesfailed to allocate %zu bytes for descriptions of %u L1I cachesfailed to allocate %zu bytes for descriptions of %u L1D cachesfailed to allocate %zu bytes for descriptions of %u L2 cachesfailed to allocate %zu bytes for descriptions of %u L3 cachesfailed to allocate %zu bytes for descriptions of %u L4 caches%s %s$)4?::999$)9[9/9G;;)g;$)$)$)$)$)$)$)$)$)$)$)$)$)$)3$)$)$)3:::_:$)$)$)9$)$)8$)$)$)8$)$)$)$)$)$)$)$)y8J88777$)$)_707766t6E6WF FEE}ENEEDDDTD$)$)#DDC$)CCaC0CB$)$)BB$)gBGB$)B)A$)AA~A^A$)$)JA6A"AA$)$)@$)@@P@)d)!@$)?)$)??e?6??d)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)>$)$)$)$)$)$)$)$)$)$)$)$)$)$)$)>>G)l>L>,> >$)$)$)G)$)$)$)$)$)==y=-=<$)$)$)$)$)<$)$)$)$)$)<~::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnx::TensorShapeProto_Dimension*; _Alloc = std::allocator; reference = onnx::TensorShapeProto_Dimension*&; size_type = long unsigned int]'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Pads has incorrect number of values. Expected 2 * values. Got Input axes has invalid data'step' cannot be 0Both 'split' input and 'num_outputs' attribute were givenAttribute `num_outputs` value cannot be lower than 1Neither 'split' input nor 'num_outputs' attribute has been givenCenterCropPad op must have 2 inputs.`shape` only supports `int32_t` or `int64_t` inputsNumber of elements of input 'shape' ( and Output does not match type of output: expected to have tensor or sparse tensor typeInvalid attribute perm {}, input shape = {Attribute perm for Transpose has repeated value: HygonRDCDM&PRiseNSCSiSNexGenUMCError in cpuinfo:   @@ @@00@0 @@ @@`@ @ @  @@@@0 @`   @@`@     >     0@~~~~~~~~~~~~~~~~NumGroupsAxes2Square = Mul (X3D, X3D)Deviation = Sub (X3D, Mean)ScaleT = Cast (scale)BiasT = Cast (bias)C = Shape (X)GroupSize = 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)Mean = ReduceMean (X3D, Axes2)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)ScaleShape = Constant ()ScaleReshaped = Reshape (ScaleT, ScaleShape)BiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)ATenConstantFillAlpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) HalfOne CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) NormalizedV = Cast (Normalized)Scaled = Mul (NormalizedV, Scale2D)X_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) Input 'input_ids' is expected to have 2 dimensions, got Input 'input_features' is expected to have 3 dimensions, got Input 'decoder_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 axis value is invalid for a tensor of rank Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps'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.data tensor must have rank >= 1axis must be in [-r, r-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.Attribute 'pooled_size' must be >= 1.Attribute 'max_output_boxes' must be >= 1.Input's shape must be 4-D) + bottom_border () + right_border () + scale[0] () + scale[1] ('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 () needs to be greater than or equal to the left_border (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!Value of attribute should be of integer type and specify a type. does not specify a valid type.Mismatch between the sum of 'split' () and the split dimension of the input (One and only one attribute must be set out of expected to be a 1D tensor but was expected but not providedframe_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!Weight rank must be 1.Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Input tensor must have rank 2Output type must be int32 or int64axis attribute value is_onesided and inverse attributes cannot be enabled at the same timeinput tensor must have rank >= 2, including the complex dimension.. Valid values are '-rank <= axis && axis != -1 && axis < rank - 1'dft_length input must be a scalar.rank == static_cast(new_shape_proto.dim_size())%s:%u: %s: Assertion `%s` failed: rank should be equal to 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.K 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 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. expected to have tensor or sparse type expected to have tensor typeAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedSecond input tensor has wrong dimensionvoid onnxruntime::If::Init(const onnxruntime::OpKernelInfo&)info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()not support normalize yet.!normalize_onnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr]GetParameterssubgraph output present_key_self_0 is expected to have 4 dimension, got onnxruntime::common::Status onnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const OrtValue*, int, const std::vector&, std::vector&, const onnxruntime::contrib::GenerationDeviceHelper::CreateWhisperEncoderInputsFunc&, const onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, OrtValue&, onnxruntime::Stream*)cpu_allocator shouldn't be nullptr;f?F?&TpxAD?Word 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 1.maskprocBroadcastLooper requires two tensors as input.void onnxruntime::BroadcastLooper(TBroadcastHelper&, const ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = contrib::{anonymous}::QLinearBroadcastHelper]onnxruntime::common::Status onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeeds(const onnxruntime::Tensor&, const OrtValue*, const std::vector&, int, int, std::vector&, const onnxruntime::contrib::GenerationDeviceHelper::CreateEncoderInputsFunc&, const onnxruntime::contrib::GenerationDeviceHelper::AddToFeedsFunc&, onnxruntime::IAllocatorUniquePtr&, OrtValue&, onnxruntime::Stream*)onnxruntime::common::Status onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeeds(onnxruntime::AllocatorPtr, gsl::span, const std::vector&, const std::vector&, const std::vector&, std::vector&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, int, onnxruntime::Stream*, bool, int, onnxruntime::contrib::transformers::Sequences&, int, bool)Invalid hidden_states expension: has_hidden_state_ == falseonnxruntime::common::Status onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeeds(onnxruntime::AllocatorPtr, gsl::span, const std::vector&, const std::vector&, const std::vector&, std::vector&, onnxruntime::contrib::GenerationDeviceHelper::DeviceCopyFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, onnxruntime::contrib::GenerationDeviceHelper::ExpandBufferFunc&, int, onnxruntime::Stream*, bool, int, onnxruntime::contrib::transformers::Sequences&, int, bool)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char]/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/qlinear_binary_op.ccIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if giventensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if giventensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if giventensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char]34N6bestla6kernel3jit22DecompressS4S8_AVX512FEf@DfDf Attention cannot have both past and relative_position_biasInput 'input' is expected to have 3 dimensions, 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: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 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 Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is set q_hidden_size= k_hidden_size= v_hidden_size=bias_dims[0]=xbyak[FAIL] XTILE_DATA request failed: %ld[FAIL] prctl(ARCH_GET_XCOMP_PERM) error: %ldstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::thread; _Alloc = std::allocator; reference = std::thread&; size_type = long unsigned int]@33@Conversion Erroren_US.UTF-8Conversion Errorstd::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState; _Sequence = std::deque, std::allocator > >; reference = re2::WalkState&]The input tensor must not have any dynamic (-1) dimensions. Input shape:onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, onnxruntime::TensorShapeVector&, bool)/onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.hA dimension cannot be less than -1, got At most one dimension can be -1.The dimension with value zero exceeds the dimension size of the input tensor.i < input_shape.NumDimensions()The input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape_size % size) == 0input_shape_size != -1requested_shape[i] >= -1unknown_dim == -1, requested shape:input_shape_size == sizeCannot concatenate scalars got: input_rank == reference_rankData type mismatchonnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const InlinedTensorsVector&, onnxruntime::Prepare&) constRanks of input data are different, cannot concatenate them. expected rank: Non concat axis dimensions must match: Axis has mismatched dimensions of lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = long unsigned int; int64_t = long int]/onnxruntime_src/onnxruntime/core/providers/cpu/signal/window_functions.cclowest_index >= 0 && lowest_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.highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = short unsigned int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = unsigned char; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = long int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = short int; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = signed char; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = double; int64_t = long int]onnxruntime::common::Status onnxruntime::CreateMelWeightMatrix::operator()(onnxruntime::OpKernelContext*, int64_t, int64_t, int64_t, float, float) [with T = float; int64_t = long int]Data type is not supported in th|||||||||||||||| #<L>?@F@$@D,D !T""<XPkernel_shape num_dims is not compatible with W num_dims.kernel_shape is not compatible with W shape. kernel_shape: channels_last: Input channels C is not equal to kernel channels * group.Output channels M is not divisible by group. C: kernel channels: M: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != dimension != Invalid input scale: Invalid input B: Invalid input mean: Invalid input var: void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const GemmWeights&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*)const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = const float; size_t = long unsigned int]const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [with T = float; size_t = long unsigned int]Invalid GRU reset gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string&))(const float*, float*, float*, int, float, float)Invalid GRU hidden gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string&))(float*, const float*, const float*, float*, int, float, float)void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = gsl::details::span_iterator; TSpanBIter = gsl::details::span_iterator; TSpanCIter = gsl::details::span_iterator]Unsupported AutoPad Type.void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) constonnxruntime::TensorShapeVector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, onnxruntime::TensorShapeVector*, bool) constinput_shape.Size() > 0 || input_shape[0] == 0void onnxruntime::PoolAttributes::InferOutputSize(gsl::span, onnxruntime::TensorShapeVector*, onnxruntime::TensorShapeVector*, bool) constinput_dims.size() >= 2onnxruntime::LayerNormImpl::LayerNormImpl(const onnxruntime::OpKernelInfo&, bool, bool)op_kernel_info.GetAttr("axis", &axis_).IsOK()bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext*, gsl::span, bool) [with AGG = ReduceAggregatorArgMinLastIndex]/onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.hPer-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.ComputePadAndOutputShapeInferPadsAndOutputShapeNot enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.onnxruntime::common::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&) [with InputType = float; ThresholdType = float; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]nodes_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()nodes_modes.size() < std::numeric_limits::max()(coor.node_id >= 0 && coor.node_id < n_nodes_)A node cannot point to itself: n_targets_or_classes > 0AVERAGEMINbase_values.empty()BRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQ is already there. in tree Node (truenode).Unable to find node (falsenode).) is different from n_nodes (Number of nodes in nodes_ ( (weights).PThe attributes in LabelEncoder ) must have the same length. and the number of values is However, the number of key is GetVectorAttrsOrDefaultonnxruntime::common::Status onnxruntime::ml::GetVectorAttrsOrDefault(const onnxruntime::OpKernelInfo&, const std::string&, onnx::TensorProto_DataType, std::vector&) [with TH = double; std::string = std::__cxx11::basic_string]onnxruntime::common::Status onnxruntime::ml::GetVectorAttrsOrDefault(const onnxruntime::OpKernelInfo&, const std::string&, onnx::TensorProto_DataType, std::vector&) [with TH = float; std::string = std::__cxx11::basic_string]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map >]const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map]onnxruntime::common::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&) [with InputType = double; ThresholdType = double; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; reference = onnxruntime::ml::detail::TreeNodeElement*&]onnxruntime::common::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&) [with InputType = long int; ThresholdType = float; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]onnxruntime::common::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&) [with InputType = int; ThresholdType = float; OutputType = float; std::string = std::__cxx11::basic_string; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = float; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]predictions.size() == 2void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = double; ThresholdType = double; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 3, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = long int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(onnxruntime::InlinedVector >&, OutputType*, int, int64_t*) const [with InputType = int; ThresholdType = float; OutputType = float; onnxruntime::InlinedVector > = absl::lts_20240116::InlinedVector, 6, std::allocator > >; int64_t = long int]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = double; ThresholdType = double; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = float; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = long int; ThresholdType = float; OutputType = float]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*, const AGG&) const [with AGG = onnxruntime::ml::detail::TreeAggregatorClassifier; InputType = int; ThresholdType = float; OutputType = float]T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = double; std::string = std::__cxx11::basic_string]External data type cannot be STRING.T onnxruntime::ml::GetDefault(const onnxruntime::OpKernelInfo&, const std::string&, const T&) [with T = long int; std::string = std::__cxx11::basic_string]w\ww\ww\ww\w\w\w\ww\w\w\w\ww\w\w\w\w\w\w\www\ww\w\w\ww\w\ww\w\w\ww\w\w\w\w\w\w\w\wvwwvwnwwSw\wxvwnwSw~wwwww:x/xw$xxx\w\w\w\w\w\w\w\w\w\w\w\w\w\w\w\w\ww\w\w\w\w\wwww\w\w\w\w\w\w\w\w\w\w\w\w\w\w\w~wvwnwSw~wwnwSwSwvwnwSw~wwSw~wwww\w\ww|tLssLs9uLssLsLsLsLssLsLsLsLs.uLsLsLsLsLsLsLs#uuLs\tLsLsLssLsLstLsLsLstLsLsLsLsLsLsLsLs|st|ssu::Compute(onnxruntime::OpKernelContext*) const::cur1 == end1cur_out == end_outonnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext*) const::void onnxruntime::BroadcastLooper(TBroadcastHelper&, const ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = BroadcastHelper]MatMul dimension mismatchM_ == 1 && N_ == 1 was falseleft_num_dims and right_num_dims must be >= 1Two inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueonnxruntime::common::Status onnxruntime::MatMulComputeHelper::Compute(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&, bool, bool, bool, bool, bool)left_num_dims > 2 && left_num_dims == right_num_dimsleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsTensor with shape information must be 1 dimensional.void onnxruntime::UntypedExpand(OpKernelContext&, const ProcessBroadcastSpanFuncs&)shape_data_tensor.Shape().GetDims().size() == 1ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::ExpandBroadcastLooper(BroadcastHelper&, const ProcessBroadcastSpanFuncs&)!helper.HaveTwoTensorInputs()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`ApplyRulesOnNodecom.microsoft.QuantizeLinearcom.microsoft.QLinearAddcom.microsoft.QLinearConcatcom.microsoft.QLinearMulcom.microsoft.QLinearSigmoidcom.microsoft.DequantizeLinearcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearReduceMeanstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&; size_type = long unsigned int]Can only add a new input at the end of the current ones.void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&)num_explicit_inputs == static_cast(target_input_idx)bool onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPoint(const onnxruntime::Node&, const GetConstantInitializerFn&, bool&)/onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_util.ccq_or_dq_input_defs.size() >= 2Method IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::IncrementIndexAndComputeOffsetSetup(MultiIndex&, size_t, gsl::span, const gsl::span&, size_t)naxes > 0min should be a scalar.min->Shape().IsScalar()max should be a scalar.max->Shape().IsScalar()void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = double]/onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = unsigned int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = long unsigned int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = long int]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::concurrency::ThreadPool*) const [with T = float]DoubleQDQRemoved_FuseSubGraphQKImplk root is not layer normConvertMaskToInt32q root should be layer normalizationq_matmul and q_add shape not matchedk_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.Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32Failed to convert mask to int32Start CheckNodesInPathVCheckDistilBertReshapeShapePass CheckNodesInPathVFailed 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 expectedreshape initializer value is not expectedStart MatchInputMaskSubgraphFailed to find Softmax nodeFailed to find path for maskPass MatchInputMaskSubgraphOutput edge count not expected for Softmaxmask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedStart MatchUnidirMaskSubgraphValidateUnidirMaskunidir mask is not constantPass MatchUnidirMaskSubgraphFaild 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_sliceunidir 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 floatValidateUnidirMask 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 slice2std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = int; _Alloc = std::allocator; reference = int&]QLinearConvTransposeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axiscom.microsoft.experimentalorg.pytorch.atenA list of ints.A list of floats.A string.An integer.A float.A list of strings.A default tensor.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.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.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.{"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type. ConvertMaskToIntstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator >; reference = nlohmann::basic_json<>&; size_type = long unsigned int]ir_versionopset_importproducer_nameproducer_versiondomainmodel_versiondoc_stringmetadata_propsoptionalstodstollstoftypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat&]libonnxruntime_providers_cuda.solibonnxruntime_providers_cuda_ut.solibonnxruntime_providers_cann.solibonnxruntime_providers_rocm.solibonnxruntime_providers_dnnl.solibonnxruntime_providers_openvino.solibonnxruntime_providers_tensorrt.solibonnxruntime_providers_migraphx.soFailed memory size calculationsparse_tensorstype_protoscomplex64complex128Incomplete string literal.Value expected but not found.Encountered invalid float literal!AddConstantProtoAsInitializerConstant node name: conflicts with graph initializer. Check that the node names have been made unique.onnxruntime::common::Status onnxruntime::Graph::AddConstantProtoAsInitializer(const onnx::NodeProto&, std::optional >)LoadFromStringInvalid OpIdentifier string: /onnxruntime_src/onnxruntime/core/graph/op_identifier.hFailed to parse since_version from op_kernel_type_str_args is null.Duplicate entry for kernel type str: op_id is null.kernel_type_str_args is null.kernel_type_str is null.Duplicate entry for op id: 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.graph_proto_ is not in sync with name_to_initial_tensor_onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensorImpl(onnx::TensorProto, bool)existing_entry != mutable_initializers.pointer_end()onnxruntime::common::Status onnxruntime::utils::GetExtDataFromTensorProto(const onnxruntime::Env&, const char*, const onnx::TensorProto&, void*&, SafeInt&, onnxruntime::OrtCallback&) are out of bounds or can not be read in full.GetExtDataFromTensorProtoGetFileLength for GetFileContentExternal initializer: size to read: given file_length:  8 (0void onnxruntime::PlannerImpl::PartitionIntoStreams(const onnxruntime::logging::Logger&, const onnxruntime::ExecutionProviders&, const onnxruntime::PathString&)std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; reference = onnxruntime::SequentialExecutionPlan::ReleaseAction&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::SequentialExecutionPlan::ReleaseAction; _Alloc = std::allocator; reference = onnxruntime::SequentialExecutionPlan::ReleaseAction&; size_type = long unsigned int]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&; size_type = long unsigned int]CreatePlanComputeReusePlanGenerateDeallocationPlanN11onnxruntime25ISequentialPlannerContextEN11onnxruntime24SequentialPlannerContextESaveTypeInfoOrtFormatSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatWe do not support type [] for nowSaveTensorTypeAndShapeOrtFormatstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = unsigned int; _Alloc = std::allocator; reference = unsigned int&; size_type = long unsigned int]SaveRuntimeOptimizationRecordToOrtFormatstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]OrtValue should contain a Tensor or a Sparse TensorThis API supports Tensors or SparseTensorsshape is invalid Expected: Got invalid dimensions for for the following indices Please fix either the inputs/outputs or the model.Field '' of 'values of data_type '' should be stored in field '' instead of '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 size, max= not in range [min=Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred= Dimension=source and destination buffer size mismatchEliminateIdentityEliminateSliceUnsqueezeEliminationEliminateDropoutExpandEliminationCastEliminationPreShapeNodeEliminationNoopEliminationDivMulFusionFuseReluClipGemmSumFusionGemmTransposeFusionNotWhereFusionConvAddFusionConvMulFusionConvBNFusionPad_FusionClipQuantRewriteReluQuantRewrite_RuleBasedTransformerMatMul_BatchNormalization_FusionGenerateRuleBasedGraphTransformerstd::unique_ptr onnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformer(onnxruntime::TransformerLevel, const onnxruntime::InlinedHashSet >&, const onnxruntime::InlinedHashSet >&)static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = long int; V = std::__cxx11::basic_string; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]RegisterOpSchemaBuildConnectionsInvalid model. Node input ' Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name=' is not a graph input, initializer, or output of a previous node.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=) in op definition.) than declared (UnpackInitializerDataUnsupported type: '.4.t-*T*L,+',)(\(,)'')-$+-+SaveInitializerOrtFormatSerialization of fused function body is not currently supported, Removable attributes were removed before the node is saved.does not have the graph for key Graph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Node [SaveAttributeOrtFormatNK JLHGDGF,C@=Must have a valid data typeHasDataType(dense_proto) data_type: SaveValueInfoOrtFormat is missing type info.onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&)DenseTensorToSparseTensorProtoSaveSparseInitializerOrtFormatSaveValueInfoOrtFormat: value_info_proto for std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::fbs::EdgeEnd; _Alloc = std::allocator; reference = onnxruntime::fbs::EdgeEnd&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = flatbuffers::Offset; _Alloc = std::allocator >; reference = flatbuffers::Offset&]GetExternalDataInfochecksummodel format error!TensorProto: , external_data.length: Tensor does not have external data to read from.model format error! Need a key for the external data infomodel format error! Need a value for the external data infomodel format error! Missing 'location'External data type cannot be UNDEFINED or STRING. external data size mismatch. Computed size: type must be object, but is RRRRRRRRuRRAR5RRRRRRRRRg R[ RO RC R RRRR R RCannot find execution provider Failed to load TuningResults (index=Correctly set TuningResults for SetTuningResultsInvalid TuningResults (index= does not support TunableOp.). Reason: , enable TunableOp for usingValidateBlockSparseShapesExpecting index blocks: Expecting to have at lest 3-D shape. Got:Expecting indices to have 2-D shape . Got: Indices shape must have dim[0] == 2 to be equal to values blocks: Expecting fully sparse tensors to have value shape {0}Expecting fully sparse tensors to have indices shape {0}N11onnxruntime15ReluQuantFusionEN11onnxruntime15ClipQuantFusionEN11onnxruntime14MatmulBNFusionEN11onnxruntime9PadFusionEN11onnxruntime12ConvBNFusionEN11onnxruntime13ConvMulFusionEN11onnxruntime13ConvAddFusionEN11onnxruntime14NotWhereFusionEN11onnxruntime19GemmTransposeFusionEN11onnxruntime13GemmSumFusionEN11onnxruntime12FuseReluClipEN11onnxruntime12DivMulFusionEN11onnxruntime15NoopEliminationEN11onnxruntime23PreShapeNodeEliminationEN11onnxruntime15CastEliminationEN11onnxruntime17ExpandEliminationEN11onnxruntime16EliminateDropoutEN11onnxruntime20UnsqueezeEliminationEN11onnxruntime14EliminateSliceEN11onnxruntime17EliminateIdentityEUNDEFINEDINTFLOATSINTSSTRINGSGRAPHSSPARSE_TENSORSPARSE_TENSORS@@e/framework/endi/endian_utils.ccCopyLittleEndiane/session/infererence_session.ccSetTuningResults/build/Release/_deps/protobuf-src/src/google/protobuf/stubs/stringpiece.ccstring length exceeds max size details: at line , column parse_errorparse errortype_errortype must be number, but is vRjR^RRRFR<R<R<R:RRinvalid_iteratorW,Y,X::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator; reference = onnxruntime::utils::data_types_internal::TypeNode&] Version mismatch. node_version: kernel start version: kernel_end_version: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeAndMoveInfo; _Alloc = std::allocator; reference = onnxruntime::NodeAndMoveInfo&]dtype_ != nullptrvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t, gsl::span)Tensor is expected to contain one of the primitive data types. Got: ISink must be provided.Default logger already set. onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, InstanceType, const std::string*, int)/onnxruntime_src/onnxruntime/core/common/logging/logging.ccdefault_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&)AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Available memory of ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at is smaller than requested bytes of Incorrect arena extend strategy.onnxruntime::BFCArena::Extend(size_t)::Failed to allocate memory for requested buffer of size onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t, int64_t)0 == memory_size % kMinAllocationSizeFailed to find a free memory block despite calling Extend. rounded_bytes=void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn) must be equal to or twice the values size: std::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constIndex size: values_count == index_sizestd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::profiling::EventRecord; _Alloc = std::allocator; reference = onnxruntime::profiling::EventRecord&]Maximum number of events reached, could not record profile event.EndTimeAndRecordEventSetGraphAndCreateKernels must be called prior to GetExecutionInfo.const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() constonnxruntime::ExecutionFrame::ExecutionFrame(gsl::span, gsl::span, gsl::span, gsl::span, const std::unordered_map >&, const onnxruntime::DeviceStreamCollection*, const onnxruntime::SessionState&)buffers_.find(location) == buffers_.end()Allocation of memory pattern buffer for node_index_info_.has_value()ExecutionFrame returned nullptr failed. Error:ExecuteThePlanSequentialExecutor::Executeai.onnx.preview.trainingbumped the operator version but forgot to update the version range in DomainToVersionRange , but its domain is not known by the checker., but its version is not in the inclusive range [] (usually, this means you in onnx/defs/schema.h).Fatal error in cpuinfo: string length exexceeds max sizeSequentialExecutxecutor::Executeai.onnx.preview.std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; reference = re2::WalkState&]std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; reference = re2::WalkState&]std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState; _Alloc = std::allocator >; reference = re2::WalkState&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Splice; _Alloc = std::allocator; reference = re2::Splice&]sequence_typemap_typeoptional_typeopaque_typesparse_tensor_typeNOT_SET::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::RuntimeOptimizationRecord; _Alloc = std::allocator; reference = onnxruntime::RuntimeOptimizationRecord&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::graph_utils::GraphEdge; _Alloc = std::allocator; reference = onnxruntime::graph_utils::GraphEdge&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]mmmmmmm0nmnstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::IDataTransfer::SrcDstPair; _Alloc = std::allocator; reference = onnxruntime::IDataTransfer::SrcDstPair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator; reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper >; _Alloc = std::allocator > >; reference = std::reference_wrapper >&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = onnxruntime::Node*; _Alloc = std::allocator; reference = onnxruntime::Node*&; size_type = long unsigned int] [seqno= rows[ row[std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr; _Alloc = std::allocator >; reference = std::unique_ptr&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::pair; _Alloc = std::allocator >; reference = std::pair&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::StringPiece; _Alloc = std::allocator; reference = re2::StringPiece&]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = true]/onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_kernel_base.hinfo.GetAttr("keepdims", &keepdims).IsOK()TypeProto must have shape for this to runconst onnx::TensorShapeProto& onnxruntime::utils::GetShape(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.honnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, std::shared_ptr, ptrdiff_t, gsl::span)type_proto is not of type sequence!static std::unique_ptr OrtSequenceTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_sequence_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kSequenceTypetype_proto is not of type map!static std::unique_ptr OrtMapTypeInfo::FromTypeProto(const onnx::TypeProto&)/onnxruntime_src/onnxruntime/core/framework/onnxruntime_map_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kMapTypebool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto&, const onnx::TypeProto&):R:R:Rt:R4Expected AllocateFinalOutput to have been called to before we increment the iteratoronnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++()onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo&, std::optional) [with bool allow_multi_axes = false]Failed to analyze start state.std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::FunctionBodyHelper::NodeDef; _Alloc = std::allocator; reference = onnx::FunctionBodyHelper::NodeDef&]opaqueSubgraph has nodes running on device: while parent graph node running on device: void onnxruntime::utils::UpdateWithParentStream(onnxruntime::DeviceStreamCollection&, onnxruntime::Stream*)onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, gsl::span, std::vector&, gsl::span, gsl::span)feed_streams.size() == num_feedsstd::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](size_type) const [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator; const_reference = const onnxruntime::MLValueCopyInfo&; size_type = long unsigned int], this is not supported yet.ExecuteGraphImplcopy_info.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicessession.node_partition_config_fileconstexpr _Tp& std::_Optional_base_impl<_Tp, _Dp>::_M_get() [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::_Optional_base]typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ITensorAllocator; _Dp = std::default_delete; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ITensorAllocator&]Missing session state for subgraph. Node:'onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::__cxx11::basic_string&, const onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, onnxruntime::InlinedHashMap, long unsigned int>&, const onnxruntime::InlinedHashMap, OrtDevice>&, bool)entry != node_to_subgraph_ss.second.cend()OuterScopeNodeArgLocationAccumulatorFinalizeSessionStateImplsession.disable_prepackingCreateKernels Attribute:p_op_kernelstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer::Iterator; _Alloc = std::allocator::Iterator>; reference = onnxruntime::OrtValueTensorSlicer::Iterator&]@@CUDAExecutionProROCMExecutionPro:constexpr const std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) const [with _Tp = long int; long unsigned int _Nm = 6; const_reference = const long int&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = Eigen::internal::TensorBlockIO::BlockIteratorState; long unsigned int _Nm = 5; reference = Eigen::internal::TensorBlockIO::BlockIteratorState&; size_type = long unsigned int]constexpr std::array<_Tp, _Nm>::value_type& std::array<_Tp, _Nm>::operator[](size_type) [with _Tp = Eigen::internal::TensorBlockIO::BlockIteratorState; long unsigned int _Nm = 5; reference = Eigen::internal::TensorBlockIO::BlockIteratorState&; size_type = long unsigned int]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 std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement*; _Alloc = std::allocator*>; reference = onnxruntime::ml::detail::TreeNodeElement*&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::ml::detail::TreeNodeElement; _Alloc = std::allocator >; reference = onnxruntime::ml::detail::TreeNodeElement&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector; _Alloc = std::allocator >; reference = std::vector&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::{anonymous}::ScaleMergeInfo; _Alloc = std::allocator; reference = onnxruntime::{anonymous}::ScaleMergeInfo&]std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = const std::vector*; _Alloc = std::allocator*>; reference = const std::vector*&]vector::_M_insert_auxstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::FreeDimensionOverride; _Alloc = std::allocator; reference = onnxruntime::FreeDimensionOverride&] execution provider is not supported in this build. CPU execution provider: string 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 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: bool 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: BFloat16 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 18 when reduction is 'min'.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 16 when reduction is 'add'.Resize operatorUpsample operatorScalesValidationScale 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 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the -INF%.8gNot able to find appropriate IDataTransfer to copy sparse datastd::unique_ptr {anonymous}::GetDataTransfer(const OrtDevice&, const OrtDevice&)static bool onnxruntime::utils::ContainerChecker::IsContainerOfType >::check(const onnxruntime::utils::ContainerChecker::Cont&, size_t) [with K = std::__cxx11::basic_string; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector; size_t = long unsigned int]??AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;rB¥B@K;?r1:n/ <*=(*>>??    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FIF A(Lp (D ABBA kw8R p< kY| FIF A(Lp (D ABBA `ki8R p<kP[| FIF A(Lp (D ABBA k[8R} p<k\|e FIF A(Lp (D ABBA  lM8R: p<@l^|" FIF A(Lp (D ABBA l?8R p<l0`|߲ FIF A(Lp (D ABBA l18R p<ma| FIF A(Lp (D ABBA @m#8Rq p<`mpc|Y FIF A(Lp (D ABBA m8R. p<me| FIF A(Lp (D ABBA n8R p< nf|ӱ FIF A(Lp (D ABBA `n8R p<nPh| FIF A(Lp (D ABBA n8Re p<ni|M FIF A(Lp (D ABBA  o݈8R" p<@ok| FIF A(Lp (D ABBA oψ8R߰ p<o0m|ǰ FIF A(Lp (D ABBA o8R p<pn| FIF A(Lp (D ABBA @p8RY p<`ppp|A FIF A(Lp (D ABBA p8R p<pr| FIF A(Lp (D ABBA q8Rӯ p< qs| FIF A(Lp (D ABBA `q8R p<qPu|x FIF A(Lp (D ABBA q{8RM p<qv|5 FIF A(Lp (D ABBA  rm8R p<@rx| FIF A(Lp (D ABBA r_8RǮ p<r0z| FIF A(Lp (D ABBA rQ8R p<s{|l FIF A(Lp (D ABBA @sC8RA p<`sp}|) FIF A(Lp (D ABBA s58R p<s| FIF A(Lp (D ABBA t'8R p< t| FIF A(Lp (D ABBA `t8Rx p<tP|` FIF A(Lp (D ABBA t 8R5 p<t| FIF A(Lp (D ABBA  u8R p<@u|ڬ FIF A(Lp (D ABBA u8R p<u0| FIF A(Lp (D ABBA u8Rl p<vЈ|T FIF A(Lp (D ABBA @vӇ8R) p<`vp| FIF A(Lp (D ABBA vŇ8R p<v|Ϋ FIF A(Lp (D ABBA w8R p< w| FIF A(Lp (D ABBA `w8R` p<wP|H FIF A(Lp (D ABBA w8R p<w| FIF A(Lp (D ABBA  x8Rڪ p<@x|ª FIF A(Lp (D ABBA x8R p<x0| FIF A(Lp (D ABBA xq8RT p<yЕ|< FIF A(Lp (D ABBA @yc8R p<`yp| FIF A(Lp (D ABBA yU8RΩ p<y| FIF A(Lp (D ABBA zG8R p< z|s FIF A(Lp (D ABBA `z98RH p<zP|0 FIF A(Lp (D ABBA z+8R p<z| FIF A(Lp (D ABBA  {8R¨ p<@{| FIF A(Lp (D ABBA {8R p<{0|g FIF A(Lp (D ABBA {8R< p<|Т|$ FIF A(Lp (D ABBA @|8R p<`|p| FIF A(Lp (D ABBA |8R p<|| FIF A(Lp (D ABBA }׆8Rs p< }|[ FIF A(Lp (D ABBA `}Ɇ8R0 p<}P| FIF A(Lp (D ABBA }8R pP|v|զ FBG E(A0A8GW 8A0A(B BBBB v8Zަ Dv(|U FIE I(A0G@ 0D(A BBBA @D8|" FIE I(A0G@ 0D(A BBBA }8, @Dx|ё FIE I(A0G@ 0D(A BBBA }8, @D | FIE I(A0G@ 0D(A BBBA XH}8,K @D||/ FIE I(A0G@ 0D(A BBBA Ĕ}8, @D4|ސ FIE I(A0G@ 0D(A BBBA 0|8, @DT| FIE I(A0G@ 0D(A BBBA |8,X @D\|< FIE I(A0G@ 0D(A BBBA H|8, @D,| FIE I(A0G@ 0D(A BBBA t|8, @D| FIE I(A0G@ 0D(A BBBA {8,e @D|I FIE I(A0G@ 0D(A BBBA L{8, @Dp| FIE I(A0G@ 0D(A BBBA H{8,Î @Dܗ@| FIE I(A0G@ 0D(A BBBA ${8,r @DH|V FIE I(A0G@ 0D(A BBBA z8,! @Dh| FIE I(A0G@ 0D(A BBBA z8,Ѝ @D | FIE I(A0G@ 0D(A BBBA hHz8, @D|c FIE I(A0G@ 0D(A BBBA ԙz8,. @D$| FIE I(A0G@ 0D(A BBBA @y8,݌ @Dd| FIE I(A0G@ 0D(A BBBA y8, @DКL|p FIE I(A0G@ 0D(A BBBA Hy8,; @D<| FIE I(A0G@ 0D(A BBBA y8, @Dt|΋ FIE I(A0G@ 0D(A BBBA x8, @D|} FIE I(A0G@ 0D(A BBBA \x8,H @D|, FIE I(A0G@ 0D(A BBBA ȜHx8, @D0|ۊ FIE I(A0G@ 0D(A BBBA 4x8, @DX| FIE I(A0G@ 0D(A BBBA w8,U @DĝX|9 FIE I(A0G@ 0D(A BBBA w8, @D0| FIE I(A0G@ 0D(A BBBA xHw8, @D| FIE I(A0G@ 0D(A BBBA w8,b @D|F FIE I(A0G@ 0D(A BBBA Pv8, @Dt| FIE I(A0G@ 0D(A BBBA v8, @D<| FIE I(A0G@ 0D(A BBBA (Hv8,o @DL|S FIE I(A0G@ 0D(A BBBA v8, @<d| FIF A(Lp (D ABBA \u8Rׇ p<|| FIF A(Lp (D ABBA u8R p<ܨ|| FIF A(Lp (D ABBA u8RQ p<<D|9 FIF A(Lp (D ABBA |u8R p<| FIF A(Lp (D ABBA ܩu8Rˆ p<| FIF A(Lp (D ABBA <u8R p<\$|p FIF A(Lp (D ABBA |u8RE p<|- FIF A(Lp (D ABBA nu8R p<d| FIF A(Lp (D ABBA \`u8R p<|} FIF A(Lp (D ABBA Ru8R| p<ܫ}d FIF A(Lp (D ABBA Du8R9 p<<D}! 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<Zs8R| p<\$=}{ FIF A(Lp (D ABBA Ls8R{ p<>}{ FIF A(Lp (D ABBA >s8R{ p<d@}r{ FIF A(Lp (D ABBA \0s8RG{ p<|B}/{ FIF A(Lp (D ABBA "s8R{ p<ܺC}z FIF A(Lp (D ABBA s8Rz p<<DE}z FIF A(Lp (D ABBA |s8R~z p<F}fz FIF A(Lp (D ABBA ܻr8R;z p<H}#z FIF A(Lp (D ABBA <r8Ry p<\$J}y FIF A(Lp (D ABBA r8Ry p<K}y FIF A(Lp (D ABBA r8Rry p<dM}Zy FIF A(Lp (D ABBA \r8R/y p<|O}y FIF A(Lp (D ABBA r8Rx p<ܽP}x FIF A(Lp (D ABBA r8Rx p<<DR}x FIF A(Lp (D ABBA |r8Rfx p<S}Nx FIF A(Lp (D ABBA ܾr8R#x p<U} x FIF A(Lp (D ABBA <zr8Rw p<\$W}w FIF A(Lp (D ABBA lr8Rw p<X}w FIF A(Lp (D ABBA ^r8RZw p<dZ}Bw FIF A(Lp (D ABBA \Pr8Rw p<|\}v FIF A(Lp (D ABBA Br8Rv p<]}v FIF A(Lp (D ABBA 4r8Rv p<<D_}yv FIF A(Lp (D ABBA |&r8RNv p<`}6v FIF A(Lp (D ABBA r8R v p<b}u FIF A(Lp (D ABBA < r8Ru p<\$d}u FIF A(Lp (D ABBA q8Ru p<e}mu FIF A(Lp (D ABBA q8RBu p<dg}*u FIF A(Lp (D ABBA \q8Rt p<|i}t FIF A(Lp (D ABBA q8Rt p<j}t FIF A(Lp (D ABBA q8Ryt p<<Dl}at FIF A(Lp (D ABBA |q8R6t p<m}t FIF A(Lp (D ABBA q8Rs p<o}s FIF A(Lp (D ABBA <q8Rs p<\$q}s FIF A(Lp (D 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FIG A(A0O. 0D(A BBBH 48 PX2 FIG B(A0A8O 8D0A(B BBBJ =8 P 7p FIG B(A0A8O 8D0A(B BBBJ $8g PH;K FIG B(A0A8O 8D0A(B BBBF 8B HP@& FIG A(A0L 0D(A BBBD  9 H0 C FIG A(A0L 0D(A BBBH | C9ؓ H G FIG A(A0L 0D(A BBBH {9 @ pJK FIF A(Ls (D ABBB T 9xj @t \MKR FIF A(Ls (D ABBB  9x5 @ HPK FIF A(Lo (D ABBF  9x << 4S FIF A(Lp (D ABBA | 9R p< T FIF A(Lp (D ABBA  9Rz p< Vb FIF A(Lp (D ABBA < 9R7 p\ 9  AKLXX3  FBB G(A0D8M 8D0A(B BBBD d9nʑ P$a֑ FIB E(I0A8J 8D0A(B BBBC $9( APb FIB E(I0A8J 8D0A(B BBBC $XV9(P AP,d@ FIB E(I0A8J 8D0A(B BBBC $9( APe FIB E(I0A8J 8D0A(B BBBF P9* Pt8g FIB E(I0A8J 8D0A(B BBBF `9*t PPhk BBB B(A0A8JD 8A0A(B BBBF <9+ R 8A0A(B BBBE ,,knFAA O ABF Plk BBB B(A0A8J. 8A0A(B BBBD <h89 D 8A0A(B BBBE ,mnFAA O ABF Pmw BBB B(A0A8J0 8A0A(B BBBB <,Z93 D 8A0A(B BBBE ,$pnFAA O ABF Pdp BBB B(A0A8J1 8A0A(B BBBA <| 9 D 8A0A(B BBBE ,TrnFAA O ABF P`r BEB B(A0A8J/ 8A0A(B BBBH < 9; D 8A0A(B BBBE ,unFAA O ABF P$\u" BBF B(A0A8Ja 8A0A(B BBBE x 9l P' BBB B(A0A8JE 8A0A(B BBBE <9 R 8A0A(B BBBE ,TnFAA O ABF P` BBB B(A0A8J. 8A0A(B BBBD <9k D 8A0A(B BBBE ,nFAA O ABF P$<3 BBB B(A0A8J0 8A0A(B BBBB <x9 D 8A0A(B BBBE ,xnFAA O ABF P BBB B(A0A8J2 8A0A(B BBBH <<9s D 8A0A(B BBBE ,nFAA O ABF P4; BEB B(A0A8J0 8A0A(B BBBG <@9 D 8A0A(B BBBE ,dpnFAA O ABF Pp" BBF B(A0A8Ja 8A0A(B BBBE b9l֋ Lh FBH G(D0A8D 8D0A(B BBBF Z9%‹ P\ FBB B(A0D8J  8A0A(B BBBF 9 <pd FED I(D0r (D ABBA T9p 0<,dX FED I(D0r (D ABBA  9$ 0P< BBB B(A0D8J  8A0A(B BBBB 9 <d FED I(D0r (D ABBA 9֊ 0LlĨ FBH B(F0A8J 8D0A(B BBBH 9 P| FBE B(D0A8L 8A0A(B BBBA 4%9D `X FFB B(A0D8Jz 8D0A(B BBBJ BVF $9p 8ຆ_ EC DEHM A .0J. %9ʊ dφ)x,φ3LbPφ)lφ3Lbφ)φ3Lbφ)φ3Lb$І)8,І3LbTPІ)hlІ3LbІ)І3LbІ)І3Lbц),ц3LbPц)(lц3Lb Dц_Ll H A G hцgLy K  ҆)<҆3Lb`҆)|҆3Lb҆)҆3Lb ҆)( ҆3LbD  ӆ)X <ӆ3Lb@t `ӆEAG i CAF N CAF J CAJ @ ӆEAG i CAF N CAF J CAJ @ hԆEAG i CAF N CAF J CAJ @@!ԆEAG i CAF N CAF J CAJ <pՆ FEI A(G0 (D ABBE <$9݆ 0<\ֆņ FEI A(G0 (D ABBE $9 0<ֆz FEI A(G0 (D ABBE T$9G 0< ׆/ FEI A(G0 (D ABBE \ $9 0<|׆ FEI A(G0 (D ABBE #9 0<@؆ FEI A(G0 (D ABBE s#9f 0<<؆N FEI A(G0 (D ABBE |(#9 0<`ن FEI A(G0 (D ABBE "9Є 0<ن FEI A(G0 (D ABBE <"9 0<\چm FEI A(G0 (D ABBE G"9: 0<ۆ" FEI A(G0 (D ABBE !9 0<ۆ׃ FEI A(G0 (D ABBE \!9 0<|0܆ FEI A(G0 (D ABBE f!9Y 0<܆A FEI A(G0 (D ABBE !9 0<<P݆ FEI A(G0 (D ABBE | 9 0<݆ FEI A(G0 (D ABBE  9x 0<pކ` FEI A(G0 (D ABBE <: 9- 0<\߆ FEI A(G0 (D ABBE 9 0<߆ʁ FEI A(G0 (D ABBE 9 0<   FEI A(G0 (D ABBE \ Y9L 0<| 4 FEI A(G0 (D ABBE  9 0< @ FEI A(G0 (D ABBE !9 0<[q FIF A(Lp (D ABBA L>!9RXq p<l>4]@q FIF A(Lp (D ABBA >!9Rq p<>^p FIF A(Lp (D ABBA  ?!9Rp p<,?t`p FIF A(Lp (D ABBA l? 9Rp p<?bwp FIF A(Lp (D ABBA ? 9RLp p<?c4p FIF A(Lp (D ABBA ,@ 9R p p<L@Teo FIF A(Lp (D ABBA @ 9Ro p<@fo FIF A(Lp (D ABBA @ 9Ro p< Ahko FIF A(Lp (D ABBA LA 9R@o p<lA4j(o FIF A(Lp (D ABBA A 9Rn p<Ak0n FIF A(Lp (D ABBA  B 9Rn p<,Bm,n FIF A(Lp (D ABBA lB 9Rwn p<Bto0_n FIF A(Lp (D ABBA Bw 9R4n p<BDq,n FIF A(Lp (D ABBA ,Ci 9Rm p<LCs0m FIF A(Lp (D ABBA C[ 9Rm pPCt_m FBB B(A0A8J 8D0A(B BBBE D: 9]tm P$Dym FBB B(A0A8J 8D0A(B BBBE xD "9]im HE~cFBB B(G0D8I 8D0A(B BBBE  F$9H=藇m FDE A(A0M 0D(A BBBI =#9/l P>l FDB B(G0A8J 8D0A(B BBBE h>`$9]vl L>М|}l FDE B(G0A8G 8D0A(B BBBD >J&9W5l D?ܞ7l FDB G(A0J 0D(A BBBG H?6(9Wk Ll?|k FDE B(G0A8G 8D0A(B BBBD ?*9Wk P?%k FDB B(A0A8G# 8A0A(B BBBA 4@+9k PX@Tk FDB B(A0A8G# 8A0A(B BBBA @,.9uk P@%k FDB B(A0A8G# 8A0A(B BBBA $A^09jk PHAk FDB B(A0A8G  8A0A(B BBBA A29^k PALk FDB B(A0A8G  8A0A(B BBBA B49Rk P8B万zk FBB B(A0A8J 8A0A(B BBBF $B69k g PHH"g FBI E(I0A8GG 8D0A(B BBBE H79Df PH,f FBI E(I0A8GG 8D0A(B BBBE I69Df P8Ihf FBI E(I0A8GG 8D0A(B BBBE I69D'f PI f FBI E(I0A8GG 8D0A(B BBBE Jk69De P(Jde FBI E(I0A8GG 8D0A(B BBBE |J769Dme PJQe FBI E(I0A8GG 8D0A(B BBBE J69De PK4d FBI E(I0A8GG 8D0A(B BBBE lK59Dd PKd FBI E(I0A8GG 8D0A(B BBBE K59DVd  TxAj E o A LT`BFB A(D@~ (A BBBD  (A BBBF U)U),U)0@U 59MECD s AAH AAAHtUFBE B(A0A8GP 8A0A(B BBBF U49PhXMT b BBB B(A0D8G 8A0A(B BBBH K 8G0A(B BBBE MS49b hMlb BBB B(A0D8G 8A0A(B BBBH k 8G0A(B BBBE TN39 b PxN$Qa FFB B(D0D8Gw 8A0A(B BBBH Np39a HxW FBB B(A0A8G`Y 8A0A(E BBBE HWpOFFE E(D0H8GP 8A0A(B BBBA <Vt,` FIF A(Lp (D ABBA ,W29R` p<LWD0` FIF A(Lp (D ABBA Wv29R` p<W,i` FIF A(Lp (D ABBA Wh29R>` p< X0&` FIF A(Lp (D ABBA LXZ29R_ p<lX,_ FIF A(Lp (D ABBA XL29R_ p<X0_ FIF A(Lp (D ABBA  Y>29Ru_ p<,YT!,]_ FIF A(Lp (D ABBA lY029R2_ p<Y$#0_ FIF A(Lp (D ABBA Y"29R^ p<Y$,^ FIF A(Lp (D ABBA ,Z29R^ p<LZ&0^ FIF A(Lp (D ABBA Z29Ri^ p<Z(,Q^ FIF A(Lp (D ABBA Z19R&^ p< [d*0^ FIF A(Lp (D ABBA L[19R] p<l[4,,] FIF A(Lp (D ABBA [19R] p<[.0] FIF A(Lp (D ABBA  \19R]] p<,\/,E] FIF A(Lp (D ABBA l\19R] p<\10] FIF A(Lp (D ABBA \19R\ p<\t3,\ FIF A(Lp (D ABBA ,]19R\ p<L]D5|\ FIF A(Lp (D ABBA ]19RQ\ p@]69\ FIF A(L! (D ABBD ]19(\ @^ :\ FIF A(Os (D ABBA T^19[ @t^<[ FIF A(O (D ABBE ^19[ @^?[ FIF A(O (D ABBE _=29[ @<_$Cw[ FIF A(O (D ABBE _|29`[ 839Z 8|Z`rZ EC BJGD G \.m. Zb39zZ 8ZxZ EC BHGM H T.l. [|39Z 8<[@ Z EC BHGM H R.l. x[39Z 8[^Z EC BGL H M.k. [39sZ 8[^Z EC BGL H R.k. 8\39sZ 8c6{Z AC BIH K . l. c39qZ @\rZ FIE I(A0 (A BBBC ][69,AZ 0@$]h%Z FIE I(A0 (A BBBC h]69,Y 0@]Y FIE I(A0 (A BBBC ]59,Y 0@]xY FIE I(A0 (A BBBC 8^59,ZY 0@\^>Y FIE I(A0 (A BBBC ^k59, Y 0@^X FIE I(A0 (A BBBC _/59,X 08,_X EF DEED .U E C. h_49X 8f`4@fX EC DH H  . Z. ,g49 X th@ h<-hXhd7hYhXh(i4i@(iLT9UZP Pnk_P FBB B(D0D8GW 8D0A(B BBBA \nU9I,P @u cP FIF A(L (D ABBF (vT9|O @Hv{O FIF A(L (D ABBD vU9|O @v4{O FIF A(L (D ABBD v-U9|O @wP{uO FIF A(L (D ABBD TwEU9|XO @twl@O FIF A(L (D ABBA w]U9mO <wO FIF A(Lp (D ABBA xjU9RN p<8xN FIF A(Lp (D ABBA xx\U9RN p<xHN FIF A(Lp (D ABBA xNU9RTN p<x謉 FIF A(L (D ABBA e9k> @ؒH3,> FIF A(L (D ABBA e9k> @<5(w> FIF A(L (D ABBA e9kN> @6K6> FIF A(Ls (D ABBB e9x> @9K> FIF A(Ls (D ABBB He9x= @hm9v2 PtZ2 FBB G(D0G8G5 8D0A(B BBBK hm9=2 L!BEB B(A0A8Dm 8D0A(B BBBE m9 <$)L,X$DBKB E(A0A8F 8D0A(B BBBG LX)$N1 FBI J(D0A8J 8D0A(B BBBA Dl9n1 Lh.$1 FBI J(D0A8J 8D0A(B BBBA l9n0 Lܢ2$0 FBI J(D0A8J 8D0A(B BBBA ,l9n0 Hث7FBB B(A0A8G 8A0A(B BBBH @`<0 FIF A(LV (D ABBG Dl9k/ @d>P/ FIF A(O (D ABBA l9/ @ȫAP/ FIF A(O (D ABBA  l9/ @,DLl/ FIF A(O (D ABBA pl9J/ @G,2/ FIF A(L (D ABBA Ԭm9k / P\I. FIG B(A0A8O] 8D0A(B BBBE Hm9. PlL. FIG B(A0A8O] 8D0A(B BBBE Am9. PLP. FIG B(A0A8OY 8D0A(B BBBA 8tm9z. @\Ss^. FIF A(L (D ABBA m9;. @Uo#. FIF A(L (D ABBA m9. @$Ws- FIF A(L (D ABBA hm9- @Zs- FIF A(L (D ABBA ̯ n9- @$\or- FIF A(L (D ABBA 0Cn9O- @P0^,7- FIF A(L (D ABBA fn9k- @_,, FIF A(L (D ABBA mn9k, @a,, FIF A(L (D ABBA \tn9k, @|c(t, FIF A(L (D ABBA {n9kK, @`ek3, FIF A(L (D ABBK $n9z, @Dlhk+ FIF A(L (D ABBK n9z+ @xkc+ FIF A(L (D ABBG n9z+ @ nP+ FIF A(O (D ABBA Pn9r+ @ppqLZ+ FIF A(O (D ABBA n98+ <p\t + FDD G(G (D ABBA o9J* LЬt* FFB B(A0A8GO 8A0A(B BBBF o9E* @Dxv* FIE I(A0 (A BBBA q9,* 0@w* FIE I(A0 (A BBBJ q9,Y* 0@w=* FIE I(A0 (A BBBJ XMq9, * 0@|0x) FIE I(A0 (A BBBJ q9,) 0PHx) BBB B(A0D8J 8A0A(B BBBG p9X) PS) BBB B(A0D8J 8A0A(B BBBG >r9) P8X) BBB B(A0D8J 8A0A(B BBBG s9( P( BBB B(A0D8J 8A0A(B BBBG .u9h( P(虋c( BBB B(A0D8J 8A0A(B BBBG |v9( P<0( FIB E(I0A8J 8D0A(B BBBK $x9(' APģ' FIB E(I0A8J 8D0A(B BBBK $ x9(' APXd}' FIB B(D0A8J 8A0A(B BBBD vy9gN' PPdU' FIB B(D0A8J 8A0A(B BBBD de{9g&' PHT-' FBB B(A0A8J 8A0A(B BBBC ܺT}9_& D0' BFB A(K0J 0A(A BBBD HH9& A@d& FIE I(A0 (A BBBJ L߀9,& 0@p& FIE I(A0 (A BBBJ 9,p& 0@شT& FIE I(A0 (A BBBJ g9,#& 0@@,& FIE I(A0 (A BBBJ +9,% 08 ij % EE BT E O.k. 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