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AO@ Aq! @  j  Atj*"3CO] 3C`q@ 3  A :  Ar"j  Atj*"3CO] 3C`q@ 3  A :  Ar"j  Atj*"3CO] 3C`q@ 3  A :  Ar"j  Atj*"3CO] 3C`q@ 3  A : Aj" G Aq"E @  j  Atj*"3CO] 3C`q@ 3  A : Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! "AJ@A!@  Atj*"3"4"AO@AA AK  4C? AM   A vAqjAjA v ! Atj  3AvAqr; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! "AJ@A!@  Atj*"3! Atj 3 3\A@A AH  AvAqjAjAv ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! " AJ@A!@  Atj*  A lj Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! "AJ@A!@  Atj*"3" Av"Aq!@@ 3C[ AqAF  Av"Aq" AI@ !  Aq! AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AtA@k Aq Avrr"A?q  AqAF" Av AqAGq AqAGqj!  Ar!  j : Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! "AJ@A!@  Atj*"3"Av! @ 3C[@ Ar!  A! AqAF Aq! Aq! Av"Aq" @ AI  AM@ AGA Akt r A kvr  AGr !AA k" t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AtAj Aq Avrr"AqAGq j!  Ar!  A !  j : Aj" G  Aj" 6H @@@@ Ak @ ("E@B!  B!A! @ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! "AL A! @ Atj*"3" Av"Aq!@@ 3C[ Aq"AF  AI@ !  Aq! AM@ Av! AG AqA Aktr A kvr Aq   !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AvA@kAq Aq Avrr! AqE  AqE  AqAM@ Aj!  Ar!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  V! ! "AL A! @ Atj*"3" Av!@ 3C[@ Ar!  A! Aq"AF Aq! Aq! @ AI  AM@ Av! AGA Akt r A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6H Aj  Aj"6 (! @@@ ("A F@A! A6(  6$ A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  A! ! !@ "AL A! A! AO@ Aq! @  j  Atj+Db:  Ar"j  Atj+Db:  Ar"j  Atj+Db:  Ar"j  Atj+Db: Aj" G Aq"E @  j  Atj+Db: Aj! Aj" G A6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj+"5DAc@ 5  Ax 6  Ar"Atj  Atj+"5DAc@ 5  Ax 6  Ar"Atj  Atj+"5DAc@ 5  Ax 6  Ar"Atj  Atj+"5DAc@ 5  Ax 6 Aj" G Aq"E @  Atj  Atj+"5DAc@ 5  Ax 6 Aj! Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j~  j+"5DCc@ 5  B 7  Ar"j~  j+"5DCc@ 5  B 7  Ar"j~  j+"5DCc@ 5  B 7  Ar"j~  j+"5DCc@ 5  B 7 Aj" G Aq"E @  At" j~  j+"5DCc@ 5  B 7 Aj! Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq! @  Atj  Atj+8  Ar"Atj  Atj+8  Ar"Atj  Atj+8  Ar"Atj  Atj+8 Aj" G Aq"E @  Atj  Atj+8 Aj! Aj" G  Aj" 6( A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j~  j+"5DCc 5Dfq@ 5  B 7  Ar"j~  j+"5DCc 5Dfq@ 5  B 7  Ar"j~  j+"5DCc 5Dfq@ 5  B 7  Ar"j~  j+"5DCc 5Dfq@ 5  B 7 Aj" G Aq"E @  At" j~  j+"5DCc 5Dfq@ 5  B 7 Aj! Aj" G  Aj" 6( A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj+"5DAc 5Dfq@ 5  A 6  Ar"Atj  Atj+"5DAc 5Dfq@ 5  A 6  Ar"Atj  Atj+"5DAc 5Dfq@ 5  A 6  Ar"Atj  Atj+"5DAc 5Dfq@ 5  A 6 Aj" G Aq"E @  Atj  Atj+"5DAc 5Dfq@ 5  A 6 Aj! Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj+"5DAc@ 5  Ax ;  Ar"Atj  Atj+"5DAc@ 5  Ax ;  Ar"Atj  Atj+"5DAc@ 5  Ax ;  Ar"Atj  Atj+"5DAc@ 5  Ax ; Aj" G Aq"E @  Atj  Atj+"5DAc@ 5  Ax ; Aj! Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj+"5DAc 5Dfq@ 5  A ;  Ar"Atj  Atj+"5DAc 5Dfq@ 5  A ;  Ar"Atj  Atj+"5DAc 5Dfq@ 5  A ;  Ar"Atj  Atj+"5DAc 5Dfq@ 5  A ; Aj" G Aq"E @  Atj  Atj+"5DAc 5Dfq@ 5  A ; Aj! Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj+"5DAc@ 5  Ax :  Ar"j  Atj+"5DAc@ 5  Ax :  Ar"j  Atj+"5DAc@ 5  Ax :  Ar"j  Atj+"5DAc@ 5  Ax : Aj" G Aq"E @  j  Atj+"5DAc@ 5  Ax : Aj! Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj+"5DAc 5Dfq@ 5  A :  Ar"j  Atj+"5DAc 5Dfq@ 5  A :  Ar"j  Atj+"5DAc 5Dfq@ 5  A :  Ar"j  Atj+"5DAc 5Dfq@ 5  A : Aj" G Aq"E @  j  Atj+"5DAc 5Dfq@ 5  A : Aj! Aj" G  Aj" 6( A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj+"3"4"AO@AA AK  4C? AM   A vAqjAjA v ! Atj  3AvAqr; Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj+"3! Atj 3 3\A@A AH  AvAqjAjAv ; Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  A lj!@ Atj+"5 5b@@ , AH@ A6 (!  A: A: Aľ-: A¾/;  5Da@ , ! 5Dc@@ AH@ A6 (!  A: A: A6  @ AH@ A6 (!  A: A: A-: A/;   59  Aj" AA Aj"6l AL  AM@    Aj"/" A   59 A  G    . Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj+"3" Av"Aq!@@ 3C[ AqAF  Av"Aq" AI@ !  Aq! AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AtA@k Aq Avrr"A?q  AqAF" Av AqAGq AqAGqj!  Ar!  j : Aj" G  Aj" 6( AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj+"3"Av! @ 3C[@ Ar!  A! AqAF Aq! Aq! Av"Aq" @ AI  AM@ AGA Akt r A kvr  AGr !AA k" t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AtAj Aq Avrr"AqAGq j!  Ar!  A !  j : Aj" G  Aj" 6( @@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj+"3" Av"Aq!@@ 3C[ Aq"AF  AI@ !  Aq! AM@ Av! AG AqA Aktr A kvr Aq   !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AvA@kAq Aq Avrr! AqE  AqE  AqAM@ Aj!  Ar!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj+"3" Av!@ 3C[@ Ar!  A! Aq"AF Aq! Aq! @ AI  AM@ Av! AGA Akt r A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6( A$j  Aj"6 (! A F@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ "AL A! A! AO@ Aq! @  j  Atj)BR:  Ar"j  Atj)BR:  Ar"j  Atj)BR:  Ar"j  Atj)BR: Aj" G Aq"E @  j  Atj)BR: Aj! Aj" G A6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj)>  Ar"Atj  Atj)>  Ar"Atj  Atj)>  Ar"Atj  Atj)> Aj" G Aq"E @  Atj  Atj)> Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j  j)7  Ar"j  j)7  Ar"j  j)7  Ar"j  j)7 Aj" G Aq"E @  At" j  j)7 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq! @  Atj  Atj)8  Ar"Atj  Atj)8  Ar"Atj  Atj)8  Ar"Atj  Atj)8 Aj" G Aq"E @  Atj  Atj)8 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j  j)9  Ar"j  j)9  Ar"j  j)9  Ar"j  j)9 Aj" G Aq"E @  At" j  j)9 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj)>  Ar"Atj  Atj)>  Ar"Atj  Atj)>  Ar"Atj  Atj)> Aj" G Aq"E @  Atj  Atj)> Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj)=  Ar"Atj  Atj)=  Ar"Atj  Atj)=  Ar"Atj  Atj)= Aj" G Aq"E @  Atj  Atj)= Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj)=  Ar"Atj  Atj)=  Ar"Atj  Atj)=  Ar"Atj  Atj)= Aj" G Aq"E @  Atj  Atj)= Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj)<  Ar"j  Atj)<  Ar"j  Atj)<  Ar"j  Atj)< Aj" G Aq"E @  j  Atj)< Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj)<  Ar"j  Atj)<  Ar"j  Atj)<  Ar"j  Atj)< Aj" G Aq"E @  j  Atj)< Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj  Atj)"3"AO@AA AK  3C? AM   A vAqjAjA v ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  Atj  Atj)" AvAqjAjAv;  Ar" Atj  Atj)" AvAqjAjAv; Aj" G E   Atj  Atj)" AvAqjAjAv;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Aj  Atj)! A lj", AH@ (.  )p7  (x6 Aj" G  Aj" 6H @@@@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @@  Atj)" AqAF@ Av!  A! Av"AI Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ At AvrA@k"Aq  AqAF" Av AqAGq AqAGqj!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  Atj)" AqAF Av"@A! AI  Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq At AvrAj"AqAGq j!  A!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj)" Av!@ Aq"AF@ !  A! AI Aq! AM@ Av! AGA Akt A kvr AqA  !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ AvA@kAq Avr! AqE  AqE  AqAM@ Aj!  Ar!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  Atj)" Aq"AF @A! AI  Aq! AM@ Av! AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Avr"AqAGq j!  A!  A! j : Aj" G  Aj6H Aj  Aj"6 (! ("A F@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ "AL A! A! AO@ Aq! @  j  Atj(AG:  Ar"j  Atj(AG:  Ar"j  Atj(AG:  Ar"j  Atj(AG: Aj" G Aq"E @  j  Atj(AG: Aj! Aj" G A6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j  j(6  Ar"j  j(6  Ar"j  j(6  A r"j  j(6 Aj" G Aq"E @  At" j  j(6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj57  Ar"Atj  Atj57  Ar"Atj  Atj57  Ar"Atj  Atj57 Aj" G Aq"E @  Atj  Atj57 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ " AL A! A! AO@ Aq!@  At"j  j(8  Ar"j  j(8  Ar"j  j(8  A r"j  j(8 Aj" G Aq"E @  At" j  j(8 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj(9  Ar"Atj  Atj(9  Ar"Atj  Atj(9  Ar"Atj  Atj(9 Aj" G Aq"E @  Atj  Atj(9 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj57  Ar"Atj  Atj57  Ar"Atj  Atj57  Ar"Atj  Atj57 Aj" G Aq"E @  Atj  Atj57 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj(;  Ar"Atj  Atj(;  Ar"Atj  Atj(;  Ar"Atj  Atj(; Aj" G Aq"E @  Atj  Atj(; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj(;  Ar"Atj  Atj(;  Ar"Atj  Atj(;  Ar"Atj  Atj(; Aj" G Aq"E @  Atj  Atj(; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj(:  Ar"j  Atj(:  Ar"j  Atj(:  Ar"j  Atj(: Aj" G Aq"E @  j  Atj(: Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj(:  Ar"j  Atj(:  Ar"j  Atj(:  Ar"j  Atj(: Aj" G Aq"E @  j  Atj(: Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj  Atj("3"AO@AA AK  3C? AM   A vAqjAjA v ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  Atj  Atj(" AvAqjAjAv;  Ar" Atj  Atj(" AvAqjAjAv; Aj" G E   Atj  Atj(" AvAqjAjAv;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Aj  Atj( A lj", AH@ (.  )p7  (x6 Aj" G  Aj" 6H @@@@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @@  Atj(" AqAF@ Av!  A! Av"AI Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ At AvrA@k"Aq  AqAF" Av AqAGq AqAGqj!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  Atj(" AqAF Av"@A! AI  Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq At AvrAj"AqAGq j!  A!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj(" Av!@ Aq"AF@ !  A! AI Aq! AM@ Av! AGA Akt A kvr AqA  !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ AvA@kAq Avr! AqE  AqE  AqAM@ Aj!  Ar!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  Atj(" Aq"AF @A! AI  Aq! AM@ Av! AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Avr"AqAGq j!  A!  A! j : Aj" G  Aj6H Aj  Aj"6 (! AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ "AL A! A! AO@ Aq! @  j  Atj/AG:  Ar"j  Atj/AG:  Ar"j  Atj/AG:  Ar"j  Atj/AG: Aj" G Aq"E @  j  Atj/AG: Aj! Aj" G A6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj.6  Ar"Atj  Atj.6  Ar"Atj  Atj.6  Ar"Atj  Atj.6 Aj" G Aq"E @  Atj  Atj.6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj27  Ar"Atj  Atj27  Ar"Atj  Atj27  Ar"Atj  Atj27 Aj" G Aq"E @  Atj  Atj27 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq! @  Atj  Atj.8  Ar"Atj  Atj.8  Ar"Atj  Atj.8  Ar"Atj  Atj.8 Aj" G Aq"E @  Atj  Atj.8 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj.9  Ar"Atj  Atj.9  Ar"Atj  Atj.9  Ar"Atj  Atj.9 Aj" G Aq"E @  Atj  Atj.9 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj27  Ar"Atj  Atj27  Ar"Atj  Atj27  Ar"Atj  Atj27 Aj" G Aq"E @  Atj  Atj27 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj.6  Ar"Atj  Atj.6  Ar"Atj  Atj.6  Ar"Atj  Atj.6 Aj" G Aq"E @  Atj  Atj.6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j  j/;  Ar"j  j/;  Ar"j  j/;  Ar"j  j/; Aj" G Aq"E @  At" j  j/; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-: Aj" G Aq"E @  j  Atj-: Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-: Aj" G Aq"E @  j  Atj-: Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  At" j."3"4"AO@AA AK  4C? AM   A vAqjAjA v ! j  3AvAqr; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  At" j  j." AvAqjAjAv;  Ar" j  j." AvAqjAjAv; Aj" G E   At"j  j." AvAqjAjAv;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Aj  Atj. A lj", AH@ (.  )p7  (x6 Aj" G  Aj" 6H @@@@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj."Av!@ AqAF Aq! Av"Aq"AI@ !  Aq! AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q vAqE  Aj!  AM@ AtA@k Aq Avrr"A?q  AqAF" Av AqAGq AqAGqj!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@ Atj." AqAF Av"Aq! Aq! Av"Aq"@ AI  AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AtAj Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj." Av!@ Aq"AF Aq! AI@ !  Aq! AM@ Av! AG AqA Aktr A kvr Aq   !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ AvA@kAq Aq Avrr! AqE  AqE  AqAM@ Aj!  Ar!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@ Atj." Aq"AF Av"Aq! Aq! @ AI  AM@ Av! AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6H Aj  Aj"6 (! @ ("AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ "AL A! A! AO@ Aq! @  j  Atj/AG:  Ar"j  Atj/AG:  Ar"j  Atj/AG:  Ar"j  Atj/AG: Aj" G Aq"E @  j  Atj/AG: Aj! Aj" G A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj/6  Ar"Atj  Atj/6  Ar"Atj  Atj/6  Ar"Atj  Atj/6 Aj" G Aq"E @  Atj  Atj/6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj37  Ar"Atj  Atj37  Ar"Atj  Atj37  Ar"Atj  Atj37 Aj" G Aq"E @  Atj  Atj37 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq! @  Atj  Atj/8  Ar"Atj  Atj/8  Ar"Atj  Atj/8  Ar"Atj  Atj/8 Aj" G Aq"E @  Atj  Atj/8 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj/9  Ar"Atj  Atj/9  Ar"Atj  Atj/9  Ar"Atj  Atj/9 Aj" G Aq"E @  Atj  Atj/9 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj37  Ar"Atj  Atj37  Ar"Atj  Atj37  Ar"Atj  Atj37 Aj" G Aq"E @  Atj  Atj37 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  Atj/6  Ar"Atj  Atj/6  Ar"Atj  Atj/6  Ar"Atj  Atj/6 Aj" G Aq"E @  Atj  Atj/6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! A! AO@ Aq!@  At"j  j/;  Ar"j  j/;  Ar"j  j/;  Ar"j  j/; Aj" G Aq"E @  At" j  j/; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-: Aj" G Aq"E @  j  Atj-: Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-:  Ar"j  Atj-: Aj" G Aq"E @  j  Atj-: Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  At" j/"3"AO@AA AK  3C? AM   A vAqjAjA v ! j ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  At" j  j/" AvAqjAjAv;  Ar" j  j/" AvAqjAjAv; Aj" G E   At"j  j/" AvAqjAjAv;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  ! ! "AJ@A!@ Aj  Atj/ A lj", AH@ (.  )p7  (x6 Aj" G  Aj" 6H @@@@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  ! ! "AL A! @@  Atj/" AqAF@ Av!  A! Av"AI Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ At AvrA@k"Aq  AqAF" Av AqAGq AqAGqj!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  Atj/" AqAF Av"@A! AI  Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq At AvrAj"AqAGq j!  A!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj/" Av!@ Aq"AF@ !  A! AI Aq! AM@ Av! AGA Akt A kvr AqA  !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ AvA@kAq Avr! AqE  AqE  AqAM@ Aj!  Ar!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  Atj/" Aq"AF @A! AI  Aq! AM@ Av! AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Avr"AqAGq j!  A!  A! j : Aj" G  Aj6H Aj  Aj"6 (! AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ "AL A! A! AO@ Aq! @  j  j-AG:  Ar"j  j-AG:  Ar"j  j-AG:  Ar"j  j-AG: Aj" G Aq"E @  j  j-AG: Aj! Aj" G A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j,6  Ar"Atj  j,6  Ar"Atj  j,6  Ar"Atj  j,6 Aj" G Aq"E @  Atj  j,6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j07  Ar"Atj  j07  Ar"Atj  j07  Ar"Atj  j07 Aj" G Aq"E @  Atj  j07 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq! @  Atj  j,8  Ar"Atj  j,8  Ar"Atj  j,8  Ar"Atj  j,8 Aj" G Aq"E @  Atj  j,8 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j,9  Ar"Atj  j,9  Ar"Atj  j,9  Ar"Atj  j,9 Aj" G Aq"E @  Atj  j,9 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j07  Ar"Atj  j07  Ar"Atj  j07  Ar"Atj  j07 Aj" G Aq"E @  Atj  j07 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j,6  Ar"Atj  j,6  Ar"Atj  j,6  Ar"Atj  j,6 Aj" G Aq"E @  Atj  j,6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j,;  Ar"Atj  j,;  Ar"Atj  j,;  Ar"Atj  j,; Aj" G Aq"E @  Atj  j,; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  Atj  j,;  Ar"Atj  j,;  Ar"Atj  j,;  Ar"Atj  j,; Aj" G Aq"E @  Atj  j,; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq! @  j  j-:  Ar"j  j-:  Ar"j  j-:  Ar"j  j-: Aj" G Aq"E @  j  j-: Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  j,"3"4"AO@AA AK  4C? AM   A vAqjAjA v ! Atj  3AvAqr; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  Atj  j," AvAqjAjAv;  Ar" Atj  j," AvAqjAjAv; Aj" G E   Atj  j," AvAqjAjAv;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Aj  j, A lj", AH@ (.  )p7  (x6 Aj" G  Aj" 6H @@@@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  ! ! "AL A! @ j,"Av!@ AqAF Aq! Av"Aq"AI@ !  Aq! AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q vAqE  Aj!  AM@ AtA@k Aq Avrr"A?q  AqAF" Av AqAGq AqAGqj!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@ j," AqAF Av"Aq! Aq! Av"Aq"@ AI  AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AtAj Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ j," Av!@ Aq"AF Aq! AI@ !  Aq! AM@ Av! AG AqA Aktr A kvr Aq   !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ AvA@kAq Aq Avrr! AqE  AqE  AqAM@ Aj!  Ar!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@ j," Aq"AF Av"Aq! Aq! @ AI  AM@ Av! AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6H Aj  Aj"6 (! (" AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ "AL A! A! AO@ Aq!@  j  j-AG:  Ar"j  j-AG:  Ar"j  j-AG:  Ar"j  j-AG: Aj" G Aq"E @  j  j-AG: Aj! Aj" G A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j-6  Ar"Atj  j-6  Ar"Atj  j-6  Ar"Atj  j-6 Aj" G Aq"E @  Atj  j-6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j17  Ar"Atj  j17  Ar"Atj  j17  Ar"Atj  j17 Aj" G Aq"E @  Atj  j17 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq!@  Atj  j-8  Ar"Atj  j-8  Ar"Atj  j-8  Ar"Atj  j-8 Aj" G Aq"E @  Atj  j-8 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j-9  Ar"Atj  j-9  Ar"Atj  j-9  Ar"Atj  j-9 Aj" G Aq"E @  Atj  j-9 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j17  Ar"Atj  j17  Ar"Atj  j17  Ar"Atj  j17 Aj" G Aq"E @  Atj  j17 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j-6  Ar"Atj  j-6  Ar"Atj  j-6  Ar"Atj  j-6 Aj" G Aq"E @  Atj  j-6 Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j-;  Ar"Atj  j-;  Ar"Atj  j-;  Ar"Atj  j-; Aj" G Aq"E @  Atj  j-; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  Atj  j-;  Ar"Atj  j-;  Ar"Atj  j-;  Ar"Atj  j-; Aj" G Aq"E @  Atj  j-; Aj! Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ "AL A! A! AO@ Aq!@  j  j-:  Ar"j  j-:  Ar"j  j-:  Ar"j  j-: Aj" G Aq"E @  j  j-: Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj  j-"3"AO@AA AK  3C? AM   A vAqjAjA v ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  Atj  j-" AvAqjAjAv;  Ar"Atj  j-" AvAqjAjAv; Aj" G E   Atj  j-" AvAqjAjAv;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Aj  j- A lj", AH@ (.  )p7  (x6 Aj" G  Aj" 6H @@@@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ  ! ! "AL A! @@  j-" AqAF@ Av!  A! Av"AI Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ At AvrA@k"Aq  AqAF" Av AqAGq AqAGqj!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  j-" AqAF Av"@A! AI  Aq! AM@ AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq At AvrAj"AqAGq j!  A!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ j-" Av!@ Aq"AF@ !  A! AI Aq! AM@ Av! AGA Akt A kvr AqA  !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ AvA@kAq Avr! AqE  AqE  AqAM@ Aj!  Ar!  A! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @A!@  j-" Aq"AF @A! AI  Aq! AM@ Av! AGA Akt A kvr AG !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Avr"AqAGq j!  A!  A! j : Aj" G  Aj6H Aj  Aj"6 (! A F@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! ! "AJ@A!@  j  Atj/A t"Aq" AF@ Ar  AqAj  ArC AqAG: Aj" G A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r"3CO]@ 3  Ax 6 Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj~  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r"3C_]@ 3  B 7 Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a! "AJ@A!@ Atj  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r6 Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r9 Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj~  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r"3C_] 3C`q@ 3  B 7 Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ Atj  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r"3CO] 3C`q@ 3  A 6 Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  At"j/" !  j A t"Aq" AF@ Ar  AqAj  ArC Axqr"3CO]@ 3  Ax ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  At"j/" !  j A t"Aq" AF@ Ar  AqAj  ArC Axqr"3CO] 3C`q@ 3  A ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  j  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r"3CO]@ 3  Ax : Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  j  Atj/"Axq A t"Aq" AF@ Ar  AqAj  ArC r"3CO] 3C`q@ 3  A : Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  At"j/" !  j A t"Aq" AF@ Ar  AqAj  ArC " Axqr""3 3\@A@A AH   AvAqjAjAv ; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@ A lj!   Atj/"AqA t"Aq"AF@ Ar  Aj   ArC AqAtr  Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj/"Axq A t"Aq"AF@ Ar  AqAj   ArC " r" Av"Aq!@@ AqC[ AqAF  Av"Aq" AI@ !  Aq! AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AtA@k Aq Avrr"A?q  AqAF" Av AqAGq AqAGqj!  Ar!  j : Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj/"Axq A t"Aq"AF@ Ar  AqAj   ArC "r" Av! @ AqC[@ Ar!  A! AqAF Aq! Aq! Av"Aq" @ AI  AM@ AGA Akt r A kvr  AGr !AA k" t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AtAj Aq Avrr"AqAGq j!  Ar!  A !  j : Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj/"Axq A t"Aq"AF@ Ar  AqAj   ArC "r" Av"Aq!@@ AqC[ Aq" AF  AI@ !  Aq! AM@ Av! AG AqA Aktr A kvr Aq   !AA k"t" qE @ Aq Ak q vAqE  Aj!  AK AvA@kAq Aq Avrr! AqE  AqE  AqAM@ Aj!  Ar!  Ar!  j : Aj" G  Aj" 6H AF@@ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A! @ Atj/"Axq A t"Aq"AF@ Ar  AqAj   ArC " r"Av!@ AqC[@ Ar!  A! Aq"AF Aq! Aq! @ AI  AM@ Av! AGA Akt r A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6H Aj  Aj"6 (! ("AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ " AL A! AG@ Aq! Aq! @  j  Atj/AqAG:  Ar" j  Atj/AqAG: Aj" G E   j  Atj/AqAG: A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq! Aq! @  Atj  Atj/At"3CO]@ 3  Ax 6  Ar" Atj  Atj/At"3CO]@ 3  Ax 6 Aj" G E   Atj  Atj/At"3CO]@ 3  Ax 6  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!! Aq! @  Atj~  Atj/At"3C_]@ 3  B 7  Ar" Atj~  Atj/At"3C_]@ 3  B 7 Aj" G !E   Atj~  Atj/At"3C_]@ 3  B 7  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a!@ "AL A! A! AO@ Aq! @  Atj  Atj/At6  Ar"Atj  Atj/At6  Ar"Atj  Atj/At6  Ar"Atj  Atj/At6 Aj" G Aq"E @  Atj  Atj/At6 Aj! Aj" G  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!" Aq! @  Atj  Atj/At9  Ar" Atj  Atj/At9 Aj" G "E   Atj  Atj/At9  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!# Aq! @  Atj~  Atj/At"3C_] 3C`q@ 3  B 7  Ar" Atj~  Atj/At"3C_] 3C`q@ 3  B 7 Aj" G #E   Atj~  Atj/At"3C_] 3C`q@ 3  B 7  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!$ Aq! @  Atj  Atj/At"3CO] 3C`q@ 3  A 6  Ar" Atj  Atj/At"3CO] 3C`q@ 3  A 6 Aj" G $E   Atj  Atj/At"3CO] 3C`q@ 3  A 6  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!% Aq! @  At" j  j/At"3CO]@ 3  Ax ;  Ar" j  j/At"3CO]@ 3  Ax ; Aj" G %E   At"j  j/At"3CO]@ 3  Ax ;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!& Aq! @  At" j  j/At"3CO] 3C`q@ 3  A ;  Ar" j  j/At"3CO] 3C`q@ 3  A ; Aj" G &E   At"j  j/At"3CO] 3C`q@ 3  A ;  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!' Aq! @  j  Atj/At"3CO]@ 3  Ax :  Ar" j  Atj/At"3CO]@ 3  Ax : Aj" G 'E   j  Atj/At"3CO]@ 3  Ax :  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! !@ " AL A! AG@ Aq!( Aq! @  j  Atj/At"3CO] 3C`q@ 3  A :  Ar" j  Atj/At"3CO] 3C`q@ 3  A : Aj" G (E   j  Atj/At"3CO] 3C`q@ 3  A :  Aj" 6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  At" j/"AtAq"AO@AA AK  C? AM   A vAqjAjA v ! j  Aqr; Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@C  Atj/"At AqAK  A lj Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj/" Av"Aq!@@ At"AqC[ AqAF  Av"Aq" AI@ !  Aq! AM@ AG AqA Aktr A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AtA@k Aq Avrr"A?q  AqAF" Av AqAGq AqAGqj!  Ar!  j : Aj" G  Aj" 6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  Atj/"Av! @ At"AqC[@ Ar!  A! AqAF Aq! Aq! Av"Aq" @ AI  AM@ AGA Akt r A kvr  AGr !AA k" t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AtAj Aq Avrr"AqAGq j!  Ar!  A !  j : Aj" G  Aj" 6H @@@@ Ak @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj/"Av"Aq!@@ At"AqC[ Aq" AF  AI@ !  Aq! AM@ Av! AG AqA Aktr A kvr Aq   !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AK AvA@kAq Aq Avrr! AqE  A/qE  AqAM@ Aj!  Ar!  Ar! j : Aj" G  @ ("E@B!  B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AL A! @ Atj/" Av!@ At"AqC[@ Ar!  A! Aq"AF Aq! Aq! @ AI  AM@ Av! AGA Akt r A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av A/qAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6H Aj  Aj6 (! @ AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! ! " AJ@A!@  j  A lj BR: Aj" G A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! 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B!A! @ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A! @  A lj"3" Av! @ 3C[@ Ar!  A! Aq"AF Aq! Aq! @ AI  AM@ Av! AGA Akt r A kvr  AGr !AA k"t" qE @ Aq Ak q  vAqE  Aj!  AM@ Av AqAGq AvAjAq Aq Avrr"AqAGq j!  Ar!  A ! j : Aj" G  Aj6H Aj  ( Aj6 (! (" AF@A! A6H  6D A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ A! ! !@ " AL A! A! AG@ Aq!) Aq!@A!  jA  j-"ArAF A Aq  AqAG :@  Ar"j-"ArAF Aq AqAG!  j : Aj" G )E  @  j-"ArAF Aq AqAG!  j : A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj "3CO]@ 3  Ax 6 Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! "AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj~ "3C_]@ 3  B 7 Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a! "AJ@A!@@  j-"AF@A~!  AF@A!  Axq! Aq! AvAq"E@ E   AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  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Aj" G B|BZ A! ! ! @ "AL A! A! AG@ Aq!* Aq!@A!  jA  j-"AF A Aq  AqAG :@  Ar"j-"AF Aq AqAG! j : Aj" G *E  @  j-"AF Aq AqAG!  j : A6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj "3CO]@ 3  Ax 6 Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj~ "3C_]@ 3  B 7 Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! a! " AJ@A!@A~!@  j-"AF Axq! Aq! AvAq"E@ E   AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj 6 Aj" G  Aj"6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  Atj  j-"AG| Axq! Aq! @ AvAq"E@ E   AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj! D 9 Aj" G  Aj"6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj~ "3C_] 3C`q@ 3  B 7 Aj" G  Aj"6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj "3CO] 3C`q@ 3  A 6 Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj "3CO]@ 3  Ax ; Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  Atj "3CO] 3C`q@ 3  A ; Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  j "3CO]@ 3  Ax : Aj" G  Aj"6H AF@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  B7p   Aj9 )p! Aj" G B|BZ ! ! " AJ@A!@  j,"Axq! Aq! @ AvAq"E@ E   Aq"AtAq Aq"" AI" tAtAqAA  kAtrr!  At Atr jAj!  j "3CO] 3C`q@ 3  A : Aj" G  Aj"6H A F@@ ("E@B!  B!A!@ ( M  ( Atj)"BS@B!  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V]2%?ѧ?Nۥ^,=`?MZ?]<i,@? ?2흩<?@Y]^3?G:\#=?`j?h+?@X?ӬZF&=`? 9Ǿ?Eh-@?9? O4%?'?-x!=?#~+?48D,=? a?l=?e?rK#/<@?3=?xTy%= ?'?}i3&=?NW?x.<,=?pu?!%?PD?Cpf`?9믾?,T=@?ZZ?oX(=?= firstProvider_SetHostMemcpyToHostMemcpyFromHostint onnxruntime::NodeIndexInfo::GetNodeOffset(NodeIndex) constconst KernelCreateInfo &onnxruntime::SessionState::GetNodeKernelCreateInfo(NodeIndex) constNode *onnxruntime::Graph::NodeAtIndexImpl(NodeIndex) constvirtual std::unique_ptr onnxruntime::ApiGraph::GetValueInfo(std::string_view) constint onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constconst OrtValue &onnxruntime::IExecutionFrame::GetMLValue(int) constauto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned short *, const TensorShapeVector &, const TensorShape &, const unsigned short *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constauto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned int *, const TensorShapeVector &, const TensorShape &, const unsigned int *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constauto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned char *, const TensorShapeVector &, const TensorShape &, const unsigned char *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constauto onnxruntime::StridedCopy(concurrency::ThreadPool *, unsigned long long *, const TensorShapeVector &, const TensorShape &, const unsigned long long *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constauto onnxruntime::StridedCopy(concurrency::ThreadPool *, std::string *, const TensorShapeVector &, const TensorShape &, const std::string *, const TensorShapeVector &)::(anonymous class)::operator()(std::ptrdiff_t, std::ptrdiff_t) constconst OrtValue &onnxruntime::TensorSeq::GetAt(size_t) constvirtual int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constvirtual int onnxruntime::standalone::StandAloneKernelContext::NumVariadicInputs(size_t) constint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constint64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constStream *onnxruntime::DeviceStreamCollectionImpl::GetStream(size_t) constauto onnxruntime::BFCArena::Extend(size_t)::(anonymous class)::operator()(const size_t) conststd::vector onnxruntime::SparseTensor::GetCooIndexDims(size_t, size_t) constTensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const KernelRegistryManager &, const InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constauto onnxruntime::PlannerImpl::ComputeValueLocation()::(anonymous class)::operator()(const NodeArg &, size_t) constonnx::GraphProto onnxruntime::Graph::ToGraphProtoWithExternalInitializers(const std::string &, const PathString &, size_t) constauto onnxruntime::contrib::NGramRepeatBlock::Compute(OpKernelContext *)::(anonymous class)::operator()(int64_t) constconst std::vector &onnxruntime::GraphViewer::GetNodesInTopologicalOrder(ExecutionOrder) constauto onnxruntime::SessionState::PrepackConstantInitializedTensors(InlinedHashMap &, const std::unordered_map &)::(anonymous class)::operator()(bool) constauto onnxruntime::CreateSchema(const std::string &, const std::vector &)::(anonymous class)::operator()(const OrtCustomOp *, int, int, bool) constNode *onnxruntime::NodesToOptimize::GetNode(size_t, bool) constTensorShapeVector onnxruntime::PoolAttributes::SetOutputSize(const TensorShape &, int64_t, TensorShapeVector *, bool) constvoid onnxruntime::PoolAttributes::InferOutputSize(gsl::span, TensorShapeVector *, TensorShapeVector *, bool) constvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(TensorShape, int64_t, const TensorShapeVector &, const TensorShapeVector &, const TensorShapeVector &, const TensorShapeVector &, const int64_t, ConvPadVector *, TensorShapeVector *, bool) constvoid onnxruntime::Node::ToProto(NodeProto &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes &, NodeIndex, const OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ValidNodes> &, NodeIndex, const OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constconst void *onnxruntime::Tensor::DataRaw(MLDataType) constNode *onnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEnd(Graph &, End) constconst Node *onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEnd(const Graph &, End) constvirtual Status onnxruntime::SequenceEmpty::Compute(OpKernelContext *) constvirtual Status onnxruntime::Trilu::Compute(OpKernelContext *) constvirtual Status onnxruntime::SequenceConstruct::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::QLinearConcat::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::MatMulNBits::Compute(OpKernelContext *) constvirtual Status onnxruntime::Compress::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::ExpandDims::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::BifurcationDetector::Compute(OpKernelContext *) constvirtual Status onnxruntime::ml::FeatureVectorizer::Compute(OpKernelContext *) constvirtual Status onnxruntime::ConvInteger::Compute(OpKernelContext *) constvirtual Status onnxruntime::MatMulInteger::Compute(OpKernelContext *) constvirtual Status onnxruntime::Loop::Compute(OpKernelContext *) constvirtual Status onnxruntime::DeepCpuGruOp::Compute(OpKernelContext *) constvirtual common::Status onnxruntime::ml::ImputerOp::Compute(OpKernelContext *) constvirtual Status onnxruntime::DeepCpuLstmOp::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(OpKernelContext *) constvirtual Status onnxruntime::ReverseSequenceOp::Compute(OpKernelContext *) constvirtual Status onnxruntime::NonMaxSuppression::Compute(OpKernelContext *) constvirtual Status onnxruntime::Flatten::Compute(OpKernelContext *) constvirtual Status onnxruntime::QLinearMatMul::Compute(OpKernelContext *) constvirtual Status onnxruntime::SplitImpl::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::NGramRepeatBlock::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::transformers::GreedySearch::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::transformers::BeamSearch::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::transformers::Sampling::Compute(OpKernelContext *) constvirtual Status onnxruntime::If::Compute(OpKernelContext *) constvirtual Status onnxruntime::Squeeze::Compute(OpKernelContext *) constvirtual Status onnxruntime::Transpose::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::QLinearWhere::Compute(OpKernelContext *) constvirtual Status onnxruntime::Reshape::Compute(OpKernelContext *) constvirtual Status onnxruntime::Tile::Compute(OpKernelContext *) constvirtual Status onnxruntime::ConcatFromSequence::Compute(OpKernelContext *) constvirtual Status onnxruntime::Pad::Compute(OpKernelContext *) constvirtual Status onnxruntime::STFT::Compute(OpKernelContext *) constvirtual Status onnxruntime::GatherND::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::MatMulFpQ4::Compute(OpKernelContext *) constvirtual Status onnxruntime::contrib::MurmurHash3::Compute(OpKernelContext *) constvoid onnxruntime::mod_internal::CallModImpl::operator()(bool, OpKernelContext *) constvoid onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constvoid onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, AutoPadType, int64_t *, int64_t *, int64_t *) constStatus onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(OpKernelContext *, const uint8_t *, const TensorShape &, float, uint8_t, bool, const Tensor *, const Tensor *, const Tensor *, const Tensor *) constStatus onnxruntime::SplitToSequence::ComputeImpl(OpKernelContext &, const Tensor &, const Tensor *) constvoid onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, ptrdiff_t, ptrdiff_t, ptrdiff_t, const std::vector &, const std::vector &, Tensor &, Tensor &, POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constint onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) constvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constconst onnxruntime::SparseTensor &OrtValue::Get() constconst onnxruntime::Tensor &OrtValue::Get() constconst onnxruntime::TensorSeq &OrtValue::Get() constvirtual size_t onnxruntime::ApiTensor::NumElements() constSparseTensor::CsrView onnxruntime::SparseTensor::AsCsr() constonnx::GraphProto onnxruntime::Graph::ToGraphProto() constSparseTensor::CooView onnxruntime::SparseTensor::AsCoo() constconst NodeIndexInfo &onnxruntime::SessionState::GetNodeIndexInfo() constHashValue onnxruntime::PrePackedWeights::GetHash() constSparseTensor::BlockSparseView onnxruntime::SparseTensor::AsBlockSparse() constvoid onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() constNodesToOptimizeIndices onnxruntime::NodesToOptimizeIndicesBuilder::Build() constvirtual std::vector onnxruntime::ApiTensor::Data() constauto onnxruntime::GetAvailableExecutionProviderNames()::(anonymous class)::operator()() constTensor *onnxruntime::contrib::AttentionBase::GetPresent(OpKernelContext *, const Tensor *, int, int, int, int &) constauto onnxruntime::optimizer_utils::GetClipConstantMinMax(const Graph &, const Node &, float &, float &)::(anonymous class)::operator()(const Node &, size_t, float &) constvirtual common::Status onnxruntime::CPUDataTransfer::CopyTensor(const Tensor &, Tensor &) constvirtual Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constvirtual Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(Graph &, bool &, int, const logging::Logger &) constvirtual Status onnxruntime::GatherToSliceFusion::ApplyImpl(Graph &, bool &, int, const logging::Logger &) constvirtual Status onnxruntime::ConstantFolding::ApplyImpl(Graph &, bool &, int, const logging::Logger &) constvirtual Status onnxruntime::FuseReluClip::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constvirtual Status onnxruntime::GemmSumFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constvirtual Status onnxruntime::ConvMulFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constvirtual Status onnxruntime::ConvAddFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constvirtual Status onnxruntime::ConvBNFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constvirtual Status onnxruntime::MatmulBNFusion::Apply(Graph &, Node &, RewriteRuleEffect &, const logging::Logger &) constvirtual bool onnxruntime::GemmSumFusion::SatisfyCondition(const Graph &, const Node &, const logging::Logger &) constvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto &) constvirtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto &) constvirtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constvirtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto &) constvirtual bool onnxruntime::OptionalTypeBase::IsCompatible(const onnx::TypeProto &) constbool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constbool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constauto onnxruntime::function_utils::IOTypeConstraintHelper(const onnx::FunctionProto &, std::unique_ptr &, const InlinedHashMap &, const InlinedHashMap &)::(anonymous class)::operator()(const onnx::NodeProto &) constsize_t onnxruntime::string_normalizer::Utf8ConverterGeneric::ComputeRequiredSizeToUtf8(const std::wstring &) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constvirtual std::vector onnxruntime::(anonymous namespace)::actions::FuseConvAddRelu::ValueMoves(const RuntimeState &) constvirtual std::vector onnxruntime::(anonymous namespace)::actions::FuseConvAddActivationAction::ValueMoves(const RuntimeState &) constvirtual NodeAttributes onnxruntime::(anonymous namespace)::actions::FuseConvActivationAction::ExtraAttributes(const RuntimeState &) constvirtual NodeAttributes onnxruntime::(anonymous namespace)::actions::FuseConvAddActivationAction::ExtraAttributes(const RuntimeState &) constvirtual std::string onnxruntime::(anonymous namespace)::actions::FuseConvActivationAction::OpType(const RuntimeState &) constStatus onnxruntime::UnsqueezeBase::PrepareCompute(OpKernelContext *, Prepare &) constStatus onnxruntime::ConcatBase::PrepareForCompute(OpKernelContext *, const InlinedTensorsVector &, Prepare &) constauto onnxruntime::SliceImpl(OpKernelContext *, const Tensor &, SliceOp::PrepareForComputeMetadata &)::(anonymous class)::operator()(SliceIterator &) constvoid onnxruntime::Graph::KahnsTopologicalSort(const std::function &, const std::function &) const failed. File doesn't existThe size of each input in the input listCDistis_test specified. It should be either bilinear or nearestUpsampleNearestaxis == 1 || axis == largestuse_pastchannels_lastcounter.current_offset == lastis_inner_broadcastGemm: Invalid bias shape for broadcastconst_castreinterpret_caststatic_castdynamic_castStringToHashBucketFast_CastSqrtdrop_no_int16_supportopset_importinput array is too shortunsigned shortSequenceInsertalertStart MatchInputMaskSubgraphDistilBertPass MatchInputMaskSubgraphDistilBertFuseSubGraphDistilBertFuseSubGraphQKDistilBertstartmodel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartKhitan_Small_Script noexceptparameters_.model_type == IGenerationParameters::kModelTypeGptq_rotk_rotParsePathRoot, but its domain is not, but it its domain is not was notearly stop or notCypriotBitwiseNotOneHot_itercountinput_def_count == kExpected_input_countmax_skip_countsequence_token_countinvalid node output count'axes' has an axis outside of the tensor dimension countZipmap does not support empty dim countComputeReuseCountprintz_zero_pointy_zero_pointx_zero_pointw_zero_pointinput_zero_pointweight_zero_pointsegment_embedding_zero_pointposition_embedding_zero_pointword_embedding_zero_pointreduced_zero_pointb_zero_pointbeta_zero_pointdata_zero_pointgamma_zero_pointY_zero_pointX_zero_pointW_zero_pointR_zero_pointC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointB_zero_pointA_zero_point%s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointvalue_intp_int >= base_intunsigned intEndTimeAndRecordEventOptional op must have a TypeProto in the 'type' attribute if the attribute is presentInput 'past_key' and 'past_value' shall be both present or both absentRecurrentinvalid number; expected '+', '-', or digit after exponentlog sum exponentInvalid argumentWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentonnx.TensorProto.Segmentinput_gather_elementSequence is missing type entry for its element' is expected to have a single boolean elementbuffer size is too small for string elementIf nodes condition input must have exactly one elementOptionalGetElementOptionalHasElementcircumflex-accentgrave-accentposition_embedding_quantword_embedding_quantbeta_quantgamma_quantqdq_s8_to_u8_quant_min_zero_constantGemm bias is not constantunidir mask is not constantWeight point must be constant_BitIntconcat_resultUse DeviceBasedPartition as defaultGetVectorAttrsOrDefaultinitEnsureUniqueDQForNodeUnitGRUUnitInitlimitmaxsplitOnly supports `int32_t` or `int64_t` inputs for splitStringSplitFaild to find path v to Splitxdigitoperator co_awaitWaitTensorRT Provider not available, can't get interface for itModelProto needs to be parsed to check for ORT config within itkv_weightorder_weightq_weightorder_global_weightscale_global_weightscale_weightscale_V_weightscale_Q_weightscale_K_weightInteger representing the embedding vector size for each word.If not provide, use the filter size of conv weightThe node id of each weightoutput_heightcom.microsoftBitShiftIrfftGRU operator does not support double yetLSTM operator does not support double yet is not supported yetProfiler not started yetopsettoken_offsetlinear_before_resetRunStateOnByteUnlocked failed after Resetattribute case_change_action is not setattribute mincharnum is not setattribute mark is not setattribute is_case_sensitive is not setattribute pad_value is not setcustom join thread function not setInput type is not float tensor but keys_floats is setInput type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setpast_sequence_length tensor must be of one element when past_present_share_buffer is setEither one of the separators OR tokenexp attributes required but none is setmax_sequence_length not matching from mask and past when past_present_share_buffer_ is setNot expecting an allocator setDestination must have a CPU allocator setThis TypeProto does not have ValueCase setThis method does not expect allocator to be setDeseretStringToHashBucketright-curly-bracketleft-curly-bracketright-square-bracketleft-square-bracketTai_Vietinput_forgetexpanded_targetThe weight for each targetRegisterOpSetGetDetSequenceConstructproductpunct restrictreflectobjc_objectOctConvActConvAddActfstatdefault_floatconst_zero_floatreplaced_value_floatconst_one_floatExpect mask data type is uint8 or floatCast Input to floatMatMulIntegerToFloat_Floatpixel_formatsession.save_model_formatsession.load_model_format[AudioDecoder]: Cannot detect audio stream formatInput must be of CSR formatInput must be of COO formatLoadOpsetImportOrtFormatSaveInitializerOrtFormatSaveSparseInitializerOrtFormatLoadSparseInitializerOrtFormatLoadInitializerOrtFormatLoadOpIdentifierOrtFormatSaveValueInfoOrtFormatLoadValueInfoOrtFormatSaveTypeInfoOrtFormatLoadTypeInfoOrtFormatSaveToOrtFormatSaveRuntimeOptimizationRecordToOrtFormatLoadTensorDimensionOrtFormatLoadEdgesFromOrtFormatLoadRuntimeOptimizationRecordFromOrtFormatLoadFromOrtFormatSaveAttributeOrtFormatLoadAttributeOrtFormatSaveMapTypeOrtFormatLoadMapTypeOrtFormatSaveSequenceTypeOrtFormatLoadSequenceTypeOrtFormatLoadTensorShapeOrtFormatLoadTensorTypeAndShapeOrtFormatFailed to match v_concatcom.microsoft.QLinearConcatStringConcatSatabsl::container_internal::raw_hash_map<>::atcommercial-atstd::nullptr_twchar_tchar8_tchar16_tchar32_tinvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tUtTtStLtSequenceAtkeysMapNamesToMLValueIdxshyphen-minusParametricSoftplusreverse-solidusgsl::narrow_cast(output_axes_.size()) == num_scan_outputsv_final_and_scan_outputsfinal_state_and_scan_outputsnum_subgraph_outputs - 1 == num_outputsValidateInputsOutputsInitInputsInitializersOutputsGetModelOutputsSynchronizeOutputsnum_scan_inputsinitial_state_and_scan_inputsadditional_inputsnum_variadic_inputs == num_subgraph_inputsconsumed_inputsEllipsis must indicate a fixed number of dimensions across all inputsEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsSlice does not have enough number of inputsMust have 1 or more inputsOutput subscript contains letters not seen in the inputs`shape` only supports `int32_t` or `int64_t` inputsGemm does not have 3 inputsSetupInputsGetThresholdsFromInputsGetModelInputsCheckInputsSynchronizeInputsValidateInputsn_supportsThe id of the token that indicates LM startsinterceptstree_rootsngram_countszero_pointsclasslabels_intsvalue_intsThe innermost dims should have the same dim value to parse the diagonal elements to have different number of elementsqkv_hidden_sizes should have 3 elementsqkv_hidden_sizes attribute should have 3 elementsInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsScatterElementsGatherElementsNode placementscoefficientsstrip_accentstuning_resultsSetTuningResultsnew_num_elts == old_num_eltsnodes_splitsfiltered_logitsGreedySearchProcessLogitsinvalid string: '\u' must be followed by 4 hex digitsbitsMatMulNBitsqkv_weightstarget_weightsfc3_experts_weightsfc2_experts_weightsfc1_experts_weightsclass_weightsnormalize_routing_weightsleaf_weightsWhether to normalize routing weightsCan not get unpacked span from prepacked weights doesn't have an implementation that can cache computed pre-packed weights doesn't have an implementation that can consume provided pre-packed weightsStringRegexSplitWithOffsetsStringECMARegexSplitWithOffsetsn_targetstransform_targetsthread_scheduling_statskeys_floatsvalues_floatsimputed_value_floats1-D tensor of floatsCurrently support only COO and CSR(x64) formatsrepeatslossSoftmaxCrossEntropyLossNegativeLogLikelihoodLossMatchAndProcesssuccessbad_variant_accessbad_optional_accessLess__backwardpassvectors_per_classmax_output_boxes_per_classmulti_classone_classbackground_classinvalid character classNo ranges in char classsupport_vectorsChosen support vectorsseparatorsvalidatorssession.use_env_allocatorsstring tensor is not supported for copying between allocatorslast_outputs.size() >= static_cast(1) + num_present_tensorssparse_tensorsNumber of subscripts in the input equation does not match number of input tensorsZipmap only supports 1D or 2D input tensorsConstrain input and output types to float tensorsConstrain input0 and output types to float tensorsConstrain to float tensorsConstrain input 0 type to float tensorsConstrain input and output types to integer tensors , or sparse tensorsSource and target must both be tensorsAll scan outputs MUST be tensors from initializer_allocation_order not found among initialized tensorsDone saving initialized tensorsCopyTensorsAllocateOutputTensorsCopySparseTensorsThis API supports Tensors or SparseTensorsInjectExternalInitializedTensorsSaveInitializedTensorsanchorsoptimization.disable_specified_optimizerssession.use_ort_model_bytes_for_initializerssession.use_device_allocator_for_initializers sparse initializer name is not unique across initializers and sparse_initializersProcessInitializersToGraphProtoWithExternalInitializersSaveModelWithExternalInitializersSaveWithExternalInitializersAddExternalInitializersGetOverridableInitializersOutput subscript contains repeated letters contains null pointersNumber of actual parameters cannot exceed number of formal parametersGetParametersCheckSliceParametersalign_cornerscannot compare iterators of different containersApplyTransformersAddPredefinedTransformersKernelUseSharedPrePackedBuffersdisable_synchronize_execution_providersSyncProvidersCannot concatenate scalarsCannot slice scalarsremove_control_charstokenize_chinese_charsnum_groups groupsNumGroupsmetadata_propsThe id of the token that indicates no timestampsOnly supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsRegisterCustomOpstype_protoszerosrel_posAcosNegPosCosThe 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 runssession_optionsFinalizeSessionOptionsTraceSessionOptionsReceived null OrtThreadingOptions is not found in model local functions did not return correct number of compiled functionsnum_detectionsscan_output_directionsscan_input_directionsBuildConnectionsApplySelectorsAndActionsoptimization.minimal_build_optimizationsApplyOrtFormatModelRuntimeOptimizationsApplySavedRuntimeOptimizationsactivationsdilationscom.microsoft.extensionsinputdimensionsInput tensor has no dimensionsIncompatible dimensionsInputs 0 (query) shall be 3 or 5 dimensionsThe past input shall be 5 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsInputs 0 (query) shall be 2 or 4 dimensionsThe past_key input shall be 4 dimensionskey and value cache shall be 4 dimensionsOutput tensor must have at least 3 dimensionsskip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsinput shall be 3 dimensionsInput 0 shall be 3 dimensionsInputs 0 shall be 3 dimensionsInputs 0 (query) shall be 3 dimensionsInputs 2 (value) shall be 3 dimensionsInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionssegment_ids input shall be 2 dimensionstoken_offset shall be 2 dimensionsinput_ids shall be 2 dimensionsInputs 0 shall be 2 dimensionsInputs 2 (value) shall be 2 dimensionsTarget shape may not have multiple -1 dimensionslowest_index >= 0 && lowest_index < num_spectrogram_binshighest_index >= 0 && highest_index < num_spectrogram_binsnum_mel_binsAddCustomOpDomainsmemory_seq_lenssequence_lenscur_tokensskip_special_tokenspred_tokensadded_tokenssrc_tokensExpecting same size spanskeepdimsnum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad - 1 != num_output_dimsleft_num_dims > 2 && left_num_dims == right_num_dimsdimension <= num_dimsbatch_dimsgrid_dims[grid_dims.NumDimensions() - 1] == data_dimstensor can't contain negative dimsaxis <= X_NumDims && axis >= -X_NumDimsExpandDimsend_idx >= start_idx && end_idx <= total_items-gramsactivation_paramskernel_paramsnum_streamsnum_beamsnum_beams >= 1 && num_beams <= kMaxNumBeamsRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsRegisterQuantizationKernelsCreateKernelsRegisterCpuContribKernelsRegisterCPUKernelsacross_channelslabelsnew_axisactivation_axisbatch_axisbatch_axis != time_axisrank must be greater than axisPadding for the beginning and ending along each spatial axisInvalid value for attribute axis'axes' has a duplicate axis'axes' has an out of range axisThe rank of input tensor must be >= axisgsl::narrow_cast(X_shape.NumDimensions()) >= axisInputs have ellipses in them but the provided output subscript does not contain an ellipsisright-parenthesisleft-parenthesisroisthisseq_lengthsFaild to match concat node for Gather pathsEgyptian_HieroglyphsAnatolian_HieroglyphsMeroitic_HieroglyphsCanUpdateImplicitInputNameInSubgraphsForThisAndAllSubgraphsCleanUnusedInitializersAndNodeArgskeys_stringscats_stringsclasslabels_stringsvalues_stringsclasses_stringspool_stringsvalue_stringsthis API does not support stringsUse GetStringTensor*() API to retrieve stringsInputs and outputs must be UTF-8 stringsInputs must be UTF-8 stringsUse MakeCsrStringsUse MakeCooStringsUse MakeBlockSparseStringsposition_embeddingspaddingsBad call to ParseState::ParsePerlFlagsnodes_trueleafsnodes_falseleafsqkv_hidden_sizesVerifyOutputSizesmax_output_boxesdetection_boxesngram_indexesnoop_with_empty_axesscan_output_axesscan_input_axessqueeze_axesnodes_valuesmembership_valuesnum_keys == num_valuesbase_valuesPads has incorrect number of values'pads' has wrong number of valuesngram_indexes must be non-empty with no negative valuesValuessizeof(uint32_t) == output_element_bytessession.optimized_model_external_initializers_min_size_in_bytesfrom_bytesinitial_chunk_size_bytesinitial_growth_chunk_size_bytesmax_power_of_two_extend_bytestried to allocate 0 bytesLoadOrtModelBytesInvalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesEmpty scale in attributesfailed at retrieving the removable attributes has no data attributesA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesPruneRemovableAttributesnotesdrop_statesdecoder subgraph output shall have same data type as that of encoder_hidden_statesdecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesnodes_hitratesgatesupdates'axes' attribute must not contain any duplicates'axes' has duplicatesdetection_classessequences_scoresdetection_scoresrequiresfmod attribute must be true for floating point typesConstrain to float typesConstrain input and output integer tensors typesshape inference not yet supported for non-tensor typesAll Tensor typesConstrain mask index to integer typesConstrain token_offset to integer typesConstrain sequence_token_count and token_offset to integer typesConstrain target to integer typesConstrain indices to integer typesConstrain to integer typesConstrain mask to integer typesConstrain mask, offset and sequence length to integer typesall typesdefined list of typesTensor sequence must contain only primitive typesAll Tensor and Sequence typesConstrain Scale to float32 typesInferAndVerifySubgraphTypesMatchKernelDefTypesTAggregatedTypesdisable_dynamic_shapesDimension could not be inferred: incompatible shapesValidateBlockSparseShapesMask is neither unidirectional nor all onestilesCreateLoopStateVariablesfc3_scalesfc2_scalesfc1_scales , or optional typed entitiessession.intra_op_thread_affinitiesdirections.size() == num_entriesUnloadLibrariespast_present_share_buffer mode must be same for init decoder and decoder subgraphespatchesmergesconcat_after_gather does not have expected number of inputs or output edgesSplit for Fused Gather nodesUnsqueeze for Fused Gather nodesSqueeze for Fused Gather nodesSlice for Fused Gather nodesOutput edge count not expected for mask nodesCreated a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodesnodes_modesInlineNodesGetCpuPreferredNodesSetOpSinceVersionForProducedNodesstridesnum_beams >= num_return_sequencesdevicesCopyOneInputAcrossDevicesCopyOutputsAcrossDevicesCopyInputsAcrossDevicesCurrently supporting only 2-D matricesbatch_indicesuse_indicesinverse_indicesthis tensor already has populated sparse_indicesselected_indicesExpecting the same number NNZ == size of Inner indices[BertTokenizerDecoder]: Expect positions empty or a [n, 2] matrix when use indicesExpecting 2xValues == indicesValidateCsrIndicesUseCsrIndicesUseCooIndicesUseBlockSparseIndicesclean_up_tokenization_spacesstopwordsTransposeSingleAxisOutwards::OrtKernelInfo output index is out of bounds::OrtKernelInfo input index is out of boundselement index is out of boundsIncorrect or missing input value for starts and endsIncorrect or missing attribute value for starts and endsleaf_targetidsnodes_featureidstarget_treeidsclass_treeidsnodes_treeidsnodes_truenodeidsnodes_falsenodeidstarget_nodeidsclass_nodeidsnodes_nodeidsencoder_input_idsdecoder_input_idsInput 'attention_mask' is expected to have same shape as input_idssegment_idstarget_idsclass_idsposition_idsall_special_idsextra_decoding_idscopy_info.size() == num_feedsCalculateStaticCopyInfoForFeedsSetup must be called before CreateInitialFeedsThe 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 padsinter_op_num_threadsintra_op_num_threadsnum_of_threadsMore work items than threadsSetInterOpNumThreadsSetIntraOpNumThreadsInput 'past_key' shall have kv_num_headsInput 'past_value' shall have kv_num_headsInputs 'past' dimension 2 shall have length of num_headsNumber of attention headssubgraph past state dimension 2 shall have a positive value for number of headsConvTransposeWithDynamicPadsGetFuncsrouter_probsnon_speech_probsRegisterGraphNodeOpSchemasRegisterCustomOpNodeSchemasantialiasquery_biasqkv_biasfc3_experts_biasfc2_experts_biasfc1_experts_biasAttention cannot have both past and relative_position_biasglobal_biasscale_biasV_biasQ_biasK_biascublasLt order of global biasGatedRelativePositionBiasMatMulBnFusion_GemmBiasMaybeTransposeToBNSHAndAddBias\sZsTsPsCskeys_int64scats_int64sclasslabels_int64svalues_int64spool_int64simputed_value_int64sfourOld_UyghurattrGetAttrstring_view::substrallocator is nullptrResize: input/output value is nullptrUpsample: input/output value is nullptrcpu_allocator shouldn't be nullptr returned nullptrinfo == nullptrfile_path == nullptrinfo_ == nullptrinit_run_gpt_subgraph_ == nullptrwhisper_encoder_subgraph_ == nullptrt5_encoder_subgraph_ == nullptrwhisper_decoder_subgraph_ == nullptrt5_decoder_subgraph_ == nullptrprepacked_buffers[0].get() == nullptrentry != nullptrcontext != nullptroutput != nullptrother_sum_input != nullptroutput_sequences != nullptrx_ptr != nullptrinput_tensor_ptr != nullptrallocator != nullptraxis_tensor != nullptraxes_tensor != nullptrinput_tensor != nullptr && indices_tensor != nullptrinitializer != nullptrsession_logger != nullptrelem_proto != nullptrgraph_proto != nullptrvalue_proto != nullptrtype_proto != nullptrkernel_info != nullptractivation != nullptrkernel != nullptrinitializer_node_arg != nullptrouter_scope_node_arg != nullptrkernel_create_info.kernel_def != nullptrnon_tensor_base != nullptrelt_type != nullptrml_type != nullptrvalue_type != nullptrtensor_data_type != nullptrc_shape != nullptrframe != nullptrdst_node != nullptrsrc_node != nullptrinput_tensor_cross_qk_layer_head != nullptrallocator_ != nullptrnode_arg_ != nullptrsession_state_ != nullptrdtype_ != nullptrelem_type_ != nullptrfetch_unique_ptrs[i] != nullptrX != nullptrA != nullptr && B != nullptrgraph_->GetNode(idx) != nullptrallocator.get() != nullptrallocator_for_caching.get() != nullptralloc && alloc.get() != nullptrinput_arg->Type() != nullptroutput_ptrinput_ptrNull rois_ptrNull batch_indices_ptrexec_plan_ptrNull crop_size_ptrNull input ptrCan't use func with null ptrNull input X ptrResolveKernelTypeStrktsrFillSparseTensorCsrDenseTensorToSparseCsrAprvectorBifurcationDetectorStringToVectorArrayFeatureExtractorfactorinvalid_iteratorExpected AllocateFinalOutput to have been called to before we increment the iteratorno argument for repetition operatorbad repetition operatorinvalid perl operatorResize operatorUpsample operatoroutput count mismatch, expected 2 outputs to be present for TopK operatorobject separatorOuterScopeNodeArgLocationAccumulatorUnable to get an allocatorIf shape was concrete we shouldn't be using a custom allocatorThis method should follow a call to constructor that supplies the allocatorCreateAllocatorsuffix_indicatorLinearRegressorTreeEnsembleRegressorSVMRegressorlogits_processornullptr != p.output_tensordefault_tensorkeys_tensorboxes_tensorvalues_tensorscores_tensorbias_tensortarget_weights_as_tensorclass_weights_as_tensornodes_values_as_tensortarget_weightsbase_values_as_tensorclasslabels_int64sbase_values_as_tensornodes_hitrates_as_tensorvar_tensormean_tensorsparse_tensorscale_tensormatmul_b_tensorexisting->second == &tensorGatherElements op: Out of range value in index tensorEmpty dimensions for input tensorlast dimension of indices must not be larger than rank of input tensorValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensorConstrain input matrix data types as single precision float tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorConstrain input B data types as 16-bit integer tensorConstrain input A data types as 16-bit integer tensorAttribute expected to have a one-dim tensorProvided type is not an optional tensorCan not trace string tensorInput/Output is a string tensorThe input must be a UTF-8 string tensorthe ort_value must contain a constructed tensor or sparse tensorAttribute expected to have a one-dim sparse tensorthe ort_value must contain a constructed sparse tensor but received a sparse tensorsegment_ids must a single tensorProvided type is not an optional sequence tensorthe ort_value must contain a constructed tensorlast dimension of indices must not be larger and rank of data tensorArgument is not a tensor but received a tensorLast dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorshape input must be 1D tensor'shape' input must be 1D tensorInt64 tensor'repeat' input tensor must have the same length as the 'input' tensorCopyTensorReadExternalDataForTensorExtDataTensorProtoToTensoronnx.TypeProto.SparseTensorSparseCsrToDenseTensorSparseCooToDenseTensorCloneTensorUnfoldTensoronnx.TypeProto.TensorOrtValue should contain a Tensor or a Sparse Tensornarrowing_errorparse_errortype_errorwstring_convert: to_bytes errorwstring_convert: from_bytes errorno errorinternal errorparse errorunexpected errorSystemErrorround_prefer_floor, fall back to default allocation behaviorreused != reused_forThe index of the target that each weight is forBitwiseXorRegionForcache_dirInsertQDQPairDictVectorizerTfIdfVectorizerFeatureVectorizerBinarizerHfBertTokenizerSeparatorExpressionTokenizerWordpieceTokenizerBasicTokenizerRobertaTokenizerCLIPTokenizerGPT2Tokenizerconst_transpose_optimizerAdded in transpose optimizerTransposeOptimizerStringNormalizerGemm weight is not constant initializerGemm bias is not constant initializer' the model will use the latest encountered initializerGetScalarConstantInitializerUnsqueezeAxesInitializerSliceAxesInitializerAddConstantProtoAsInitializerStart ValidateGemmInitializerPass ValidateGemmInitializerTransposeInitializerAddInitializerquery_layerNumber of neurons in the hidden layerlowerStringLowersession.disable_double_qdq_removerDoubleQDQPairsRemoverThe number of channels to sum overAddLayoutTransformationRequiredOpsToKernelTypeStrResolverImputerScatter name shorterdelimiter_fence_afterMust be 2 for whisperparameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperupperCategoryMapperStringUpperAllocateMLValueTensorSelfOwnBufferHelperAllocateReusedOrtValueIfNotAllocatedHelperCreateGraphPartitionerPartitioner type is not DeviceBasedPartitionerUnable to write the provided PrePackedWeights instance into the containerMemcpyTransformerRemoveDuplicateCastTransformerQDQFinalCleanupTransformerQDQSelectorActionTransformerInserted by QDQPropagationTransformerFreeDimensionOverrideTransformer_RuleBasedTransformerNchwcTransformerNhwcTransformerQDQS8ToU8TransformerCastFloat16TransformerKhmernot_smallerImageScalerLinearClassifierTreeEnsembleClassifierSVMClassifierweight_gatherMatchPositionEmbeddingSubgraphsFromGather, but it its version is higherapply softmax to elements for dimensions axis or highernot_largerinter_op_num_threads option in the model file must be an integerintra_op_num_threads option in the model file must be an integergraph_optimization_level option in the model file must be an integerenable_profiling option in the model file must be an integerexecution_mode option in the model file must be an integerConvIntegerReduceSumIntegerMatMulInteger in KernelRegistryManagerCreateFeedsFetchesManagerdecoder subgraph: output_cross_qk could only work with past_present_share_bufferdecoder_masked_attention shall use with past_present_share_bufferReadFileIntoBufferLoadKernelTypeStrResolverFromBufferAllocateFinalBufferAllocateMLValueTensorPreAllocateBufferWebGPU_Bufferborderstorage_orderInvalid ExecutionOrderDequantizeWithOrderQuantizeWithOrderencoderOneHotEncoderLabelEncoderinit_decoderbyte_decoderBertTokenizerDecoderAudioDecoderBpeDecoderImageDecoderDeleteFolderp_provider::OrtKernelInfo cannot get a valid logger from its execution providerReceived nullptr for exec providerGetProviderRknpuExecutionProviderTensorrtExecutionProviderJsExecutionProviderRegisterExecutionProviderTvmExecutionProviderDnnlExecutionProviderDmlExecutionProviderXnnpackExecutionProviderNnapiExecutionProviderInternalTestingExecutionProviderAzureExecutionProviderSessionOptionsAppendExecutionProviderMIGraphXExecutionProviderCPUExecutionProviderOpenVINOExecutionProviderArmNNExecutionProviderWebNNExecutionProviderQNNExecutionProviderCANNExecutionProviderROCMExecutionProviderCoreMLExecutionProviderACLExecutionProviderVitisAIExecutionProviderSNPEExecutionProviderCUDAExecutionProviderLoadDynamicLibraryFromProviderLoadOrtModelWithLoaderLoadWithLoader~ExLibLoaderOctoberdilations_.size() == image_dim_numberstrides_.size() == image_dim_numberpads_.size() == 2 * image_dim_numberNovemberSeptemberDecemberinput_varrunning_vartype used for stash mean/inv_std_varsaved_varMakasarMyanmarFailed to parse num_beams or it is not positive integer scalarFailed to parse num_return_sequences or it is not positive integer scalarFailed to parse max_length or it is not positive integer scalarCannot apply CumSum operator on a scalarScale and Zero-point must be a scalarunsigned charTwo interpolation modes: nearest(default), bilinearcom.microsoft.DequantizeLinearDynamicQuantizeLinearcom.microsoft.QuantizeLinearQLinearMarinvalid string: control character U+000D (CR) must be escaped to \u000D or \rBitwiseOrrqseqscale_grad_by_freqsession.disable_quant_qdq in AddToThreadq_dq_pre_qNumber of attention heads for qFailed to find path for qExpecting a non-empty tokenexpReduceLogSumExpSetupoffset_groupinvalid named capture groupCanCreateNodeGroupsession.enable_quant_qdq_cleanupspdft_bluestein_z_chirpsession.force_spinning_stopfull-stop) is not a registered function/op-inter-op-intra-opThere was a problem acquiring temporary memory allocator in Einsum opAxis tensor must be provided to the CumSum opweight_gather_tempThe id of the first timestampclipFuseReluClipskipHipfpframe_step in stepmin_tokens_to_keepSepmmapmax_mapfound_in_outer_scope_location_mapThere is no location for this node arg in the outer scope location map" in node-stream mapCastMapZipMapSequenceMaponnx.TypeProto.Maptop_pZpCleanUpTpNoOpCreateOpInvokeOpYieldOpVerifyEachNodeIsAssignedToAnEpInvalid library handle %p%I:%M:%S %ptwo autoobjcprotobn_var_tensor_protobn_mean_tensor_protosparse_tensor_protobn_scale_tensor_protoconv_W_tensor_protoconv_B_tensor_protobn_B_tensor_protomul_B_tensor_protoadd_B_tensor_protonullptr != type_protomodel_loading_from_saved_protoonnx_func_protoUnpackTensor: the pre-allocate size does not match the size in proto) in protoonnx.StringStringEntryProtoConstantNodeProtoToTensorProtoSetValueFromTensorProtoGetExtDataFromTensorProtoDeserializeTensorProtoDenseTensorToSparseTensorProtoonnx.SparseTensorProtoSparseTensorProtoToDenseTensorProtoonnx.TensorProtoThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoFailed to get size of TensorProtoInvalid TensorProtoonnx.TrainingInfoProtoonnx.ValueInfoProtoInlineFunctionProtoonnx.FunctionProtoParseOrtConfigJsonInModelProtoParseSessionOptionsFromModelProtoonnx.ModelProtoonnx.GraphProtoonnx.AttributeProtoonnx.TypeProtoonnx.TensorShapeProtoonnx.NodeProtoonnx.OperatorSetIdProtoTotocast_toprocessor_from <= processor_toValue(s) to change tolibonnxruntime_providers_migraphx.solibonnxruntime_providers_cuda_ut.solibonnxruntime_providers_tensorrt.solibonnxruntime_providers_openvino.solibonnxruntime_providers_cann.solibonnxruntime_providers_rocm.solibonnxruntime_providers_dnnl.solibonnxruntime_providers_vitisai.solibonnxruntime_providers_shared.solibonnxruntime_providers_cuda.soallowzeroconst_zerosession.set_denormal_as_zeroInner and Outer indices must either be both zero or non-zeroThe epsilon value to use to avoid division by zeroDivide by zeroWeight zero point must be zeroNonZeroPadInputWithDimValueOfZeroMroHanunooFillSparseTensorCooFormat() == SparseFormat::kCooDenseTensorToSparseCooNkosampling_ratiorhoWanchoBopomofovalue_info expected to have type infoInvalid shape infomodel format error! Need a key for the external data infomodel format error! Need a value for the external data infoInitializeFeedFetchCopyInfoFeedsFetchesInfoSetupSubgraphExecutionInfoCreateGraphInfoPopulateKernelCreateInfoGetExternalDataInfoSoyomboMiaoLaoSoPoNoLoDoCoElement type of tensor or sparse tensor input was unknownKey type of map input was unknownValue type of map input was unknownElement type of optional input was unknownElement type of sequence input was unknownInput shape is unknown or not 2D, or data type unknownMask shape is unknown or not 2D, or data type unknownmodel_runTypeProto must have shape for this to runSunRunJuncarriage-returnUsing transpose optimized pattern.jsonConstructorCommonComputeCommonSVMCommonsemicolonepsilonFloatEpsilonDistributionstd::exceptionAddTensorRTCustomOpDomainToSessionOptionc2p_attentiondisentangled_attentiondecoder subgraph: output_cross_qk could only work with has_decoder_masked_attentionp2c_attentionc2c_attentionGroupQueryAttentionApplyAttentionStart FuseGptAttentionQOrderedLongformerAttentionDecoderAttentionDecoderMaskedSelfAttentionQOrderedAttentionPackedAttentionDecoderMaskedMultiHeadAttentionPackedMultiHeadAttentionQAttentionHidden layer sizes of Q, K, V paths in AttentionPartitionpositionInvalid DataTypeImpl TypeProto definitionconditionadditionreductionaggregate_functionInlineFunctioncache_indirection" not supported, expect zeros, border or reflectionSegmentExtractioncase_change_actionen_normalizationq root should be layer normalizationSkipLayerNormalizationQOrderedLayerNormalizationSkipSimplifiedLayerNormalizationQEmbedLayerNormalizationGroupNormalizationLpNormalizationBatchNormalizationInstanceNormalizationMeanVarianceNormalizationsession_initializationmincharnum is too big for char level tokenezationscore_activationFusedMatMulActivation_FusedActivationtokenize_punctuationequationonnx.TensorAnnotationCastEliminationNoopEliminationCommonSubexpressionEliminationUnsqueezeEliminationPreShapeNodeEliminationExpandEliminationsession.debug_layout_transformationoptimization.enable_gelu_approximationuse_approximationGelu approximationGeluApproximationScalesValidationduplicated locationComputeValueLocationIncompatible dimensions for matrix multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationWhether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationWhether A should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationNegative values are not allowed in a shape specificationGenerated from Matmul BatchNormalization fusionBiasSoftmaxFusionFastGeluFusionBiasGeluFusionQuickGeluFusionBiasDropoutFusionGatherSliceToSplitFusionMatMulIntegerToFloatFusionLabelEncoderFusionAttentionFusionConvActivationFusionGemmActivationFusionMatMulActivationFusionConvAddActivationFusionGemmSumFusionSkipLayerNormFusionSimplifiedLayerNormFusionEmbedLayerNormFusionConvMulFusionDivMulFusionDynamicQuantizeMatMulFusionGemmTransposeFusionMatmulTransposeFusionNotWhereFusionReshapeFusionMatMulScaleFusionGatherToSliceFusionConvAddFusionMatMulAddFusionMatMul_BatchNormalization_FusionConvBNFusionCould not parse model successfully while constructing the inference sessionTokenExpressionNonMaxSuppressionSessionqdq_s8_to_u8_zp_conversion: no conversionir_versionproducer_versionplugin_versionmodel_versionep_sdk_versionsnpe_versionapply softmax to elements for dimensions axis,or all dims along with axis according to op-versiongsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionfirst input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionSecond input tensor has wrong dimensionScatterElements op: input tensor must have at least one dimension in the same dimensionResize: input shape needs to be at least a single dimensionMust have a single dimensionbias shall be 1 dimensiononnx.TensorShapeProto.DimensionunionMonWhich axis to split onWhich axis to concat onHanntf_half_pixel_for_nnLatinAsinMedefaidrinStringJoinMinimum value, under which element is replaced by minCould not find chunk in bindomainValidateOpsetForDomain(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chain_cond_inTinSinReduceMinSoftsignpercent-signplus-signequals-signnumber-signdollar-signless-than-signgreater-than-signinvalid number; expected digit after exponent signEinsum op: Input shapes do not alignSignto.custom_join_thread_fnfloat8e4m3fnExpect to have present state output when past state input is givenNeither 'split' input nor 'num_outputs' attribute has been giveninvalid BOM; must be 0xEF 0xBB 0xBF if giveninput y_zero_point must be a scalar or 1D tensor of size 1 if giveninput x_zero_point must be a scalar or 1D tensor of size 1 if givenMatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenMatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenMatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenBoth 'split' input and 'num_outputs' attribute were givenseven]. It will be overwrittenFlattenorg.pytorch.atenCan't happenshould never happenopen_seqlenmax_seq_lencumulated_seq_lenstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= leneos_tokenbos_tokencls_tokensep_tokenunknown_tokenmask_tokenunk_tokenwhitespace_tokenpad_tokenunknown tokenThe id of the padding tokenThe id of the end-of-sequence tokenGenerateNextTokenhyphenMarchenhiddenprovider.name.size() <= kMaxExecutionProviderNameLendnDuployanAvestanSamaritanTibetanAtanElbasanHatranspanCypro_MinoannanBlackmanBuildExecutionPlanGenerateDeallocationPlanAllocateAsPerAllocationPlanFinalizePlanCreatePlanComputeReusePlanExecuteThePlanShavianOld_PersianOld_HungarianCarianArmenianCaucasian_AlbanianChorasmianMongolianInscriptional_ParthianGeorgianLydianCopyLittleEndianOld_SogdianLycianPhoenicianOld_South_ArabianOld_North_Arabianinput_meanrunning_meansaved_meanbooleansqeuclideanNabataeanManichaeancom.microsoft.QLinearReduceMeanTanJanHaninvalid string: control character U+000A (LF) must be escaped to \u000A or \nTnMnDnmomentumEinsuminput_skip_bias_sumweight_gather_sumembedding_sumlog summincharnumalnumThe type of BFP - must match with the BFPType enumBamumSegmentSumCumSumReduceLogSumReduceSumFused Gemm with SumInvalid inputs given to local_trsmk root is not layer normL2 normL1 normpost_transforminput_gather_element_transformdiscrete_fourier_transformshort_time_fourier_transformRandomUniformCuneiformmap_formStftNormSkipGroupNormpermcustomAhomscale_qkv_gemmscale_values_gemmscale_global_gemmscale_V_gemmscale_QKT_gemmscale_Q_gemmscale_K_gemmFusedGemmMatMulBnFusion_Gemmsignal_ndimscale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimleft_dim == right_dimblock_dimNumber of attention heads. Default value is 0. Must use with rotary_embedding_dimmax_memEither `sizes` or `scales` must be provided, but not both of themAdlamMalayalamOghamSiddhamTai_ThamChambasic_iostreambasic_ostreambasic_istreamencoded_stream!c1->in_use() && !c2->in_use() && c1->stream == c2->streamOptimizeReusePlanForMultiStreamSmLmCol2ImstoulFaild to find path to qkv_matmulHangulComplexMulQLinearMatMulDynamicQuantizeMatMulTransposeMatMulSparseToDenseMatMulFusedMatMulQOrderedMatMulData types of the inputs must match for MatMulcom.microsoft.QLinearMulJultlcntrlApplyImplMoveInputOutputImplRocmBlasAltImplReplaceInitializedTensorImplVerifyEachNodeIsAssignedToAnEpImplPartitionOnnxFormatModelImplPartitionOrtFormatModelImplExecuteGraphImplTensorProtoToOrtValueImplReleaseMLValueImplComputeImplFinalizeSessionStateImplUnpackTensorWithExternalDataImplInlineFunctionsAOTImplFuseSubGraphQKImplstolMaxUnpoolunnamed_thread_poolNumber of top experts to select from expert poolcustom join thread function not set for inter op thread poolcustom join thread function not set for intra op thread poolOnly boolGlobalMaxPoolNhwcMaxPoolGlobalLpPoolMaxRoiPoolcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearGlobalAveragePoolai.onnx.mlstoullEnv is nullAxes input is nullProvided allocator is nullProvided OrtMemoryInfo is nullWaitOnEPStep.wait_handle is null expected to have type but instead is nullOpKernel was nullInput type was nullExecution frame was nullfbs_node_arg_names cannot be nullgraph_proto cannot be nullstollConstantFillGivenTensorFillMaskedFillstd::bad_function_callReceived negative size from stat callAprilTamilround_prefer_ceilCeilpytorch_half_pixelaccuracy_levelgraph_optimization_levelSetGraphOptimizationLevelp_op_kernelKernel found kernelExecuteKernelTryCreateKernelElementWiseKernelEmpty graph proto from deserialization of ORT format model(Optional) Some notes for the modelDid not find session options in the model file to be used while running the modelFound session/run/environment configuration in the model file to be used while running the modelFound tuning results in the model file to be used while loading the modelCreateEpContextModelPartitionOnnxFormatModelPartitionOrtFormatModelSaveModelLessOrEqualGreaterOrEqualStringEqualresidualcom.microsoft.experimentalnumber literalnull literalstring literaltrue literalfalse literalinvalid literal'[', '{', or a literalAllocateRawInternalRegisterOpSchemaInternaloptionalonnx.TypeProto.Optionalunidirectionalis_bidirectionalInput tensor must be 4-dimensionalInput tensor must be 2-dimensionalInput tensor must be 1-dimensionalthe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensionalCanadian_Aboriginalnumber of dimensions comprising the signalRandomNormalv_initialspatialMultinomialglobalZlUlNlLlasteriskThe id of the translate taskThe id of the transcribe taskencoder_attention_maskhas_key_padding_masksqueeze_maskpresence_maskprefix_vocab_maskFailed to find path for maskFaild to match path 4 for unidirectional maskFaild to match path 3 for unidirectional maskFaild to match path 2 for unidirectional maskFaild to match path 1 for unidirectional maskFaild to match the path (Div-->Where-->Add) for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskValidateUnidirMaskMaxpoolWithMaskquestion-markquotation-markexclamation-markoutput_qkdecoder_output_cross_qkmax_dead_bytes_per_chunkyptnkShrinkleft_rank == right_rankinput_rank == reference_rankpads_data.size() == 2 * data_rankAll inputs to Concat must have same rankIndices and updates must have the same rankdim_iter == rankblankMeetei_MayeklseekGreekNGramRepeatBlock`InlinedVector::at(size_type) const` failed bounds check`InlinedVector::at(size_type)` failed bounds checksession.disable_cpu_ep_fallbacklongest_from_backPrePackBatakFailed to find path for past_kFailed to find path for present_kseqlens_kTkSkFailed to find path for kInvalid value for attribute kComplexMulConjPsalter_PahlaviInscriptional_PahlaviMende_KikakuiWarang_CitiGujaratimodel_loading_uriTakriSyloti_NagriNandinagariDevanagariFriVithkuqiApistoiroiMultaniMahajaniBrahmiBernoulliBengaliBhaiksukiKhojkiOl_ChikiKaithiKharoshthiGurmukhiModiMasaram_GondiGunjala_GondiYezidiKhudawadiThaiVaiYiPiKayah_Li(outputs_.size() - 1) == i(inputs_.size() - 1) == iORT Build Info: git-branch=HEAD, git-commit-id=b2481e3602, build type=Release, cmake cxx flags: -ffunction-sections -fdata-sections -flto -pthread -Wno-pthreads-mem-growthdepthSpaceToDepthquery_lengthkey_lengthmax_lengthframe_length == window_lengthbad_array_new_lengthdft_lengthmin_lengthmax_gram_lengthmin_gram_lengthpadding_lengthframe_lengthpast_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthcumulative_sequence_lengthinput_ids and position_ids shall have same sequence_lengthmax_length > sequence_lengthOne sided attention windows length W, or half of total window lengthInput steps has incorrect lengthInput axes has incorrect lengthAttribute axes has incorrect lengthAttribute pads has incorrect lengthAttribute pooled_shape has incorrect lengthStringLengthmax_length <= kMaxSequenceLengthoutput_widthbeam_widthrealpathep.context_file_pathOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathOutput edge count not expected for nodes in gemm pathFaild to match gemm pathFailed to find mask pathFailed to find shape pathFindPathAcoshCoshHardSwishinvalid string: forbidden character after backslashOutput edge count not expected for nodes in past subgraph is not If node's input or previous node output in this subgraphFailed in match input mask subgraphInput of reshape_before_gemm is not the input of subgraphStart MatchPastSubgraphPass MatchPastSubgraphMatchInputToConcatSubgraphStart MatchGemmSubgraphPass MatchGemmSubgraphStart MatchInputMaskSubgraphPass MatchInputMaskSubgraphStart MatchUnidirMaskSubgraphPass MatchUnidirMaskSubgraphInlineIfSubgraphFuse_SubgraphInputs 0 shall be 3 dimensions in whisper graph in graph is marked single but has an empty string in the graphInvalid ``body`` argument. Expected a graphTransformGraphExecuteGraphFuseSubGraphIndexedSubGraph contains values not present in the GraphAsinhSinhscaledtanhAtanhScaledTanhResult buffer is not large enoughhighTifinaghstretchfetchinput count mismatchGatherNDBase PrepareForCompute: Input count mismatchactivation_params count mismatchResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchMatMul dimension mismatchTensor size mismatchsource and destination buffer size mismatchSize mismatchData type mismatchStarts and steps shape mismatchStarts and axes shape mismatchStarts and ends shape mismatchsrc and dst types must matchoutput and sum shape must matchLast dimension of bias and input does not matchLast dimension of beta and input does not matchLast dimension of gamma and input does not matchoutput count does not matchinput count does not matchFirst dimension (num_rois) of batch_indices and rois don't matchAttribute name and type don't 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long longunsigned longPass 1 to enable broadcastingEnable broadcastingSignWriting, enable TunableOp for usingNode::LoadFromOrtFormat, input_arg_counts is missingRequired attribute axis is missingmatching node is missingReceived null affinity_stringdefault_stringvalue_stringdoc_stringbasic_stringVectorToStringLoadFromStringUnknown AutoPadType StringConstantSharingDynamicTimeWarpingearly_stoppingSaveMLValueNameIndexMappingStringMappingSaveInputOutputNamesToNodeMappingsession.inter_op.allow_spinningsession.intra_op.allow_spinningsession.disable_aot_function_inliningai.onnx.trainingai.onnx.preview.trainingString data type is not supported for on-device trainingInvalid number of outputs for BN trainingConcatTrainingHammingenable_opencl_throttlingPresence penalty for custom samplingenable_profilingSetEnableProfilingEndProfilingsession.disable_prepackingbox_codingNewIOBindingConstantFoldingsegment_embeddingposition_embeddingword_embeddingGemmaRotaryEmbeddingWordConvEmbeddingTorchEmbeddingoutput_paddingRemovePaddingRestorePaddingPerformTypeAndShapeInferencingSora_SompengRejangort_configReading the provided model for the ORT config%.8gErfstof__uuidofinfIsInfhalfoffp_kernel_defShould not have entry in kernel create info with nullptr for kernel_defnullptr != func_meta_def%afinvalid string: control character U+000C (FF) must be escaped to \u000C or \fPf%.0Lf%LfIfCfQuantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0f0.0f <= ratio_value && ratio_value < 1.0fThe value to be filled in the attention mask. Default value is -10000.0frepetition_penalty > 0.0fbeta_ > 0.0falpha_ > 0.0fclip_ > 0.f%fblocksizetf_crop_and_resizeCropAndResizevalues_count == index_sizeconv_window_sizelocal_window_sizescores_output_data.size() >= scores_output_sizeparameter_sizecrop_sizeactivation_sizeHidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeInput 'past' expect k_hidden_size == v_hidden_sizemax_ngram_sizeno_repeat_ngram_sizemin_ngram_sizewindow_size <= signal_sizeSpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeposition_ids's first dimension shall be 1 or batch_sizeinput_ids and presence_mask must have the same batch_sizeinput_ids and prefix_vocab_mask must have the same batch_sizeDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizescores_dims[0] == batch_sizeextra_decoder_tensor_dims[0] == batch_sizeroi_batch_id < batch_sizechar_embedding_size(size_t)blob_len == buf_sizesample_sizeimage_sizepooled_sizeDimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizehidden_size != num_heads * head_sizeparameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizetoken_id < vocab_sizedim0_offset < dim0_sizesubgraph past state dimension 2 shall have a positive value for vocabulary sizeInvalid assumption of output element sizeAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute pads has incorrect sizeAttribute kernel_shape has incorrect sizeno repeat ngrams sizeoffsets buffer is not equal to tensor sizeinput array doesn't equal tensor sizeinvalid repetition sizeAttribute pads must have an even size does not match the actual size must be of equal sizecopy shape must have non-negative sizelocation dimensions do not match shape sizeQGemm : zero point and scale of input b should have same shape sizeExpecting the same sizespecify the sizeinput_shape_size == sizeInvalid other(scalers) sizedo_basic_tokenizeInitializeParseScalesDataAndAdjustOutputSize0 == memory_size % kMinAllocationSizeUnsqueezeSqueezeReservenftw_removeResolvedetect_positiveBlocksize must be positiveis_case_sensitivedetect_negativevalue of k must not be negativeexclusiveMeroitic_CursivefiveNumber of top elements to retrieveRunForSavenodes_missing_value_tracks_trueChild node if expression is trueCreating and using per session threadpools since use_per_session_threads_ is trueTwo inputs should have same rank and rank >= 3 if transBatchA or transBatchB is true initializer name is not uniqueUniqueopaqueonnx.TypeProto.Opaqueoutput_mlvaluep_mlvaluepast_valueW_zero_point_data[i] == W_zero_point_valuepresent_valueconstant_valuemask_filter_valueextrapolation_valuep_ml_valuemissing_valuesparse_valuepad_valueiterator does not fit current valuecannot get valueMap is missing type entry for its valueinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueScaling valueattribute mincharnum must have a positive valueTensor shape cannot contain any negative valueMissing/Invalid 'axis' attribute valueMissing/Invalid 'axes' attribute valueInvalid 'pads' attribute valueInvalid 'mode' attribute valueThreshold valueattribute case_change_action has invalid valueconcat_after_gather input 2 does not have expected valueReceived nullptr for OrtValueFindDeviceForValueBatchOrCopyMLValueDistributionEnqueueTueNew_Tai_Lueinvalid string: ill-formed UTF-8 byteUnexpected special state in RunStateOnByteNULL state in RunStateOnByteDeadState in RunStateOnBytecomputePrepareForComputeMultinomialComputeBaseComputePrepareComputeDeviceComputeSequentialExecutor::ExecuteMissing or invalid starts and ends attributek_transpose has not perm attribute model uses the deprecated attributeThe pads attribute cannot be used simultaneously with auto_pad attributeMust have valid 'axis' attributeMissing 'equation' attributeinvalid string: missing closing quoteReluQuantRewriteClipQuantRewriteIsAllFiniteoperator deletehas_layer_stateencoder_session_stateinit_run_decoder_session_stateCreateSubgraphSessionStateFinalizeSessionStatesaturatesample_rateapproximateCreateValidateTraceAllocateRecurseinverseInversereverseFailed to convert dense initializer to sparseRaggedTensorToSparseFillSparseTensorBlockSparseFormat() == SparseFormat::kBlockSparseParseQLinearConvTransposeDoConvTransposeAddBiasTransposeThe rank of the input must match permutation size for TransposeFused Gemm with TransposecloseStringRaggedTensorToDenseMatchPastSubgraph returns falseMatchInputMaskSubgraph returns falseCheckNodesInPathQ returns falseCheckNodesInPathK returns falseBoolean. Whether the identification of stop words in X is case-sensitive. Default is falseChild node if expression is falseUsing global/env threadpools since use_per_session_threads_ is falseM_ == 1 && N_ == 1 was falseInputs 'mask_index' with 4D data shall have is_unidirectional set to falseCheckSliceParameters return falseCheckNodesInPathV return falseInvalid hidden_states expension: has_hidden_state_ == falseplus infinity (if supported by the datatype) or the maximum value of the data type otherwiseminus infinity (if supported by the datatype) or the minimum value of the data type otherwiseminus infinity (if supported by the datatype) or undefined otherwiseBalineseBugineseJavaneseSundaneseSequenceErase__cxa_guard_releasedo_lower_caseignore_casesession.dynamic_block_basenullptr != tensor_type_baseRoiAlignBaseUpsampleBasehardware_architecturetemperaturesize of each output featuresize of each input featureEnsure_fence_beforeunderscore__cxa_guard_acquireQLinearWheresum squareReduceSumSquareZanabazar_Squaredecltypeoutput_dtypeoutput_datatypeInitializer must have a datatypekey_typemask_index_typeblk_quant_typetarget_typesparse_tensor_typeop_typebfp_typeproto.data_type() == proto_typeactivation_typeelem_typekernel_typemodel_typeoptional_typestash_typeopaque_typevalue_typesequence_typedevice_typedata_typeUnsupported input typedata type is different from updates type and specified value's type expected to have tensor typeAttribute expected to have tensor or sparse tensor typeUnknown error typeundefined error type expected to have map typeQuantization typeunknown kernel typeThe type is not tensor, sparse tensor, sequence, map or optional type output to be of optional type input to be of optional type expected to have optional typeSpecified domain and type names combination does not refer to a registered opaque type expected to have tensor or sparse typeSrc and Dst must be of the same typeAll inputs to 'Range' op must be of the same type output to be of single type input to be of single type expected to have sequence type expected to be a sequence typeThis OrtValue is neither Tensor, SparseTensor, Map or Sequence typeInvalid typeUnsupported type has unknown expected type output to be of variadic type input to be of variadic typeencoder subgraph outputs 1, 2, ... shall have same data typeExpecting the same data typeMust have a valid data typeInput data type does not match the expected data typeencoder subgraph output 0 (logits) shall be float or float16 data type::OrtKernelInfo output does not have a type::OrtKernelInfo input does not have a typeexpected a registered ONNX typedecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typedecoder subgraph input 1 (encoder_hidden_states) shall have float or float16 typeencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph input 0 (encoder_input_ids) shall have int32 typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeencoder subgraph input 1 (decoder_input_ids) shall have int32 typedecoder subgraph input 0 (input_ids) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typeencoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typesubgraph input 2 (attention_mask) shall have int32 typeGetElementTypevalue_case == ONNX_NAMESPACE::TypeProto::kTensorTypethisProto->value_case() == TypeProto::ValueCase::kTensorTypethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypevalue_case == ONNX_NAMESPACE::TypeProto::kMapTypethisProto->value_case() == TypeProto::ValueCase::kMapTypethisProto->value_case() == TypeProto::ValueCase::kOptionalTypevalue_case == ONNX_NAMESPACE::TypeProto::kSequenceTypethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeslopeSnpeAddBiasReshapeoutput_shapeinput_as_shapekernel_shapeblock_shapeoutput_type_shapeinput_type_shapeimage_shapepooled_shapeextra_shape!mask || mask->Shape() == X_shapeB_shapeshape && sp_tensor.DenseShape() == *shapeshape && tensor.Shape() == *shapeNew shapeSize of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapevalues in 'axes' are beyond the bounds of the computed output shapeFailed in match v_matmul and v_add input shapetried Filling sparse tensor with negative value in values shapetried Filling sparse tensor with negative value in block sparse indices shapeExpecting COO 2-D indices shapeInvalid bias shapeEinsum op: The candidate output does not match the actual output's shapetried creating tensor with negative value in shapeDilations dimensions should match kernel shapeInput 0 and 1 shall have same shapeInput 0 and 7 (mask) shall have same shapeAll inputs must have the same shapeMust have the same shapeX and mask should have the same shapetwo paths share the same shapeinvalid expand shape should specify a shapeShape must be 1 dimensional as it's tensor data of a shapedata_n.Shape() == shapedata_1.Shape() == shapeInferPadsAndOutputShapeComputePadAndOutputShapeConstantOfShapeSetOutputTypeShapeCheckDistilBertReshapeShapeUpdateTypeAndShapeJunenoneconst_oneimpl_->weighting_criteria_ != kNoneninenewlinelow-linevertical-lineaffineAffinePalmyreneOneonnxruntimegenerated at runtimecustom op registered at runtime_kernel_timeis_onesided and inverse attributes cannot be enabled at the same timeHidden size of Q, K and V shall be sameExecutionFrametruncation_strategy_nameproducer_nameop_namepartition_namesession.optimized_model_external_initializers_file_nameSparse tensor initializers must have a non-empty nameTensor initializers must have a non-empty nameOutput buffer is not large enough for ::OrtKernelInfo output nameOutput buffer is not large enough for ::OrtKernelInfo input nameDuplicate type constraint nameReceived nullptr for nameOutput buffer is not large enough for ::OrtKernelInfo node nameUpsampleGridSampleBraille volatileenable_npu_fast_compilesession.node_partition_config_filevocab_fileTileUnmapFileGot null library handleh != kInvalidChunkHandlelong doubleTreeEnsembleThe broadcasted dimensions of the inputs are incompatibleIsCompatibleThe input is not evenly splittableOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptablebias_tableep.context_enableGraph attribute inferencer for "body" not availableInvalid build with no providers availableEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availableNo requested allocator availablez_scaley_scalex_scalew_scaleinput_scaleweight_scaleheight_scalespatial_scalewidth_scalesegment_embedding_scaleposition_embedding_scaleword_embedding_scalereduced_scaleb_scalebeta_scaledata_scalegamma_scaleY_scaleX_scaleW_scaleR_scaleC_scaleB_scaleA_scalelocaleParseStringWithClassicLocaleFused MatMul and Scale_block_invokeWaitRevokeCastLikeRandomUniformLikeRandomNormalLikeEyeLikeapostrophenew_key_cacheis_kv_cachecos_cachesin_cachenew_value_cacheRunStateOnByteUnlocked failed after ResetCacheShapeInputMergeTensor shape is too largeout_of_rangeinvalid character class rangeinvalid location range in the supported version rangeinput index out of rangeIndex out of rangeNode index is out of rangeiterator out of range: out of rangeRangeDiv and Shape does not have edgeDiv and Shape1 does not have edgeThree modes: constant(default), reflect, edgeFailed to get error messageDynamic linker failed to allocate memory for error messageOsageimagememory.enable_memory_arena_shrinkagethreeTraceFreeCherokeesrc_node || dst_nodeFailed to find Softmax nodeq and v are not from same Split nodek and v are not from same Split node capable of executing this nodekernel already mapped to existing nodenearest_modeexecution_modecoordinate_transformation_modeceil_modetraining_modepadding_modeembed_modeParallel modeSequential modeResize: unexpected modeUpsample: unexpected modeAddCopyNodeCreateReplacementNodeApplyRulesOnNodeConstantFoldIfNodeSetExecutionModetildeexclude_outsideFailed to fetch a stream hosting the requested resourceoutput_sequenceinput_sequenceout_sequence but received a tensor sequenceMisuse of LoopStateVariable. Attempt to move beyond end of sequenceinvalid escape sequenceThe strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceSplitToSequenceConcatFromSequenceIterateSequenceReverseSequenceCleanUpNodeSequenceonnx.TypeProto.Sequencesession.strict_shape_type_inferenceThe session already has a PrePackedWeightsContainer instanceSerialization error. Graph attribute was serialized without Graph instancemax_distanceMax distancenormalize_variancetarget_device) is not supported on this deviceCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceEliminateSliceDynamicSlice'step' cannot be 0 for Sliceright-braceleft-braceTracebackspaceDepthToSpaceglobal_replaceStringRegexReplaceStringECMARegexReplaceTePeMeTai_Lestdmax_input_chars_per_wordforwardPropagateDQForwardfowardPropagateQBackwardstodReduceProdfmodperiod!foundunordered_map::at: key not foundmap::at: key not found' is not foundRegisterCustomOpsUsingFunction: Registration function was not foundTempSpace allocator not foundCPU allocator not foundKernel not foundRoundonly_secondinsert_result.secondp.secondqkv_hidden_sizes first element should be same as the second_condbad mlas activation kindTindExtendoutput == output_endC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endA + (M * K) <= A_endA + (M * lda - (lda - K)) <= A_endsince == endcur + size <= endampersandExpandLogStart must pair with LogEndBitwiseAndiou_thresholdscore_thresholdspecify the dimension to unfold) should not be stored in raw_data fieldgridAffineGridvoidhardsigmoidcom.microsoft.QLinearSigmoidHardSigmoidSparse initializer must have a name. This model is invalidshape is invalidQLinearConv : filter zero point shape invalidQLinearConv : filter scale shape invalidMatmulInteger : b zero point is not validMatmulInteger : B zero point is not validgraph_optimization_level is not validexecution_mode is not validBuhiddecoder_start_token_idno_timestamps_token_ideos_token_idbeginning_timestamp_token_idstart_of_lm_token_idno_speech_token_idtranslate_token_idtranscribe_token_idpad_token_idgpu_graph_idTidnormalizedSession was not initializedsqueezedsession.qdqisint8allowedNegative thread index is not allowed) to UNDEFINED is not allowedNegative ngram_indexes values are not allowedInput dimensions are either [C] or [N][C] allowedInput dimensions are either[C > 0] or [1][C > 0] allowed!sum_input_movedrotary_interleavedWhether C should be broadcastedconst_zero_castedconst_one_castedsortedUpdating cos_cache and sin_cache in RotaryEmbedding is not currently supportedmust be 0, normalization currently not supported4D mask in attention cpu kernel is not supportedX-device copy of strings not supportedNested parallelism not supportedindices tensor data type not supportedOnly 4-D or 5-D tensor is supportedmust be 1, only one sided FFTs supported is not currently registered or supportedOnly tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedDepthToSpace op: only 'DCR' and 'CRD' modes are supportedList of non-negative integers, indicate the dimensions to be inserted is not implementedNot implementedNot ImplementedInheritedloss_unweightedv_reshape initializer value is not expectedgather input 1 value is not expectedGemm weight shape is not expectedGemm bias shape is not expectedshape of layer norm bias tensor not expectedgather axis value not expectedunsqueeze_after_gather axes value not expectedpresent_v_unsqueeze axes value not expectedpresent_k_unsqueeze axes value not expectedGemm bias shape not expectedunidir mask shape not expectedMatrix dimensions are not equal. Square matrix is expectedConversion is expectedSparse indices int64 data size does not match expectedSparse indices int32 data size does not match expected, but was not selectedLength of permutation must match the rank of the input to be permutatedBatch can only be done if all beams have been generated/duplicated_Unusednode_index_info and ort_value_idx_map are out of sync and cannot be usedinput count mismatch, expected 1 input - the tensor to be processedUnsupported indices_format passedis_model_proto_parsedWhether B should be transposedWhether A should be transposedMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredmax_skip_count is requiredNon-empty ngram_counts is requiredNon-empty ngram_indexes is requiredAttribute value for pads is requiredmin_gram_length is requiredmode is requiredOnly ONNX MLDataType can be registeredconst_zero_target_typedreshapedHipPinnedCudaPinnedTensor type is undefined1 == undefinedFormat() == SparseFormat::kUndefinedOne and only one of the 'cats_*' attributes must be defined_sum_transformedThis model does not have any local functions defined. AOT Inlining is not performed'unnamedSimplifyWalker::ShortVisit calledNumCapturesWalker::ShortVisit calledRepetitionWalker::ShortVisit calledCoalesceWalker::ShortVisit calledlengths allocation failedOutput buffer allocation failedstring buffer allocation failedpattern too large - compile failed' faileddynamic linking not enabled not specifiednum_heads must be provided if rotary_embedding_dim is specifiedExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedExpected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedpool_strings must not be empty if specifiedAttribute kernel_shape must be specifiedAttribute pooled_shape must be specifiedmask_mul const input not matchedmask_sub const input not matchedq_reshape const not matchedk_reshape const not matchedq_transpose perm attribute not matchedpast_k_transpose perm attribute not matchedopt_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedq_matmul and q_add shape not matchedk_matmul and k_add shape not matchedseedform-feedCalculateStaticCopyInfoForFeeddiscardedexpandedFound '.' not part of an ellipsis in the output subscript providedFound a '.' not part of an ellipsis in the output subscript providedInput 0 expected but not providednon-empty pool_int64s is required if pool_strings not providedc_shape is required if c_data is provided were providedinputdimensions attribute must be providedonesidedModel was not loadedreducedWedaddcom.microsoft.QLinearAddlambdauto_padcount_include_padUnloadLoad input dimensions insteadreadMust specify input cross_qk_layer_headsubgraph past state dimension 4 shall have a positive value for hidden size per headCenterCropPad\dPdNdUnhandled type: %dcom.ms.internal.nhwccom.microsoft.nchwcFormat() == SparseFormat::kCsrcstd::bad_allocCopyTensorAsyncintra op thread pool must have at least one thread for RunAsyncComputeAsyncGetDeleteFuncmcCopticUgariticGlagoliticasymmetrichalf_pixel_symmetricEthiopicRunicOld_PermicCyrillicOld_ItalicOld_TurkicGothicIf 1 custom sampling logicperiodic" not supported, expect bilinear, nearest or bicubic" not supported, expect linear, nearest or cubicmode_ != 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dataCopyDataParseSizesDataParseScalesDataMakeCsrDataUnpackInitializerDataScatterDataMakeCooDataCopySparseDataMakeBlockSparseDataTangsaSaurashtraDograBFCArenaKatakanaHiraganaThaanacommaactivation_gammaChakmaRegisterOpSchemaRegisterNodeOpSchemaSinhalaGranthaactivation_alphaValue of alphaAlphaLepchaCuda'lambdaSharadaKannadaeco_acubic_coeff_aprob_aPhags_Painit_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14a%aAxis tensor should be of type `int32_t` or `int64_t`Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Supported modes: `constant`(default), `reflect`, `edge`, `wrap`Supported modes: `constant`(default), `reflect`, `edge`(Optional) The size of the quantization block (number of times every scale is replicated). Used only for blocked quantization. The block size is a positive integer. Given `x` shape `(D0, ..., Di, ..., Dn)`, `y_scale` shape `(S0, ... Si, ...Sn)` and `axis=i`, the accepted range is `[ceil(Di/Si), ceil(Di/(Si-1))-1]`If node condition initializer: `0 == center_point_box_ || 1 == center_point_box_When outputing cross qk, number of outputs expected to be first_present_output_index_min_ <= max_unsqueeze_output_InsertedPrecisionFreeCast_column_wise_quant_default_keys_gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_!using_counters_cur_iteration_ < num_iterations_num_broadcasted_indices < num_of_ellipsis_dims_stream_idx < num_streams_beam_idx == num_beams_values_predictions.size() == (size_t)n_targets_or_classes_graph_proto_ is not in sync with name_to_initial_tensor_sparse_tensor_names_ not in sync with name_to_initial_tensor_TransposeOptimizer_encoder_feeds_fetches_manager_init_run_decoder_feeds_fetches_manager_then_feeds_fetches_manager_ && else_feeds_fetches_manager_init_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_decoder_subgraph_.past_present_share_buffer_FuseReluClip_post_layout_transform_step_SequenceMap_feeds_fetches_manager_ && info_decoder_subgraph_.has_decoder_masked_attention_activation_UnsqueezeElimination_iteration_num_ < sequence_len__token_is_spatial_impl_->max_gram_length_ >= impl_->min_gram_length_position_ >= 0 && position_ < sequence_length_p_int < base_int + memory_size_node_offsets_index < node_offsets_size_ort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_offset >= 0 && static_cast(offset) < node_values_size_current <= buffer_size_max_ngram_size_ >= min_ngram_size_!normalize_Unsqueeze_final_output_mlvalue_Create_State_Release_State_type == dtype_onnxruntime::DataTypeImpl::GetType() == type_!is_concrete_shape_standalone_onnxruntime_profile_wait_handle_DoubleQDQRemoved_top_k <= beams_used_enabled_!is_prepacked__inlfunc_basic_weights.quant_para_unk__ConvBnFusion_W_ConvMulFusion_W_MatMulBnFusion_GemmB_ConvAddFusion_B_ConvMulFusion_Mul_B_ConvAddFusion_Add_B_ConvBnFusion_BN_B_operator^[^\x00-\x{10ffff}]virtual void onnxruntime::concurrency::ThreadPoolTempl::RunInParallel(std::function, unsigned int, std::ptrdiff_t) [Environment = onnxruntime::Env]virtual void onnxruntime::concurrency::ThreadPoolTempl::RunInParallelSection(ThreadPoolParallelSection &, std::function, unsigned int, std::ptrdiff_t) [Environment = onnxruntime::Env]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = unsigned short]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned short]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]void onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned short]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = short]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = short]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]const T *onnxruntime::Tensor::Data() const [T = short]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]T *onnxruntime::Tensor::MutableData() [T = short]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = short]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = short]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]auto onnxruntime::BitShift::Compute(OpKernelContext *)::(anonymous class)::operator()(BroadcastHelper &) const [T = unsigned int]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned int]Status onnxruntime::PadImpl(OpKernelContext *, const PadsVector &, const PadsVector &, const Mode &, T) [T = unsigned int]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]void onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned int]onnxruntime::BitShift::BitShift(const OpKernelInfo &) [T = unsigned int]void onnxruntime::core_impl(const Tensor *, const Tensor *, Tensor *, int64_t, concurrency::ThreadPool *) [Tin = int]void onnxruntime::ml::CastInputToFloat(const Tensor &, gsl::span &) [SrcType = int]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = int]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = int]virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = int]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = int]const T *onnxruntime::Tensor::Data() const [T = int]Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(Stream *, const OrtValue &, int, AllocatorPtr, OrtValue &, bool, int) [T = int]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = int]int64_t onnxruntime::GetIndex(size_t, const T *, int64_t) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = int]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = int]void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]T *onnxruntime::Tensor::MutableData() [T = int]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = int]onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = int]void onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputs(const OrtValue &, int, AllocatorPtr, OrtValue &) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = int]virtual Status onnxruntime::contrib::MatMulInteger16::Compute(OpKernelContext *) const [T1 = short, T2 = short, T3 = int]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = const float]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = float, TTo = float]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = std::string, TTo = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = float, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = float, TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = std::string, TValue = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = int, ThresholdType = float, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = int, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = int, ThresholdType = float, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = int, ThresholdType = float, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = int, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = float, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = float, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = float, ThresholdType = float, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = float, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = float, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = float, ThresholdType = float, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = float, ThresholdType = float, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = float, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = long long, ThresholdType = float, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = long long, ThresholdType = float, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = long long, ThresholdType = float, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = long long, ThresholdType = float, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = long long, ThresholdType = float, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(InlinedVector> &, const TreeNodeElement &, gsl::span>) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(InlinedVector> &, OutputType *, int, int64_t *) const [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::compute(OpKernelContext *, const Tensor *, Tensor *, Tensor *) const [InputType = double, ThresholdType = double, OutputType = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(InlinedVector> &, const InlinedVector> &) const [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommonClassifier::Init(const OpKernelInfo &) [InputType = double, ThresholdType = double, OutputType = float]virtual Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(const OpKernelInfo &) [InputType = double, ThresholdType = double, OutputType = float]Status onnxruntime::ml::detail::TreeEnsembleCommon::Init(int, int, int, const std::string &, const std::vector &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [InputType = double, ThresholdType = double, OutputType = float]size_t onnxruntime::ml::detail::TreeEnsembleCommon::AddNodes(const size_t, const InlinedVector &, const InlinedVector &, const InlinedVector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, std::vector &, int64_t, const InlinedVector &) [InputType = double, ThresholdType = double, OutputType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = std::string, TargetType = float]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = float]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = float]typename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = float]Status onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = float, U = float]virtual Status onnxruntime::Det::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::BatchNorm::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::Col2Im::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::RoiPool::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::GridSample::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::LRN::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::Max_6::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::Min_6::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::Mean_6::Compute(OpKernelContext *) const [T = float]virtual Status onnxruntime::Sum_6::Compute(OpKernelContext *) const [T = float]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = float]gsl::span onnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan() const [T = float]const T *onnxruntime::Tensor::Data() const [T = float]Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(Stream *, const OrtValue &, int, AllocatorPtr, OrtValue &, bool, int) [T = float]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = float]T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span, size_t, size_t) [T = float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = float]T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = float]void dlib::blas_bindings::local_trsm(const CBLAS_ORDER, CBLAS_SIDE, CBLAS_UPLO, const CBLAS_TRANSPOSE, const CBLAS_DIAG, long, long, T, const T *, long, T *, long) [T = float]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = float]T onnxruntime::signal::get_scalar_value_from_tensor(const Tensor *) [T = float]Status onnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogits(const OrtValue &, transformers::IGreedySearchState *, transformers::ISamplingState *, transformers::ISequences *, AllocatorPtr &, onnxruntime::concurrency::ThreadPool *, transformers::ILogitsProcessorList *, const transformers::IGenerationParameters *, bool, int, Stream *, const transformers::IConsoleDumper *) [T = float]Status onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogits(const OrtValue &, transformers::IBeamSearchState *, transformers::ISequences *, AllocatorPtr &, onnxruntime::concurrency::ThreadPool *, transformers::ILogitsProcessorList *, transformers::IBeamScorer *, const transformers::IGenerationParameters *, int, Stream *, const transformers::IConsoleDumper *) [T = float]Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(AllocatorPtr, Stream *, const std::vector &, std::vector &, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, transformers::Sequences &, const transformers::IConsoleDumper *) [T = float]void onnxruntime::contrib::DequantizeBlockwiseBnb4(T *, const uint8_t *, const T *, int32_t, int32_t, int32_t, int32_t, onnxruntime::concurrency::ThreadPool *) [T = float]onnxruntime::contrib::BahdanauAttention::BahdanauAttention(AllocatorPtr, const logging::Logger &, int, int, int, int, int, bool, concurrency::ThreadPool *) [T = float]void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, POST_EVAL_TRANSFORM, int, bool, concurrency::ThreadPool *) [T = float]void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = float]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = float]T *onnxruntime::Tensor::MutableData() [T = float]Status onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = float]Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = float]onnxruntime::contrib::CDist::CDist(const OpKernelInfo &) [T = float]onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const OpKernelInfo &) [T = float]onnxruntime::ml::SVMRegressor::SVMRegressor(const OpKernelInfo &) [T = float]onnxruntime::contrib::ImageScaler::ImageScaler(const OpKernelInfo &) [T = float]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = float]onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = float]onnxruntime::contrib::GroupQueryAttention::GroupQueryAttention(const OpKernelInfo &) [T = float]onnxruntime::contrib::MultiHeadAttention::MultiHeadAttention(const OpKernelInfo &) [T = float]onnxruntime::LpNorm::LpNorm(const OpKernelInfo &) [T = float]onnxruntime::BatchNorm::BatchNorm(const OpKernelInfo &) [T = float]onnxruntime::InstanceNorm::InstanceNorm(const OpKernelInfo &) [T = float]onnxruntime::contrib::FusedGemm::FusedGemm(const OpKernelInfo &) [T = float]onnxruntime::Col2Im::Col2Im(const OpKernelInfo &) [T = float]onnxruntime::RoiPool::RoiPool(const OpKernelInfo &) [T = float]onnxruntime::contrib::RotaryEmbedding::RotaryEmbedding(const OpKernelInfo &) [T = float]onnxruntime::contrib::CropAndResize::CropAndResize(const OpKernelInfo &) [T = float]onnxruntime::clip_internal::Clip_6Base::Clip_6Base(const OpKernelInfo &) [T = float]onnxruntime::contrib::Affine::Affine(const OpKernelInfo &) [T = float]onnxruntime::GridSample::GridSample(const OpKernelInfo &) [T = float]onnxruntime::contrib::Scale::Scale(const OpKernelInfo &) [T = float]onnxruntime::LRN::LRN(const OpKernelInfo &) [T = float]onnxruntime::RNN::RNN(const OpKernelInfo &) [T = float]onnxruntime::LpPoolV18::LpPoolV18(const OpKernelInfo &) [T = float]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = float]Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const Tensor *, const OrtValue *, int, AllocatorPtr, OrtValue &, OrtValue &) [T = float]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = float]virtual void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span &, const gsl::span &) [T = float]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = float]void onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span &, gsl::span &) [T = float]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = float]Status onnxruntime::ml::GetVectorAttrsOrDefault(const OpKernelInfo &, const std::string &, onnx::TensorProto_DataType, std::vector &) [TH = float]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const Tensor *) [T2 = float]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = float, T2 = float]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = double, T2 = float]void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret &) const [Ret = onnxruntime::common::Status]Pads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]const T &onnxruntime::OpKernelContext::RequiredInput(int) const [T = onnxruntime::Tensor][ONNXRuntimeError]void onnxruntime::BroadcastLooper(TBroadcastHelper &, const ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::contrib::(anonymous namespace)::QLinearBroadcastHelper]void onnxruntime::BroadcastLooper(TBroadcastHelper &, const ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::BroadcastHelper]Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(OpKernelContext *) const [T8Bits = unsigned char]virtual Status onnxruntime::contrib::NhwcMaxPool::Compute(OpKernelContext *) const [T8Bits = unsigned char]static std::vector onnxruntime::QLinearConv::ComputeOutputScale(OpKernelContext *, int64_t) [ActType = unsigned char]static void onnxruntime::QLinearConv::ComputeOffset(OpKernelContext *, int64_t, ActType &, ActType &, uint8_t &) [ActType = unsigned char]virtual Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector &, int, bool &) [ActType = unsigned char]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = unsigned char]virtual Status onnxruntime::DynamicQuantizeLinear::Compute(OpKernelContext *) const [T = unsigned char]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = unsigned char]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]auto onnxruntime::BitShift::Compute(OpKernelContext *)::(anonymous class)::operator()(BroadcastHelper &) const [T = unsigned char]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned char]Status onnxruntime::PadImpl(OpKernelContext *, const PadsVector &, const PadsVector &, const Mode &, T) [T = unsigned char]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]void onnxruntime::contrib::(anonymous namespace)::QLinearImpl(OpKernelContext &, double, const ProcessBroadcastSpanFuncs &) [T = unsigned char]void onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned char]void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const Tensor *, const Tensor *, const Tensor *, const Tensor *, const LookupTableArrayTransformer &) [T = unsigned char]onnxruntime::BitShift::BitShift(const OpKernelInfo &) [T = unsigned char]Status onnxruntime::contrib::QLinearAveragePool::ComputeImpl(OpKernelContext *) const [T8Bits = signed char]virtual Status onnxruntime::contrib::NhwcMaxPool::Compute(OpKernelContext *) const [T8Bits = signed char]static std::vector onnxruntime::QLinearConv::ComputeOutputScale(OpKernelContext *, int64_t) [ActType = signed char]static void onnxruntime::QLinearConv::ComputeOffset(OpKernelContext *, int64_t, ActType &, ActType &, uint8_t &) [ActType = signed char]virtual Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector &, int, bool &) [ActType = signed char]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = signed char]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]const T *onnxruntime::Tensor::Data() const [T = signed char]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]T *onnxruntime::Tensor::MutableData() [T = signed char]void onnxruntime::contrib::(anonymous namespace)::QLinearImpl(OpKernelContext &, double, const ProcessBroadcastSpanFuncs &) [T = signed char]void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const Tensor *, const Tensor *, const Tensor *, const Tensor *, const LookupTableArrayTransformer &) [T = signed char]pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]const T *onnxruntime::Tensor::Data() const [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]T *onnxruntime::Tensor::MutableData() [T = bool]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]const T *onnxruntime::Tensor::Data() const [T = unsigned long long]auto onnxruntime::BitShift::Compute(OpKernelContext *)::(anonymous class)::operator()(BroadcastHelper &) const [T = unsigned long long]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = unsigned long long]Status onnxruntime::PadImpl(OpKernelContext *, const PadsVector &, const PadsVector &, const Mode &, T) [T = unsigned long long]bool onnxruntime::TypedDoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const uint8_t *, uint8_t *) [T = unsigned long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]void onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = unsigned long long]onnxruntime::BitShift::BitShift(const OpKernelInfo &) [T = unsigned long long]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = float, TTo = long long]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = std::string, TTo = long long]void onnxruntime::core_impl(const Tensor *, const Tensor *, Tensor *, int64_t, concurrency::ThreadPool *) [Tin = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = long long, TValue = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = std::string, TValue = long long]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = double, TValue = long long]OrtStatus *OrtCreateValueImplMapHelper(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = std::string, TargetType = long long]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = long long]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = long long]void onnxruntime::ml::CastInputToFloat(const Tensor &, gsl::span &) [SrcType = long long]virtual Status onnxruntime::NonZero::Compute(OpKernelContext *) const [T = long long]virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = long long]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]const T *onnxruntime::Tensor::Data() const [T = long long]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = long long]int64_t onnxruntime::GetIndex(size_t, const T *, int64_t) [T = long long]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = long long]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = long long]T onnxruntime::signal::get_scalar_value_from_tensor(const Tensor *) [T = long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]T *onnxruntime::Tensor::MutableData() [T = long long]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = long long]onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = long long]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = long long]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = long long]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = long long]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = long long]T onnxruntime::OpNodeProtoHelper::GetAttr(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext, T = long long]T onnxruntime::ParseStringWithClassicLocale(std::string_view) [T = unsigned long]T onnxruntime::bit_reverse(T, unsigned int) [T = unsigned long]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = float, TTo = std::string]Status onnxruntime::ml::CastMap::ComputeImpl(OpKernelContext &, TTo) const [TFrom = std::string, TTo = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = float, TValue = std::string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = float, TValue = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = std::string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = long long, TValue = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = std::string]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const OpKernelInfo &) [TKey = std::string, TValue = std::string]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = double, TValue = std::string]OrtStatus *OrtCreateValueImplMapHelper(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = long long, TargetType = std::string]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = std::string]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = std::string]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::string]const T *onnxruntime::Tensor::Data() const [T = std::string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::string]T *onnxruntime::Tensor::MutableData() [T = std::string]void onnxruntime::StridedCopy(concurrency::ThreadPool *, T *, const TensorShapeVector &, const TensorShape &, const T *, const TensorShapeVector &) [T = std::string]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = std::string]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = std::string]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = std::string]T onnxruntime::OpNodeProtoHelper::GetAttr(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext, T = std::string]void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField &, const google::protobuf::RepeatedPtrField &) [isOutput = true]virtual Status onnxruntime::IdentityOp::Compute(OpKernelContext *) const [is_dropout = true]onnxruntime::ReduceKernelBase::ReduceKernelBase(const OpKernelInfo &, optional) [allow_multi_axes = true]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = float, simplified = true]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = double, simplified = true]static OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = const OrtValue]typename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = const OrtValue]static OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = OrtValue]typename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = OrtValue]void onnxruntime::function_utils::Inliner::bind(google::protobuf::RepeatedPtrField &, const google::protobuf::RepeatedPtrField &) [isOutput = false]virtual Status onnxruntime::IdentityOp::Compute(OpKernelContext *) const [is_dropout = false]onnxruntime::ReduceKernelBase::ReduceKernelBase(const OpKernelInfo &, optional) [allow_multi_axes = false]virtual Status onnxruntime::contrib::BiasGelu::Compute(OpKernelContext *) const [T = float, use_approximation = false]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = float, simplified = false]onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const OpKernelInfo &) [T = double, simplified = false]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = long long, TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = std::string, TValue = double]onnxruntime::ml::LabelEncoder_4::LabelEncoder_4(const OpKernelInfo &) [TKey = double, TValue = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = long long, TargetType = double]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const OpKernelInfo &) [AttrType = std::string, TargetType = double]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = long long, ValueType = double]OrtStatus *OrtCreateMapMLValue(const Tensor &, const Tensor &, OrtValue **) [KeyType = std::string, ValueType = double]typename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = double]void onnxruntime::ml::CastInputToFloat(const Tensor &, gsl::span &) [SrcType = double]Status onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = double, U = double]virtual Status onnxruntime::CumSum::Compute(OpKernelContext *) const [T = double]virtual Status onnxruntime::BatchNorm::Compute(OpKernelContext *) const [T = double]virtual Status onnxruntime::GridSample::Compute(OpKernelContext *) const [T = double]virtual Status onnxruntime::Sum_6::Compute(OpKernelContext *) const [T = double]void onnxruntime::Clip::ComputeImpl::operator()(const Tensor *, const Tensor *, const Tensor *, Tensor *, concurrency::ThreadPool *) const [T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]const T *onnxruntime::Tensor::Data() const [T = double]Status onnxruntime::CreateMelWeightMatrix::operator()(OpKernelContext *, int64_t, int64_t, int64_t, float, float) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const Tensor &, const TensorShape &, const Tensor &, const TensorShape &, const gsl::span &, bool) [T = double]void onnxruntime::WritableSliceIterator::Init(gsl::span, gsl::span, gsl::span) [T = double]void onnxruntime::GemmBroadcastBias(ptrdiff_t, ptrdiff_t, T, const T *, const TensorShape *, T *) [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]T *onnxruntime::Tensor::MutableData() [T = double]onnxruntime::contrib::CDist::CDist(const OpKernelInfo &) [T = double]onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const OpKernelInfo &) [T = double]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const OpKernelInfo &) [T = double]onnxruntime::ml::ScalerOp::ScalerOp(const OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const OpKernelInfo &) [T = double]onnxruntime::LpNorm::LpNorm(const OpKernelInfo &) [T = double]onnxruntime::BatchNorm::BatchNorm(const OpKernelInfo &) [T = double]onnxruntime::GridSample::GridSample(const OpKernelInfo &) [T = double]std::vector onnxruntime::ml::GetAttribute(const OpKernelInfo &, const std::string &, const std::string &) [T = double]T onnxruntime::ml::GetDefault(const OpKernelInfo &, const std::string &, const T &) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const Tensor &, const gsl::span &) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const Tensor &, const gsl::span &, const Tensor &, const gsl::span &, AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = double]Status onnxruntime::ml::GetVectorAttrsOrDefault(const OpKernelInfo &, const std::string &, onnx::TensorProto_DataType, std::vector &) [TH = double]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const Tensor *) [T2 = double]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = float, T2 = double]virtual Status onnxruntime::Dropout::Compute(OpKernelContext *) const [T1 = double, T2 = double]void onnxruntime::mod_internal::CallModImpl::operator()(bool, OpKernelContext *) const [T = float, Enable = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, OpKernelContext *) const [T = double, Enable = void] [truncated]operator new[]operator[]operator delete[]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FNUZ]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FNUZ]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E4M3FNUZ]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E4M3FNUZ]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2FNUZ]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2FNUZ]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E5M2FNUZ]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E5M2FNUZ]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FN]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E4M3FN]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E4M3FN]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E4M3FN]The 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]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSum]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Neg]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL1]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]Status onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = float, U = std::complex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Selu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::ThresholdedRelu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::ThresholdedRelu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Celu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::ParametricSoftplus]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = gsl::details::span_iterator, TSpanBIter = gsl::details::span_iterator, TSpanCIter = gsl::details::span_iterator]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = gsl::details::span_iterator, TSpanBIter = gsl::details::span_iterator, TSpanCIter = gsl::details::span_iterator]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Floor]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Exp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMin]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Softsign]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSum]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Ceil]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::ScaledTanh]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::ScaledTanh]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Tanh]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Log]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Neg]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorProd]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL1]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = int, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = float, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = long long, ThresholdType = float, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMax]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorClassifier]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorMin]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorSum]void onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAgg(concurrency::ThreadPool *, const Tensor *, Tensor *, Tensor *, const AGG &) const [InputType = double, ThresholdType = double, OutputType = float, AGG = onnxruntime::ml::detail::TreeAggregatorAverage]static void onnxruntime::IAllocator::ValidateAllocator(const T &) [T = std::shared_ptr]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Neg]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSum]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Neg]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorProd]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL2]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorL1]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool *, Tensor &, std::ptrdiff_t, const TensorShapeVector &, const TensorShape &, const Tensor &, std::ptrdiff_t, const TensorShapeVector &) [EnabledDataTypes = onnxruntime::TypeList]onnxruntime::Scatter>::Scatter(const OpKernelInfo &) [EnabledDataTypes = onnxruntime::TypeList]Status onnxruntime::DispatchStridedCopy(concurrency::ThreadPool *, Tensor &, std::ptrdiff_t, const TensorShapeVector &, const TensorShape &, const Tensor &, std::ptrdiff_t, const TensorShapeVector &) [EnabledDataTypes = onnxruntime::TypeList]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]Status onnxruntime::short_time_fourier_transform(OpKernelContext *, bool, bool) [T = double, U = std::complex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMax]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMax]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Floor]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorLogSumExp]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Exp]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorArgMin]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorMean]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSum]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Ceil]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Tanh]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Log]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel>::ElementWiseKernel(const OpKernelInfo &) [F = onnxruntime::functors::Neg]bool onnxruntime::check_and_reduce_empty_set_input(OpKernelContext *, const gsl::span, bool) [AGG = onnxruntime::ReduceAggregatorSumSquare]virtual Status onnxruntime::ElementWiseKernel>::Compute(OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::map]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>::check(const Cont &, size_t) [T = std::map]const T &OrtValue::Get() const [T = std::vector>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>>::check(const Cont &, size_t) [T = std::vector>]T *OrtValue::GetMutable() [T = std::vector>]const T &OrtValue::Get() const [T = std::vector>]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType>>::check(const Cont &, size_t) [T = std::vector>]T *OrtValue::GetMutable() [T = std::vector>]size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::string, ForwardIter = std::__wrap_iter *>, Map = absl::flat_hash_map, std::unique_ptr>, std::hash, std::equal_to>]size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = long long, ForwardIter = gsl::details::span_iterator, Map = onnxruntime::InlinedHashMap>>]void onnxruntime::ConstantOfShapeBase>::SetValueFromTensorProto(const onnx::TensorProto &) [EnabledOutputTypeList = onnxruntime::TypeList]onnxruntime::ConstantOfShapeBase>::ConstantOfShapeBase(const OpKernelInfo &) [EnabledOutputTypeList = onnxruntime::TypeList]const T *onnxruntime::ConstPointerContainer>::at(size_t) const [Container = std::vector][:^print:][:print:][:^xdigit:][:xdigit:][:^digit:][:digit:][:^punct:][:punct:]The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:][:^lower:][:lower:][:^upper:][:upper:][:^alnum:][:alnum:][:^cntrl:][:cntrl:][:^blank:][:blank:][:^ascii:][:ascii:][:^graph:][:graph:][:^space:][:space:][:^word:][:word:][:^alpha:][:alpha:]virtual Status onnxruntime::Scan<9>::Compute(OpKernelContext *) const [OpSet = 9]void onnxruntime::Scan<9>::Init(const OpKernelInfo &) [OpSet = 9]virtual Status onnxruntime::Scan<9>::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &) [OpSet = 9]virtual Status onnxruntime::Scan<8>::Compute(OpKernelContext *) const [OpSet = 8]void onnxruntime::Scan<8>::Init(const OpKernelInfo &) [OpSet = 8]virtual Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &) [OpSet = 8]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]Status onnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBuffer(Stream *, const OrtValue &, int, AllocatorPtr, OrtValue &, bool, int) [T = onnxruntime::MLFloat16]Status onnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeeds(AllocatorPtr, Stream *, const std::vector &, std::vector &, int, gsl::span, gsl::span, gsl::span, int, int, int, bool, int, int, bool, bool, transformers::Sequences &, const transformers::IConsoleDumper *) [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]Status onnxruntime::contrib::transformers::BeamSearchWhisper::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = onnxruntime::MLFloat16]Status onnxruntime::contrib::transformers::BeamSearchT5::Execute(const FeedsFetchesManager &, const FeedsFetchesManager &) [T = onnxruntime::MLFloat16]Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputs(const Tensor *, const OrtValue *, int, AllocatorPtr, OrtValue &, OrtValue &) [T = onnxruntime::MLFloat16]void onnxruntime::contrib::transformers::BeamHypotheses::Output(int, int, gsl::span &, gsl::span &) [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]virtual Status onnxruntime::DequantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2]virtual Status onnxruntime::QuantizeLinear::Compute(OpKernelContext *) const [T = onnxruntime::Float8E5M2]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::Float8E5M2]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::Float8E5M2]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]axis must be in [-r, r-1]'axis' must be in [-rank(indices), rank(indices)-1]input_dims[rank - 2] == input_dims[rank - 1]'axis' must be in [-rank(indices)-1, rank(indices)]rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]missing ].\\.\[]^-\(){}[]*+?|.^$\invalid substitution \trailing \ row[ rows[] != number of classlabels[pixel vector[Input features_per_batch[Invalid Y argument: index is out of range: Y[Invalid value of attribute 'axis'. Accepted range=[OrtMemoryInfo:[nearest_mode:[coordinate_transform_mode:[Device:[{"bytes":[Config with key [k argument [Invalid start/ending offset [Cannot find NodeArgs for [) index value at position [The ORT format model version [ All nodes placed on [ Node(s) placed on ['axis' must be in [keep_aspect_ratio of [] should not be greater than specified axis dim value [] already exists with value [] op_type [We do not support type [Broadcast Output range [Axis must be within range [ must be within the inclusive range [. Expected range [LoadNodeArgsFromOrtFormat: Node [", "block_size": [RE2: invalid startpos, endpos pair. [sZ____ZTYZFloat8E4M3FNUZFloat8E5M2FNUZscaleYorder_Yscale_YTYALLOW_RELEASED_ONNX_OPSET_ONLY%a %b %d %H:%M:%S %Yorder_Xscale_XPOSIXECREATE_MUTEXSlice ends is less than INT_MAXSOFTMAX\WQWMWAWStart CheckNodesInPathVPass CheckNodesInPathVTVGRUOpenVINO_GPUPacked KV not implemented for CPUPacked QKV of shape (B, L, N, 3, H) not implemented for CPUUnable to convert strings tensor to a sparse tensor that not on CPUUnable to convert strings tensor to a sparse tensor that is not on CPUActivation after group normalization: 0 for None, 1 for SiLUformat_data_.size() == 2Uformat_data_.size() == 1UfpTETIMEOUT$TTESTRING_CASTDisentangledAttention_TRTPyramidROIAlign_TRTMultilevelCropAndResize_TRTEfficientNMS_TRTORTEBROKEN_ASSERTInlineFunctionsAOTINTINVALID_ARGUMENTMEMORY_EFFICIENTGMTBRANCH_LTDEFAULTRIGHTBRANCH_GTSTFTDFTNOT_SETauto_pad == AutoPadType::NOTSETauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETESOCKETTO_FLOAT$T\SINTSFLOATSSUCCESSSPARSE_TENSORSEINCOMPATIBLE_OPTIONSJSGRAPHSETOO_MANY_ARGSETOO_FEW_ARGSSTRINGSEMULTIPLE_OCCURANCES%Y-%m-%d_%H-%M-%STREMONITORSPARSE_TENSORENGINE_ERRORGENERAL ERRORSAME_LOWERSAME_UPPERELISTENERECREATE_SIGNALERECONFIG_READERDCRLINEARrQStart CheckNodesInPathQPass CheckNodesInPathQBRANCH_EQBRANCH_LEQdropDQdropSplitQDQ2DQ1DQ*DQsPDequantizeBFPQuantizeBFPGetCapabilityForEPSOFTMAX_ZEROOpenVINOINFODOsrNIsNaN is NaN_GLOBAL__NUNKNOWNLRNEINVALID_OPTIONEMISSING_REQUIRED_OPTIONRUNTIME_EXCEPTIONECONNECTIONRNNQNNWEBNNMINFloat8E4M3FNTraining mode only supports spatial BNNAN$NQGemm : 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 Nlda >= K && ldb >= K && ldc >= Ngrid_dims[0] == NSUMAttnLSTMDynamicQuantizeLSTMORTMPMAMfL%LaLNULDMLMatchUnidirMaskSubgraph returns NULLWalk NULLkernel def can't be NULLSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_ALLLC_ALLEP_FAILNO_MODELORT_LOAD_CONFIG_FROM_MODELEFATALFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALStart CheckNodesInPathKPass CheckNodesInPathKFuseSubGraphQKXNNPACKJUa9enable_ifIEGUIINVALID_GRAPHEINVALID_OPTION_ARGentry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRINGECAST_TO_STRINGWARNINGLANGINVALID_PROTOBUFTFINFinvalid 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+DBFFVIWEF is unrecognized, acceptable values are TF,IDF,TFIDFRBFinvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: control character U+000F (SI) must be escaped to \u000FMoEERESOLVEEIMAGE_SAVEBRANCH_GTEEDIR_CREATEEPORT_IN_USEEOPTION_PARSEmax_map must be > 0 if map_form is SPARSE Expected DENSE or SPARSEDENSEAZURESNPEOENONENO_SUCHFILEAVERAGEb1Einvalid string: control character U+001E (RS) must be escaped to \u001Eb0Einvalid string: control character U+000E (SO) must be escaped to \u000E\DProcessed_STDCRDScatterNDVALIDNOT_IMPLEMENTEDPRIORITY_BASEDONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDEUNSPECIFIEDSetting graph_optimization_level to ORT_ENABLE_EXTENDEDMODEL_LOADEDX_LogSM_NCDEIMAGE_LOADECREATE_THREADETHREADShape3DInput 'key_padding_mask' shape shall be 1D, 2D, or 3DComputeMeanVarianceNormalization2DInput's shape should be 1D or 2Dinvalid string: control character U+001D (GS) must be escaped to \u001DAxis tensor should be 0D or 1DInput tensor to Unique op should be 1DInput's shape must be 4-Dscale_C\CTCDLCSetting graph_optimization_level to ORT_ENABLE_BASICDCinvalid string: control character U+001C (FS) must be escaped to \u001CcublasLt order of matrix Y and optional matrix CThe number of groups of channels. It should be a divisor of the number of channels CtransBtransBatchBscaleBorder_BLinear_Bscale_B\BTBRGBinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+000B (VT) must be escaped to \u000B%BcublasLt order of matrix BtransAtransBatchAscaleAorder_ALinear_Ascale_ATAinvalid string: control character U+001A (SUB) must be escaped to \u001A%AInput scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @Wrong input type encountered for zero point input def @Input scale is not float for input def @operator?. Do you have duplicated calls to SessionState::AddInitializedTensor function?<|startoftext|><|endoftext|>operator>, std::allocator Expected std::map or std::mapoperator>>operator<=>operator-> expected to have rank >operator|=row_index=Invalid TuningResults (index=Failed to load TuningResults (index=Tried to allocate without valid type information, ort_value index=TraceAllocation for ort_value_idx=TraceFree for ort_value_idx=indices element out of data bounds, idx=, max= Input=Type mismatch. Current= NumOutputs= at pos=Input shape had more than 2 dimension. Dims=Cannot split using values in 'split' attribute. Axis= sum of split values=Failed to find a free memory block despite calling Extend. rounded_bytes=operator= Tensor= [seqno=Indices vs updates dimensions differs at position= Dimension= not in range [min= then= is greater than input dim=Indices dim=. Input rank=Indices must have the same rank as Input. Indices rank=Invalid value of attribute 'axis'. Rank= - i=Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Bad args: nsubmatch=key type mismatch from MapProto. existing=type case mismatch. existing=type case unsupported. existing= v_hidden_size= q_hidden_size= k_hidden_size=. batch_size=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Transpose of element size not supported in this build. Size=Memory allocation failed. Size=], Value= else=Unsupported Source/Target type=Input cannot be split evenly on selected axis. Input shape=. shape=This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= is out of range=Input Sequence and Tensor are expected to have the same elem type. Sequence= it.second=Mismatch between number of inferred and declared dimensions. inferred=type case unsupported for symbolic shape inference. inferred=Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred=Mismatch between inferred and declared type. Inferred= declared= Declared=operator^=bias_dims[0]=. N=, D=operator>=operator>>=) >=operator==operator<=operator<<=operator/=operator-=operator+=operator*=starts.size()=extents.size()=steps.size()=dims.size()=operator&=operator%=operator!=operator<template<id<?P<operator<<.<"<;Error parsing function body: profile_file_prefix:Slice parameter is not expected. Input index: is not the same as this node's index:duplicated ort_value index:SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:. Index: output idx: Max:The inner-most 2 dimensions must have the same size (mat_w:Invalid scan input:Using an input in multiple nodes on different devices is not supported currently. Input:Attempting to get index by a name which does not exist:expect >=6 outputs, got:expect 3 inputs, got:expect 2 inputs, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers, got:number of outputs expected to be 2 + 4 * layers, got: + 3 * layers, got:number of outputs expected to be 1 + 2 * layers, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:, aw_attn_size}. Got:sequence_lens shape must be {batch_size}. Got:Validating no unexpected access using an invalid node_index. Got:Invalid input shape. Only N can be zero. Got:Expecting to have at lest 3-D shape. Got:Invalid SparseTensor indices. Should be rank 0 or 1. Got:, am_attn_size}, Got: right: left: ExplicitInputs: ImplicitInputs:Input string contains invalid utf8 chars:Warning: Model contains experimental ops: max_num_graph_transformation_steps:Input 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes: (actual) rounded_bytes: use_per_session_threads:hidden_size should be divisible by num_heads:Cannot replace concat node with initializer: execution_order: enable_mem_pattern: SinceVersion: with domain:Schema was not found for fused node. Domain:. bin_num: inter_op_param: intra_op_param: session_log_verbosity_level: session_log_severity_level: graph_optimization_level:}. Actual:Input X must have 3 dimensions only. Actual:} for per-channel quantization. Actual:[abi:) for tensor of length: optimized_model_filepath:Input shape dimensions mismatch:Size mismatch: != mat_h:Graph ctor should have created NodeArg for initializer. Missing: thread_pool_allow_spinning: enable_profiling: [enable_if:) are not at boundary of span with size:GatherElements op: Unsupported tensor type, size:Create state function failed. Return value:Expected value: use_deterministic_compute: Attribute: enable_mem_reuse:' op_type:Mismatched map tensor key type:Invalid input type:Mismatched sparse tensor element type:Mismatched tensor element type:Unsupported type:Mismatched type: MemoryType: OrtAllocatorType:' OpType: OrtMemType:DeviceType:output_shape is smaller than minimum required. output_shape:Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: inferred output shape:The input tensor must not have any dynamic (-1) dimensions. Input shape:The input tensor cannot be reshaped to the requested shape. Input shape:Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Cannot use 'edge' mode to pad dimension with a value of 0. Input shape: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 should be scalar like tensor, yet got shape:OrtValue shape verification failed. Current shape:, requested shape: Requested shape:. Shape:Invalid output name:Initializer with same name exists. Name:Error unexpected extra input in node:Error parsing node: execution_mode:Unexpected mode:Node:Mismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Input not found: inputs. Found: session_logid: id:Scores output is incorrect size. Expected: failed:Failed since multiple edges matched: DeviceId: enable_cpu_mem_arena:Make the EP: N:(?:std::],"subtype":"ts" :"dur" :"tid" :"pid" :0123456789invalid string: control character U+0019 (EM) must be escaped to \u0019weight_zp_s8_2_u8uint8GemmFloat8init_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8T8complex128unsigned __int128__float128decimal128Config key is empty or longer than maximum length 128invalid string: control character U+0018 (CAN) must be escaped to \u0018en_US.UTF-8C.UTF-8invalid UTF-8invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0007 (BEL) must be escaped to \u0007Input scale is not float for quantized output z @ 7uint16bfloat16MatMulInteger16invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0006 (ACK) must be escaped to \u0006model type: 0 for GPT-2; 1 for encoder decoder like T5invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0005 (ENQ) must be escaped to \u0005Value of beta default to 0.5Input scale is not float for quantized input y @ 5R->Shape()[1] == 5input_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5uint4all_tensor_types_ir4MatMulBnb4T4MatMulFpQ4quant_type == FP4 || quant_type == NF4quant_type_ == FP4 || quant_type_ == NF4complex64uint64default_int64expanded_target_int64replaced_value_int64Input is ether string UTF-8 or int32/int64Input data type is not int32 or int64Constrain indice type to int32 or int64Output type must be int32 or int64Invalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64Constrain index tensor to int64Input of string must have output of int64'Repeats' input must be 1D tensor of type int64'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64decimal64 Expected TO_FLOAT, TO_STRING or TO_INT641024invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0004 (EOT) must be escaped to \u0004nbits_ == 4X->Shape().NumDimensions() == 4mp3MurmurHash3feature_map_3T3EGENERAL_TYPE3ComputeImplOpset13invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0003 (ETX) must be escaped to \u0003AffineGrid : Input theta tensor dimension is not 3First input tensor must have rank 3Softmax attribute axis is expected to be 3logits_shape.NumDimensions() == 3input_features_shape.NumDimensions() == 3dims.size() == 3new_gemm_input_defs.size() == 3range_input_defs.size() == 3fast_shape.size() == 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3static_cast(activation_func_names.size()) == num_directions_ * 3activation_func_names.size() == static_cast(num_directions_) * 3output2Axes2float8e5m2CheckSliceParameters return false for slice2LabelEncoder_2feature_map_2T2Float8E5M2ReduceL2EGENERAL_TYPE2uint32Failed to convert mask to int32Cast Input from int64 to int32Cast mask from int64 to int32ConvertMaskToInt32Mask_Int32Mask data type is not int32 or int64 or float32Constrain scales to float32Constrain bias and scales to float32(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 float32decimal32EUTF8_TO_UTF32invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0002 (STX) must be escaped to \u0002Value of alpha default to 0.2Input tensor should have a rank of at least 2EyeLike : Input tensor dimension is not 2Input tensor must have rank 1 or 2Input tensor must have rank 2First input does not have rank 2Second input does not have rank 2Failed to find reshape shape path 2subgraph past state dimension 0 shall have length of 2Inputs 'past' dimension 0 shall have length of 2For map type num_values MUST be 2Input scale is not float for quantized input x @ 2'input' must have rank >= 2input_dims.size() >= 2q_or_dq_input_defs.size() >= 2signal_components == 1 || signal_components == 2p_ == 1 || p_ == 2Indices shape must have dim[0] == 2right.NumDimensions() == 2original_decoder_input_ids_shape.NumDimensions() == 2b_shape.NumDimensions() == 2div_inputs.size() == 2mul_inputs.size() == 2predictions.size() == 2scores_dims.size() == 2input_ids_dims.size() == 2extra_decoder_tensor_dims.size() == 2decoder_dims.size() == 2fast_shape.size() == 2pooled_shape.size() == 2gemm_input_edge.src_arg_index < 2batch_axis < 2time_axis < 2input_node.InputDefs().size() == 2 && scale_and_index->second < 2!char_tokenezation_ || mincharnum_ < 2mask_unsqueeze_2 axes not matched. Expect: 2static_cast(grid_dims.NumDimensions()) == data_dims + 2Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2activation_func_names.size() == static_cast(num_directions_) * 2output1c2->prev == h1shape2 must have less dimensions than shape1CheckSliceParameters returns false for slice1cur1 == end1weight_gather_temp_1feature_map_1T1ReduceL1EGENERAL_TYPE1Maximum 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=1invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0001 (SOH) must be escaped to \u0001concat first input value is not -1padding_length should be more than 0 or equal -1Invalid Target shape product of 0. Product cannot be 0 in combination with -1ort_value_name_idx_map.MaxIdx() > -1requested_shape[i] >= -1unknown_dim == -1domain_version != -1input_shape_size != -1Number of dimensions for batch indices should be exactly 1Number of dimensions for crop size should be exactly 1QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1center_point_box only support 0 or 1fmod must have value either 0 or 1new_axis must be either 0 or 1Received invalid value for allow_spinning. Valid values are 0 or 1. Must be 0 or 1) or 1word_embedding and segment_embedding shall have same dimension 1word_embedding and position_embedding shall have same dimension 1Attribute `num_outputs` value cannot be lower than 1Scale and Zero-point must be of rank 1Failed to find reshape shape path 1Must have a single dimension of 1'sequence_lens' must have rank of 1QGemm : 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 1QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1QLinearConv : input zero point must be a scalar or 1D tensor of size 1QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1QLinearConv : result zero point must be a scalar or 1D tensor of size 1QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1Segment 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 1Word embedding zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1input y_scale must be a scalar or 1D tensor of size 1Input x_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1QLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearMatmul : input scale must be a scalar or 1D tensor of size 1QLinearConv : result scale must be a scalar or 1D tensor of size 1QLinearMatmul : result scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Word embedding scale must be a scalar or 1D tensor of size 1Beta scale must be a scalar or 1D tensor of size 1Gamma scale 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 : scale of input a must be a scalar or 1D tensor of size 1k tensor should be a 1D tensor of size 1Either the key tensor or the value tensor has NumDimensions > 1input_count >= 1inputCount >= 1num_inputs >= 1num_return_sequences >= 1Indices tensor must have rank >= 1data tensor must have rank >= 1left_num_dims and right_num_dims must be >= 1count == 1num_axes_dims == 1n_dims == 1parameters->sequence_length == 1called_ == 1candidate_output_dims[iter] == 1input_shape[i] == 1t_proto_p->dims()[0] == 1size_ % 2 == 1left.NumDimensions() == 2 || left.NumDimensions() == 1blob_shape.NumDimensions() == 1split_tensor->Shape().NumDimensions() == 1axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1shapeTensor->Shape().NumDimensions() == 1t_proto_p->dims_size() == 1new_gemm_output_defs.size() == 1classes.size() == 2 || classes.size() == 1shape_data_tensor.Shape().GetDims().size() == 1output_node.OutputDefs().size() == 1transpose_node.InputDefs().size() == 1ratio_tensor->Shape().Size() == 1value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1largest <= 1past_v_gather indices != 1num_classes is < 1num_samples is < 1batch_size is < 1mask_unsqueeze_1 axes not matched. Expect: 1i == count - 1true if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1actual_num_affinities == static_cast(options.thread_pool_size) - 1snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1n <= num_threads_ + 1Outer size must be M + 1past_0present_0feature_map_0present_key_self_0present_value_self_0data_0shape.Size() must >=0invalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0000 (NUL) must be escaped to \u00001.19.0Custom scale will be used if specified. Default value is 1.0Expected a sequence type for input 0Input 1 dimension 0 should have same length as the last dimension of input 0Input 1 dimension 0 should have same length as dimension 2 of input 0Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0One float, indicates the value to be filled, default is 0Single dimension value must be greater than 0indices tensor must has rank larger than 0Invalid Target shape product of 0Invalid position of 0'step' value cannot be 0'step' cannot be 0DequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0snprintf_result > 0n_elements > 0mem_steps <= max_memory_steps_ && mem_steps > 0naxes > 0num_axes > 0n_targets_or_classes > 0info.GetAttr("kv_num_heads", &kv_num_heads).IsOK() && kv_num_heads > 0info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0split_scalar > 0k_temp > 0processor_from > 0 && processor_to > 0mincharnum > 0data_rank > 0) must have a dense-rank > 0last_loop_size > 0dim_size > 0buf_size > 0last_loop_red_size > 0info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0Invalid value in 'split' attribute. All values must be > 0Split should be > 0processor_id > 0pooled_height_ > 0num_categories_ > 0map_form_ != PACK_MAP::SPARSE || max_map_ > 0impl_->min_gram_length_ > 0pooled_width_ > 0max_ngram_size_ > 0min_ngram_size_ > 0spatial_scale_ > 0kernel_shape[dim] > 0kernel_shape_[dim] > 0Input shape must have either [C] or [1,C] dimensions where C > 0BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0projected_index.size() > 0ops.size() > 0input_indices.size() == expected_values.size() && input_indices.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0weights_to_be_filled_in.buffers_.size() > 0total_num_elements_to_copy >= 0index >= 0v >= 0cur_input == end_input || cur_input->first >= 0input_offset >= 0 && output_offset >= 0logits_processor >= 0n >= 0*out_size >= 0Invalid value in 'split' input. All values must be >= 0Seed must be >= 0start_token_id >= 0roi_batch_id >= 0seed >= 0impl_->max_skip_count_ >= 0sampling_ratio_ >= 0epsilon_ >= 0M_ >= 0 && K_ > 0 && N_ >= 0right[i] >= 0left[i] >= 0sum_output_edge.src_arg_index == 0info == 0start_offset % span_size == 0 && real_end % span_size == 0p != nullptr || size == 0layout_ == 0offset % span_size_ == 0p.first->second->id_ == 0input_shape.Size() > 0 || input_shape[0] == 0input_shape.Size() > 0 || N == 0input_num_bytes % 4 == 0input_count_x3 >= 3 && input_count_x3 % 3 == 0input_def_count >= 5 && (input_def_count - 2) % 3 == 0std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0size != 0 && (input_shape_size % size) == 0sparse_tensor_names_.count(tensor_name) == 0node->GetOutputEdgesCount() == 0sum_node.GetOutputEdgesCount() == 0kRegexpCapture cap() == 0group count is <= 0past_k_gather indices != 0Invalid Y argument: num_indices = 0node_info.stream_index < 0node_info_vec[i].stream_index < 0offset < 0operator//QuickGeluFusion//GatherSliceToSplitFusion//SimplifiedLayerNormFusion//LayerNormFusion//GemmTransposeFusion//MatmulTransposeFusion//MatMulScaleFusion/.//./ was not found. Defaulting to a rank 1 shape of {0}.The expression contained mismatched { and }.' is required to be non-empty.Stack not empty.' has one dimension but is empty.' is specified but is empty.X dims is empty.Graph state to be loaded into must be empty.[BPEDecoder]id vocab text cannot be empty.Both inputs cannot be empty.[BPEDecoder]byte_decoder cannot be empty.Splitting pattern cannot be empty.pattern (second input) cannot be empty.Input 'split' can not be empty.) should not be empty.vocabulary shouldn't be empty.merges shouldn't be empty.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.[BertTokenizer]: only support one or two query.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.Unknown error occurred in memory copy.or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.The scale to apply.Please 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.Node id for each node. Node ids must restart at zero for each tree and increase sequentially.Incorrect arena extend strategy.Input 'value' is expected to have same hidden size as key.Failed to generate EP context model since the file exist already.Shouldn't be possible to have NodeArgs that haven't been handled already.(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.cublasLt order of output matrix.Invalid roi input index.Failed to fetch type shape info for the index.Regex pattern to match on. This must be valid RE2 syntax.opset version of corresponding SoftMax.Only tensors are supported for external outputs for now.Non per-tensor quantization is not supported now.No valid axes are specified. This is not handled now.Input 'key' and 'value' shall be both present, or both absent in the case of packed qkv.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.] because it's the graph's output.Max detections to output.) has zero input and zero output.Optional is expected to have an 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.Label encoder has only one output.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.Minimumber of tokens we keep per batch example in the output.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.Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output.OptionalHasElement is expected to have 1 output.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.Activation function to apply to the scores input.BroadcastLooper requires two tensors as input.Only 1D tensor are supported as input.Only one of scales or sizes must be provided as input.Either scales or sizes MUST be provided as input.Scale 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.Label encoder has only one input.The output type will be a tensor of strings or integers, and will have the same shape as the input.Input type is null. Type information is expected for the input.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Its length must be equal to the rank of the input.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.Got nullptr for sequence input.ExpandBroadcastLooper should only have a shape for the second input.SequenceConstruct is expected to have at least 1 input.OptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 input. already exist.Input to set must exist.Attempting to get an output that does not exist.Attempting to get an input that does not exist.Attribute does not exist.Singleton attribute value cannot be specified as a list.Tensor must have at least 3 dimensions to convert between channels first and channels last.Output Type. Same definition as attribute 'to' for operator Cast.Dimension on which to do the sort.Works on NHWC layout or not? Default not.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. does not.Flag indicating whether the regression is a one-class SVM or not. did not.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.The type of the output and its zeropoint.The type of input b and its zeropoint.The type of input a and its zeropoint.Maximum number of events reached, could not record profile event. is not present.Input 'past_key' and 'past_value' shall be both present or both absent.Input 'cos_cache' and 'sin_cache' shall be both present or both absent.Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.will be different.The exponent.Invalid tensor shape slice argument.This session will use the allocator registered with the environment.OptionalGetElement must have an input element.Input 'depth' must have exactly one element.total_sequence_length tensor must be of one element.Input 3 is skip_empty, it has 1 element.Input 2 is the delimiter, it has 1 element.Input 3 is the axis, it should have 1 element.Input 2 is the separator, it should have 1 element.single_node_compute_func should have 1 element.default 1; Pooled output Y's height.not support normalize yet., this is not supported yet.MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.Key type is not supported yet.Attribute type not supported yet.Async Kernel Support is not implemented yet.DequantizeLinear into BFLOAT16 is not implemented yet.Attribute to is not set.Attribute blocksize is not set.Attribute shape is not set.Non of classlabels_int64s or classlabels_strings is set.Container for generated shape data cannot be nullptr when enable_data_propagation option is set.No kernel shape is set.Attempt to retrieve final output before it was set.Optional is expected to have either an input or the type attribute set.At least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set.At least one of values_tensor, values_strings, values_int64s, values_floats must be set.A target node must be set.Attributes 'leaf_targetids' and 'leaf_weights' must both be set.A list of strings. One and only one of 'keys_*'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.A list of strings. One and only one of 'value_*'s should be set.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.Constrain the input to float and bfloat.Constrain quantized zero point types to uint8/int32/float16/float.The input must be an integer map to either string or float.The output will be a sequence of string or integer maps to float.Attribute 'value_float' expect a float.Size of data not large enough to hold a float.A float.Expect 'value' be none when 'key' has packed kv format.The value for the elements of the output tensor in sparse format.Requested size is too large to fit into size_t.Constrain scalar length types to int64_t.Constrain the input size to int64_t.Expected 1 or more outputs.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.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.Encoding type for the boxes or anchors inputs. Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.Slice op must have either three, four or five inputs.Expected 1 or more inputs.MaxUnpool op must have either two or three inputs.OneHot node must have three inputs. inputs. Either provide all subgraph inputs, or just the required inputs.Operator SegmentSum has 2 inputs.CenterCropPad op must have 2 inputs. already exists. in function opset imports. domain not found in opset imports.The id of the token that indicates decoding starts.A collection of intercepts.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.A list of ints.Rerunning with verbose output on a non-minimal build will show node assignments.Whether to return the top-K largest or smallest elements.Input 'values' must have exactly two elements.Axis has less than the requested k elements.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.Key and value tensors have unequal number of elements.X and Y must both have M * N elements.First set of probability coefficients.Support vector coefficients.Failed to get Clip min/max constants.Failed to get position embedding weights.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.The total number of targets.Size of data not large enough to hold the array of floats.A list of floats.The number of support vectors. model may run depending upon legacy support of some older opset version operators.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.Transpose not implemented for empty tensors.Type of Mean and InvStdDev tensors.Scalar multiplier for the product of the input tensors.Constrain input and output types to floating-point tensors.Constrain input, weight, and output types to floating-point tensors.Constrain input and output types to float/int tensors.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Constrain input type to float or int tensors.Constrain types to int tensors.Constrain sequence_length to int tensors.Constrain output type to float32 or 8 bit tensors.Constrain input and output types to 8 bit tensors.Constrain output zero point types to 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input A and its zero point types to 8 bit tensors.Constrain weights types to 8 bit tensors.Constrain input and output types to float and 8 bit tensors.Constrain output types to 32 bit tensors.Constrain input and output types to float/half_float/brain_float tensors.Constrain input and output types to float/half_float tensors.Constrain input and output to float tensors.Constrain signal and output to float tensors.Constrain input to float tensors.Constrain output types to float tensors.Constrain scores input and output types to float tensors.Constrain input and output types to float tensors.Constrain input X and output types to float tensors.Constrain input types to float tensors.Constrain scale and bias types to float tensors.Constrain types to float tensors.Constrain scale types to float tensors.Constrain mean and variance types to float tensors.Constrain grid types to float tensors.Constrain input X and output Y types to float tensors.Constrain input X, skip, bias and output Y, S types to float tensors.Constrain input 'ratio' types to float tensors.Constrain input and output types to float tensors.Constrain mean and inv_std_var to float tensors.Constrain to float tensors.Constrain output Y, scale and bias type to float tensors.Constrain output Y and scale type to float tensors.Constrain input types and output Y type to float tensors.Constrain input X type to float tensors.Constrain gamma and beta to float tensors.Constrain 'x', 'y_scale' to float tensors.Constrain 'y', 'x_scale' to float tensors.quantization scale must be float tensors.Constrain mean and inv_std_var to be float tensors.Constrain input and output types to uint8, uint16, float tensors.Constrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.Constrain input C to 32 bit integer tensors.Constrain input/output to integer tensors.Constrain input to integer tensors.Constrain input and output types to integer tensors.Constrain to integer tensors.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.Constrain input/output to boolean tensors.Constrain output types to boolean tensors.Constrain output mask types to boolean tensors.Constrain output 'mask' types to boolean tensors.Constrain input 'training_mode' types to boolean tensors.Constrain to boolean tensors.Constrain input and output types to numeric and Boolean tensors.Constrain key_padding_mask to bool tensors.Constrain output types to all numeric tensors and bool tensors.Constrain input and output types to all numeric tensors and bool tensors.Constrain input and output types to all tensors.Constrain input to integral tensors.Constrain seq_lens to integral tensors.Constrain output types to integral tensors.Constrain input and output types to float or half tensors.Constrain input types to common numeric type tensors.Constrain output types to bool, int32, int64, float16, float, double tensors.Saving initialized tensors.Constrain input and output types to 8 bit signed and unsigned tensors.Constrain input types to 8 bit signed and unsigned tensors.Constrain input and output types to high-precision and 8 bit numeric tensors.Constrain output types to numeric tensors.Constrain input and output types to numeric tensors.Constrain input and output types to high-precision numeric tensors.Constrain input and output types to all numeric tensors.Constrain input types to all numeric tensors.Constrain input and output types to signed numeric tensors.TreeEnsemble only works on 1D, 2D tensors.The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Constrain weights type to uint8 tensors.Constrain input and output types to int8 tensors.quantization tensor must be int8 tensors.Constrain input and output types to signed/unsigned int8 tensors.Constrain input and output types to float or float16 tensors.Constrain input and output types to float16 tensors.Constrain input and output types to float and float16 tensors.Constrain repeat's type to int64 tensors.Constrain tiles and axis's type to int64 tensors.Constrain size's type to int64 tensors.Constrain output 'mask' types to uint32 tensors.Constrain qk output types to float32 tensors.Constrain input and output types to float32 tensors.Constrain scales to float32 tensors.Constrain cross_qk to float32 tensors.Constrain input types to all (non-complex) tensors. must be either specified in graph inputs or graph initializers.No kernels to registers.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.At least one element in the sequence is of a type different from others.The output will be a tensor of strings or integers.Constrain scalar length types to integers. are different, which is not permitted for LabelEncoders.An entry for this node should be added in kLayoutTransformationPotentiallyAddedOps.Conflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.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.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.: Conflicting with a registered kernel with op versions.size of 'dilations' attribute, if provided, should equal to the number of image dimmensions.size of 'strides' attribute, if provided, should equal to the number of image dimmensions.size of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.Invalid argument: X input has empty dimensions.Invalid argument: input has empty dimensions.Empty input dimensions.'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.If set, defines the broadcast dimensions.Only works on matrices with two dimensions.Input is expected to have dim value in all dimensions.Ellipsis represents incompatible dimensions.Only works on matrices with three dimensions.Input tensor X must have at least 2 dimensions.Target shape may not have multiple -1 dimensions.Duplicate special tokens.kernel_shape num_dims is not compatible with X num_dims.kernel_shape num_dims is not compatible with W num_dims.X num_dims does not match W num_dims.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.If set, defines the broadcast dimensions. See doc for details.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.if align_corners=1, consider -1 and 1 to refer to the centers of the corner pixels. if align_corners=0, consider -1 and 1 to refer to the outer edge the corner pixels.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.A list of labels.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.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.Insert and concatenate on a new axis or not, default 0 means do not insert new axis.The axis on which to apply normalization, -1 mean last axis.Stride along each spatial axis.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.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along 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.Stride along each spatial axis. If not present, the stride 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.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.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.The size of the kernel along each axis.Stride along each axis.Stride along each spatial axis. Default is 1 along each axis.Dilation value along each spatial axis of the kernel. Default is 1 along each axis.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.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.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. in one of the subgraphs.Tensor of substrings.[HfBertTokenizer]: Support only two input strings.Size of data not large enough to hold the array of strings.A list of strings.Done saving OrtValue mappings.The expression contained an invalid character range, such as [b-a] in most encodings. not in allowed output sizes. not in allowed input sizes.Pads tensor size should be equal to twice the number of explicitly provided axes.) has no index values.Lower boundary of the output values.Upper boundary of the output values.Empty value of imputed values. bytes.If `nodes_trueleafs` is false at an entry, this represents the position of the true branch node. This position can be used to index into a `nodes_*` entry. If `nodes_trueleafs` is false, it is an index into the leaf_* attributes.If `nodes_falseleafs` is false at an entry, this represents the position of the false branch node. This position can be used to index into a `nodes_*` entry. If `nodes_falseleafs` is false, it is an index into the leaf_* attributes.decoder subgraph past inputs shall have same data type as that of encoder_hidden_states.Only support GirdSample in 4-D or 5-D cases.Only support GridSample Cubic mode in 4-D cases. features.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.Constrain input and output types.Constrain input types.Constrain input and output float tensors types.Constrain input and output to all tensor types.Constrain output types to all tensor types.Constrain input and output types to all tensor types.Constrain input types to all tensor types.Constrain input `X` and output `Y` types to all tensor types.Constrain to all tensor types.Constrain input 'X' and output 'Y' to all tensor types.Constrain input and output types to all numerical tensor types.Constrain input and output types to all numeric tensor types.Constrain to integer types.Input is not of one of the supported map types.Constrain input and output types to all tensor, sequence, and optional types.Constrain to any numerical types.Constrain output type to all tensor or sequence types.Constrain output type to all optional tensor or optional sequence types.Constrain input type to optional tensor and optional sequence types.Constrain input and output types to all tensor and sequence types.Constrain input type to all tensor and sequence types.Input is not of one of the supported sequence types.Constrain input and output to only numeric types.Constrains input to only numeric types.Constrain input to only numeric types.Attribute saturate is only used for cast to float 8 types.Can only add a new input at the end of the current ones.' has been used as output names multiple times.' has been used as graph input names multiple times.' appeared multiple times.File not found in preloaded files.Constrain scales.Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.The value used to module the next token probabilities.boxes and scores should have same num_batches. as it still has output edges. is not output of any previous nodes.For 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.For each node, define whether to follow the true branch (if attribute value is 1) or false branch (if attribute value is 0) in the presence of a NaN input feature. This attribute may be left undefined and the default value is false (0) for all nodes.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.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Members to test membership of for each set membership node. List all of the members to test again in the order that the 'BRANCH_MEMBER' mode appears in `node_modes`, delimited by `NaN`s. Will have the same number of sets of values as nodes with mode 'BRANCH_MEMBER'. This may be omitted if the node doesn't contain any 'BRANCH_MEMBER' nodes.Conflicting free dimension overrides.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. does not match the equation indices.The axis in which to compute the arg indices. is out of bounds.Out of bounds.The parent of two shape nodes are expected to be input_ids.The parent of shape nodes are expected to be input_ids.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.Constrain output types to be numerics.data_transfer registered is nullptr.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.List 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.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.' must be a vector.Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.operator.the index tensor.(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.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.The value for the elements of the output tensor.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.The shape of the 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.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.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.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.The dimension with value zero exceeds the dimension size of the input tensor.The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.(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 mask to int tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.Constrain input A, b_scale and output Y data type as float tensor.Constrain 'x' to float tensor.The split tensor must be a vector tensor.An axes tensor must be a vector tensor.A shape tensor must be a vector tensor.Failed to get initializer tensor.Constrain output type to 8-bit integer tensor.Constrain input type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.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 input b and its zero point data type to 8-bit integer tensor.Constrain input a and its zero point data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain input A data type to 8-bit integer tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.Constrain bias type to 32-bit integer tensor.Constrain output y data type to 32-bit integer tensor.Constrain output type to unsigned and signed 32-bit integer tensor.Constrain seq_lens to integer tensor.Constrain output types to 8-bit unsigned integer tensor.Constrain input types to 8-bit unsigned integer tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.An axis tensor must be a scalar tensor.Constrain output to boolean tensor.Constrain input to boolean tensor.Constrain 'training_mode' to boolean tensor.Constrain output to a boolean tensor.Constrain the output to a boolean tensor.Constrain split size to integral tensor.) second dimension size does not match rank of tensor.Allow inputs and outputs to be any kind of tensor. was not a tensor.boxes must be a 3D tensor.scores must be a 3D tensor.The default tensor must be a singleton 1D tensor.An axes tensor must be a scalar or a 1-D tensor.k should be a 1-D or 0-D tensor.Constrain output to int64 tensor.Constrain shape of B must be int64 tensor.Constrain output 'mask' types to bit-packed uint32 tensor.Constrain 'x' to float or int32 tensor.Constrain 'x' to float, float16, bfloat16 or int32 tensor.Input 'depth' must be a scalar or rank 1 tensor.Input 'values' must be rank 1 tensor.'output_shape' must be rank 1 tensor.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.Internal error.The storage order of the tensor. 0 is row major, and 1 is column major.node id that this weight is for.The index of the class list that each weight is for. is not found or is not constant initializer.Number of neurons in the hidden layer.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.Dilation value along each spatial axis of filter.dilation value along each spatial axis of the filter.Scaling parameter.This session will use the CUDA/HIP Graph feature as requested by the user.Buffer containing the initializer must be owned by the user.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. cannot be used with this model due to its ONNX opset not being supported by the layout transformer.Number of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller. known by the checker. 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.The output is a 1-D tensor of string, float, or integer.Attribute 'value_int' expect an integer.InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.An integer.DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.] not in sorted order.Whether to return the elements in sorted order.] not in lexicographic sorted order.Only one of keys_*'s can be set in label encoder.Only one of values_*'s can be set in label encoder. must be the same in the LabelEncoder.Adding default CPU execution provider.So disabling it for this session since it uses the DML Execution Provider.So making the execution mode sequential for this session since it uses the DML Execution Provider.filter number not equal to input channel number.An empty regex is not allowed in the POSIX grammar.[MaskedFill]: the dimension of input value should be vector or scalar.[BasicTokenizer]: only support string scalar.num_mel_bins input must be scalar.dft_length input must be scalar.frame_length input must be scalar.Invalid argument for depth; it's not a scalar.axis input must be a scalar.dft_length input must be a scalar.size input must be a scalar.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.max should be a scalar.min should be a scalar.tensor of bool, which should be a scalar.tensor of int64, which should be a scalar.Input channels is not divisible by group.Output channels M is not divisible by group.Input channels C is not equal to kernel channels * group. This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used. Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`. If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size: `out_size[d] = sizes[i]` If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio: ``` scale = Min(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio: ``` scale = Max(sizes[i] / in_size[d]) out_size[d] = round_int(scale * in_size[i]) ``` For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. This attribute describes how to 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.Decoder subgraph to execute in a loop.specify the step.Each element of the sequence should be either tensor or map.Input is not of type sequence or map. does not support TunableOp. but different TensorProto.number of groups input channels and output channels are divided into.The datatype to dequantize to.Value(s) to change to.Node id for each node. Ids may restart at zero for each tree, but it not required to.The epsilon value to use to avoid division by zero.dft_length must be greater than zero.both data and indices tensor need to have rank larger than zero. is missing type info.SetGraphAndCreateKernels must be called prior to GetExecutionInfo.word_embedding should have 2 dimensions and dimension size is known.The Model Proto hasn't been checked for the ORT config json.The standard deviation of the normal distribution.The mean of the normal distribution. is already in session option.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.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.[json.exception.cublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.cublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.cublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.) in op definition.Axes input and attribute should not both be present for reduction.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.If set to 1 will perform the sums in reverse direction.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.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.Nodes to optimize are not valid, skipping action.Number of input tensors does not match the operands in the equation.The floating-point precision used in stage one of the computation.Empty list attribute value requires type annotation.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.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.Type of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.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.Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the addition operation. 'max': reduction using the maximum operation.'min': reduction using the minimum operation.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.Optional-type input must contain an element with type information.Input must be an optional-type value containing an element with type information.This is an invalid model. Tensor does not have type information.) does not have type information.Input type is null. Input must have Type information.Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation.) is stored externally but doesn't have a location.Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.Initializing session.The expression contained an invalid range in a {} expression.The parser did not consume the entire regular expression.One of *?+{ was not preceded by a valid regular expression.Be compatible with float version.Missing model IR version.boxes and scores should have same spatial_dimension.input tensor must have rank >= 2, including the complex dimension.Each 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.Two input tensor should have the same dimension.Upsample: input shape needs to be at least a single dimension.Input shape needs to be at least a single dimension.Keep the reduced dimension or not, default 1 means keep reduced dimension.Keep the reduced dimension or not, default 1 mean keep reduced dimension.Type of Mean and InvStdDev. This also specifies stage one's computation precision.Version number of the TRT plugin.The id of the tree that this node is in.The id of the tree that each node is in.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.A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Empty special token.Constrain output types to be numerics or boolean.invalid arg_num.Operator MatMulFpQ4 not yet supported on this hardware platform.Tensor does not have external data to read from.[AudioDecoder]: unexpected error on WAV stream.[AudioDecoder]: unexpected error on FLAC stream.[AudioDecoder]: unexpected error on MP3 stream.Index tensor shape should be same as that of the input data tensor to unpool.op_kernel_type_str_args is null.kernel_type_str is null.Input Sequence and Tensor are expected to have type info. Current type is null.op_id is null.inputs are expected to have tensor type and output type should not be null.Outputs from Scan are not optional and should never be null. are out of bounds or can not be read in full.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.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.The complexity of an attempted match against a regular expression exceeded a pre-set level.Pad should be smaller than kernel.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. value of 'save' is only valid when saving an ORT format model.Model must have opset imports. Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Missing dimensions for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing values for sparse initializer. Invalid ORT format model.Null tensor type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.InferenceSession is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.Graph is null. Invalid ORT format model.Graph attribute value was null. Invalid ORT format model.Missing Model. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Null string attribute. Invalid ORT format model.Null value type info in fbs::MapType. Invalid ORT format model.Null value type info in fbs::SequenceType. Invalid ORT format model.dim_param value with no name. Invalid ORT format model.. Please, fix your model.(Optional) SDK version used to convert the model.(Optional) SNPE version used to convert the model. Please fix either the inputs/outputs or the model.. 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.'. It is not used by any node and should be removed from the model.This session already contains a loaded model.Incomplete string literal.cublasLt order of matrix Y, must be same as order_X if specified together. Optional.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Tensor with shape information must be 1 dimensional.(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.Ensure that the dft size is smaller than the signal.Target rank must be 1 less than the input rank.Resize: input tensor's rank does not match the output tensor's rank.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back.The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.Maximum relative distance, k.Must provide classlabels_strings or classlabels_int64s but not both.Must provide imputed_values_float_ or imputed_values_int64_ but not both.If both frame_length and window are set, then the size of the window must be equal to the frame_length.cos_cache dimension 0 should be of max_sequence_length.sin_cache dimension 0 should be of max_sequence_length.Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.Keys and values must have the same length.[Stft] Only support size of FFT equals the frame length.output buffer is too small. Use GetStringTensorDataLength.default 1; Pooled output Y's width.Gather node in path 2 is not linked to another subgraph.The subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.Failed to match position subgraph.SetupSubgraphExecutionInfo should only be called once for each subgraph.Failed to match position embedding subgraph.The subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.CreateFeedsFetchesManager must be called prior to execution of graph.) does not exist in the graph. does not contain a graph.ModelProto does not have a graph.Constrain output to int64 tensor, which should be a scalar though.AddFoldedRange recurses too much. Version mismatch.Output size mismatch.Input and target dimension value mismatch.Tensor type mismatch.Replacement tensor's data type does not match.Replacement tensor's dimensions do not match.If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.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.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.' is required but missing.' is missing.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.Einsum expression string.', but the existing argument with that formal parameter name has a different formal parameter type string.Size of data not large enough to hold the string.Attribute 'value_string' expect a string.A string.An allocator for this device has already been registered for sharing.No allocator for this device has been registered for sharing.cublasLt 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.Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.The maximum NGram size for suffix matching.The minimum NGram size for suffix matching.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.position_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.Rotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding.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.A value that needs replacing.No graph was found in the protobuf.Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.If true, check only for Inf, -Inf.The weight for each leaf.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The epsilon value to use to avoid division by zero, default is 1e-5f.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.Unsupported bit size.Attribute strides has incorrect size.Attribute pads has incorrect size.Attribute kernel_shape has incorrect size.Per-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.The NGram size.Unsupported pooling size.Image size.Pooled size.List of non-negative integers, indicate the dimensions to squeeze. conflicts with graph initializer. Check name generation above.Method IncrementIndexAndComputeOffset assumes this value is strictly positive.) dimensions are not positive.Depth is negative. Parameter to BackUp() can't be negative.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Child node if expression is true.Exiting due to terminate flag being set to true. conflicts with graph initializer. Check that the node names have been made unique.All filtered values will be set to this float value.Attribute has no float value.dft_length must be a scalar value.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.ratio input should have a single value.Score threshold value.Box IOU threshold value.Alpha value.Attribute has no int64 value.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.legacy optimization attribute.Char embedding size does not match char_embedding_size attribute.Conv filter size does not match embedding_size attribute.Invalid attribute.Unspecified required 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.Subgraph SessionState was not found for 'body' attribute.Subgraph SessionState was not found for 'encoder' attribute.Subgraph SessionState was not found for 'decoder' attribute.Output type is determined by the specified 'values_*' attribute.Failed to analyze start state.StateSaver failed to restore state.lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.upper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.'. 0 == forward. 1 == reverse.Child node if expression is false. was false.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.(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.Indicate if the ops run in training_mode, by default, False.Outputs are bools and are True where there is a full regex match and False otherwise.Unsuported type proto value case.Start another run for necessary memory allocation or graph capture.(Optional) Hardware architecture.it is safe to use the graph capture feature. is already there.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.Input tensor can be of arbitrary type.Expecting primitive type as map key type.target map type missing key type.source map type missing key type.'x_scale' determines the output type.Constrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.Unsupported input type.Input 'scales' must have float element type.Unexpected type in sparse-tensor element type.target optional type missing element type.source optional type missing element type.target sequence type missing element type.source sequence type missing element type.Constrain type to result type.A default tensor. {"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.Attribute 'scales' must have floats type.Constrain output types to any tensor type.Constrain input and output types to any tensor type.Constrain input types to any tensor type.Constrain to any tensor type.Input can be of any tensor type.Input and output types can be of any tensor type.Constrain scale types to any float tensor type.inputs are expected to have tensor type.The input must be a tensor of a numeric type or string. The output will be of the same tensor type.The input must be a tensor of a numeric type. The output will be of the same tensor type.Invalid index requested for map type.Unexpected literal type.Subgraph input missing type.target map type missing value type.source map type missing value type.Unsupported OrtValue type.Unexpected attribute type.input and zero_point pair is expected to have same type.weight and zero_point pair is expected to have same type.input and zero_point pair is expected to have be same type.Existing destination type is not compatible with source type.Constrain input types to any sequence type.Constrain to any tensor or sequence type. does not specify a valid type.Unexpected type.The input must be a tensor of a numeric type.The input type must be a tensor of a numeric type.TensorSeq: tensor to be added has a different data type.Violation of the requirment that all input tensors must have the same data type.Attribute dtype should be of integer type and specify a type.Attribute 'type' should be a TypeProto and it should specify a type.) must have INT64 type.OrtValue is TensorSequence type but has no element Tensor DataType.Unsupported AutoPad Type.Optional position subgraph nodes Where node is expected to be the parent of Reshape.The input type is a tensor of any shape.The input type must be a tensor of integers or strings, of any shape.Whether to use ceil or floor (default) to compute the output shape.The output is a tensor of strings or integers. Its shape will be the same as the input shape.Must have a valid input shape.Output edge count not expected for nodes in path 2 of position shape.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 1 of position shape.Failed to find path 1 of position shape.This 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.The 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.Please fetch output tensor with specified shape.Allocation of tensor types requires a shape.kernel_shape is not compatible with W shape.The expression contained an invalid escaped character, or a trailing escape.(Optional) Seed to the random generator, if not specified we will auto generate one.There was insufficient memory to convert the expression into a finite state machine.Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time.axes as an input and attribute cannot be specified at the same time.cos_cache and sin_cache dimension 1 must be the same.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.Element type of inputs are expected to be the same.Rank of input and output tensor should be same.[MaskedFill]: the dimension of input value and mask should be same.The expression contained an invalid collating element name.The expression contained an invalid character class name.(Optional) partitioned graph name.AttributeProto must have a name.Number of times to sample., but it is not regular file.payload of the SNPE DLC file.custom_create_thread_fn returned invalid handle.Some nodes are not included in the topological sort, graph have a cycle.Constrain roi type to float or double.Unknown aggregation function in TreeEnsemble. However the types are incompatible., but it doesn't exist or is not accessible.Module.MountedFiles is not available.CUDA and/or ROCM execution provider is either not enabled or not available.Op schema must be available.File is too large.] out of range. is outside range.At least one graph node must be specified in the propagation edge.Argument mismatch when removing edge.Invalid destination node arg slot specified when removing edge.Invalid source node arg slot specified when removing edge.Invalid node indexes specified when removing edge.Argument type mismatch when adding edge.Invalid destination node arg slot specified when adding edge.Invalid source node arg slot specified when adding edge.Invalid node indexes specified when adding edge.Set session_options.log_severity_level=1 to see the detail logs before this message.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.Coefficient of leakage. is not used by any node.'. It is no longer used by any node.) does not have type information set by parent node.) should refer to attribute in parent node.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.' is not a graph input, initializer, or output of a previous node.Fused an attention node.Expected activation node.Thresholds to do the splitting on for each node.Feature id for each node.Tree id for each node.Index into `nodes_*` for the root of each tree. The tree structure is derived from the branching of each node.Failed to remove node.Failed to set op schema for added DQ node.Failed to set op schema for added Q node.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode. conflicts with graph initializer. Check Specializing code.Invalid free dimension override. is referred to more than once.There was insufficient memory to determine whether the regular expression could match the specified character sequence.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The expression contained an invalid back reference.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.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.Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.Unsupported OrtValue type to copy between device.Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.Too many produced nodes in the runtime optimization record.Not enough produced nodes in the runtime optimization record.Identifier expected but not found.Value expected but not found.Integer value expected, but not found.String value expected, but not found.' was not found.Data type is not supported in this build.Gather Tind type not supported in this build.ROCm execution provider is not enabled in this build.TensorRT execution provider is not enabled in this build.oneDNN execution provider is not enabled in this build.CANN execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.Memory efficient topological order is not enabled for non-training build.) should contain one and only one value field.) should not contain more than one value field.) is stored externally and should not have data field.' Model is invalid.) is invalid.The weight for the class in class_id.Session successfully initialized.Session not initialized.Execution providers must be registered before the session is initialized.This session has already been initialized.Session has already been initialized.Regexp not destroyed.Blocks of [blocksize, blocksize] are moved.Removable attributes were removed before the node is saved.If set to true, it indicates BatchNormalization is being used for training, and outputs 1 and 2 are to be computed.Popularity of each node, used for performance and may be omitted.An invalid regex grammar has been requested.At least one output should be requested.Only 2-d matrix supported.[Stft] Only 1-d hanning window supported.Constrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.Specified provider is not supported.Reading data from non-tensor typed value is not supported.Tensor proto with external data for value attribute is not supported.Indices type is not supported.Model type is not supported.Specified device is not supported.Constrain output types. Casting to strings and complex are not supported.Constrain input types. Casting from strings and complex are not supported.Constrain output types. Strings and complex are not supported.Constrain input types. Strings and complex are not supported.ComputePad: pad type not supported.[Stft] Only batch == 1 tensor supported.Received OrtValue is not a tensor. Only tensors are supported.only block size 16, 32, 64, 128, 256 are supported.The order of the normalization, only 1 or 2 are supported.Invalid quant_type, only 0 (FP4) and 1 (NF4) are supported.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Removable attributes were removed before the conversion is started.Extra unparsed input unexpected.Optional position subgraph nodes number of outputs unexpected.Position embedding shape is not expected.Word embedding shape not expected.Input type for input at index 0 is null. Type info is expected.Sparse Indices raw data size does not match expected.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.The number of substrings generated.OrtValue indexes should have been populated.If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.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.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.The Function Style fusion is deprecated.left_window_size for local attention (like Mistral). Default value is -1 meaning unused.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.Weights of the intercepts, if used. is undefined so it cannot be parsed.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.Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.) has saved runtime optimizations. They will be ignored.Attribute 'nodes_splits' is required.Attribute 'scales' is required. has already been registered.Attempt to use DefaultLogger but none has been registered.) pruned.Only 4b quantization is supported for MatMulNBits op, additional bits support is planned.Node has no function body and cannot be inlined.The total number of regression targets, 1 if not defined.'is defined.No attribute with this name is defined.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.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.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using string keys.
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.Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.A string vocabulary array.
One and only one of the vocabularies must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Class labels when using string labels. One and only one 'classlabels' attribute must be defined.One float, indicates the value to be filled.Scan<8> spec does not support transpose of output. This should never be called.Protobuf serialization failed.ORT model verification failed.Failed to load model because protobuf parsing failed.Protobuf parsing failed.Profiler is disabled.) is required but not specified.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.A 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.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.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.No provider specified.provider_type must be specified.Attribute kernel_shape must be specified.RegisterCustomOpsUsingFunction: Registration function name must be specified.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.Only one of the attributes 'class_weights', 'class_weights_as_tensor' should be specified.Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Only one of the attributes 'base_values', 'base_values_as_tensor' should be specified.Only one of the attributes 'nodes_hitrates', 'nodes_hitrates_as_tensor' should be specified.qk_div const not matched.equal const not matched.where const not matched.gather indices not matched.Position embedding shape not matched.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.There 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.(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.Tensor has external data but a data reader was not provided. not found from the table user provided. were provided.Exactly one of 'cats_*' attributes must be provided.If 'num_outputs' is specified, the 'split' input should not be provided.string tensor requires allocator to be provided.must be overloaded.Model was not loaded.Could not write a profile because no model was loaded. has already been loaded.OrtValue has not been allocated so can't be sliced.Expected Conv then Add.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad. bytes were able to be read.(Optional) Target device like CPU, DSP, etc.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.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.QLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.This is an invalid model. Error: the graph is not acyclic. to a custom op may be marked variadic.Three interpolation modes: bilinear (default), nearest and bicubic.Value of beta.Output count is inconsistent with IO Binding output data.p value of the Lp norm used to pool over the input data.Failed to verify KernelTypeStrResolver flatbuffers data.The most inner dimension in boxes must have 4 data.Attribute 'value' of Constant node must exist with 'Tensor' data.Failed to set node op schema.Value of alpha.The type of the input `y_zero_point` and the output `y`.List 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`.List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. The above behavior is similar to numpy, with the exception that numpy defaults `keepdims` to `False` instead of `True`. If the input data type is Boolean, the comparison should consider `False < True`.Whether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.The number of groups of channels. It should be a divisor of the number of channels `C`.when anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(Optional) The output data type. If not supplied, the output data type is inferred from `y_zero_point` data type (`T2`). If neither `output_dtype` nor `y_zero_point` are supplied, output data type is uint8. If both `output_dtype` and `y_zero_point` are specified, `output_dtype` must be `T2`.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`.(Optional) The axis of the dequantizing dimension of the input tensor. Used for per-axis and blocked quantization. Negative value means counting dimensions from the back. Accepted range is `[-r, r-1]` where `r = rank(input)`.graph_proto_ is not in sync with name_to_initial_tensor_.[AudioDecoder]: Expect input dimension [n] or [1,n].[SegmentExtraction]: Expect input dimension [n] or [1,n].[BertTokenizerDecoder]: Expect ids dimension [n] or [1,n].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].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].A {name} window with length: size. The output has the shape: [size].The input must be a tensor of strings or integers, either [N,C] or [C].The input must be a tensor of a numeric type, either [C] or [N,C].The input type must be a tensor of a numeric type, either [C] or [N,C].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].Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].axis must be in [-rank, rank-1].indices must be in [-rank, rank-1].Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].iou_threshold must be in range [0, 1].The expression contained mismatched [ and ].) first dimension size does not equal NNZ.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.'output_shape' must have same number of elements as the shape of input tensor X.List of stop words. If not set, no word would be removed from X.(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The shape of the convolution kernel. If not present, should be inferred from input W.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.Coefficient of ELU.Constrain mean and variance types to float tensors. It allows all float type for U.Fused an attention node for GPT.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.cublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.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. 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.If true, check only for NaN.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.CPU beam search current not support output cross QK.When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.External data type cannot be UNDEFINED or STRING.External data type cannot be STRING.Background class ID.. It must be 4 for 2D or 5 for 5D.ZipMap input shape should be 1D or 2D.Shape of quantized tensor must be 1D.Attribute 'nodes_splits' must be 1D.Constrain type to input C.Scalar multiplier for the product of input bias C.Bias applied to each channel, same size as C.Scalar multiplier for input tensor C.Constrain type to input B.Scalar multiplier for the product of input tensors A * B.Constrain type to input A.All Tensor types up to IRv9.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.Constrain y to uint8.Constrain the input to uint8.Constrain quantized weight types to uint8.The comparison operation performed by the node. This is encoded as an enumeration of 0 ('BRANCH_LEQ'), 1 ('BRANCH_LT'), 2 ('BRANCH_GTE'), 3 ('BRANCH_GT'), 4 ('BRANCH_EQ'), 5 ('BRANCH_NEQ'), and 6 ('BRANCH_MEMBER'). Note this is a tensor of type uint8.cos_cache dimension 1 must be <= head_size / 2 and a multiple of 8.sin_cache dimension 1 must be <= head_size / 2 and a multiple of 8.Coefficient of SELU default to 1.0507.Constrain y to float and bfloat16.Failed 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.Position embedding data type shall be float or float16.DequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.The lambd value for the Shrink formulation. Default is 0.5.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.AffineGrid : Invalidate size - length of size should be 4 or 5.All Tensor types up to IRv4.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.quantization data type. 0 for FP4, 1 for NF4.Constrain shape and strides to uint64.Size of data not large enough to hold an int64.Size of data not large enough to hold the array of int64.K input must be of type int64.Maximum string length for a provider options key/value is 1024.Input dimension cannot be less than 3.input must have rank 3.Constrain quantized weight types to uint8/int32.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Coefficient of SELU default to 1.6732.) must have rank 1 or 2.Char embedding size does not match conv kernal size 2.Input rank must be >= 2.Output edge count not expected for nodes in path1.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.Whether B should be transposed. Float 8 only supprted transB=1.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Coefficient of leakage default to 0.01.The token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.At most one dimension can be -1.Number of values should be at least 1.number of groups input channels and output channels are divided into. default is 1.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.Number of groups of offset. C must be divisible by offset_group. Default is 1.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.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 1.1: 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.If 0, normalize the mean only. Default is 1.If value is 1, output type is uint32_t, else int32_t. Default value is 1.Which axis to concat on. Default value is 1.4-D input with innermost scale (usually channel of NHWC) as 1.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.If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m, n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.The scale along height dimension. It takes value greater than or equal to 1.The scale along width dimension. It takes value greater than or equal to 1.Scale value should be greater than or equal to 1.) must have rank 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.Couple the input and forget gates if 1.x_zero_point must be null or a scalar or 1D tensor or size 1.Must be a scalar or 1D tensor or size 1.K input must be a one-dimensional tensor of size 1.input zero point must be a scalar or 1D tensor of size 1.Second input of Gather in path 2 of position shape should be a constant with value 1.The third input of Range should be a constant with value 1.Weight rank must be 1.Whether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.Reduction on all axes, output size should be 1.Attribute 'max_output_boxes' must be >= 1.Attribute 'pooled_size' must be >= 1.window input must have rank = 1.payload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.Whether A should be transposed. Float 8 only supprted transA=0.Constrain input and output to all tensor types up to IRv10.Constrain input and output types to all tensor types up to IRv10.All Tensor types up to IRv10.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv10.p value of the Lp norm used to pool over the input data, default is 2.0.Scalar multiplier for input tensor C, the default value is 1.0.Scalar multiplier for the product of input tensors A * B, the default value is 1.0.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Coefficient of ELU default to 1.0.Couple the input and forget gates if 1, default 0.If nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.The sequence output for the hidden is optional if 0. Default 0.If set to nonzero, run spatial batch normalization in test mode, default is 0.The bias value added to output. Default is 0.If 1, mean and variance are computed across channels. Default is 0.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.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.Whether every token can only attend to previous tokens. Default value is 0.Rotary embedding dimension. Default value is 0.Whether to use rotary position embedding. Default value is 0.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Values greater than this are mapped to 1, others to 0. is less than 0. is less than 0. less than 0.size attribute in UnfoldTensor must greater than 0.size input must be greater than 0.scales size should be greater than 0.Scale value should be greater than 0.Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.src and dst must have same shape and not be rank 0.Invalid position of 0.Second input of Gather in path 1 of position shape should be a constant with value 0.The first input of Range should be a constant with value 0.Input shape must have either [C] or [B,C] dimensions with B > 0.length of each output. Values should be >= 0.Unknown Category and zeros = 0.outputs...sizeof...Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..signal shape must end in 1 (real) or 2 (real, imaginary).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).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).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).(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).(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).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).(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) 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).Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).Activation function, RELU or GELU or NONE (default).(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).Indicates whether to do OvR or multinomial (0=OvR is the default).Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input and output types to all tensor types (including bfloat).Constrain to tensor(float).Invalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). (weights).(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).A collection of weights of the model(s).Weights of the model(s).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).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).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).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).Error parsing TensorProto shape (expected numeric dimension).Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).A 1-D values of (height, width).ROI pool output shape (height, width).in onnx/defs/schema.h).seqlens_k must be shape (batch_size).Rotate using interleaved pattern. Default value is 0 (False).Error parsing TensorProto (expected a tensor type).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Error parsing TensorProto (expected a tensor shape). (truenode). (falsenode).This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).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).The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).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).Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Which axis to gather on. 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 squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).The index of the target that this leaf contributes to (this must be in range `[0, n_targets)`).Constrain type for all input scales (scaleA, scaleB, scaleY). are out of bounds or can not be read in full (>4GB).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).Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).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).Constrain 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).Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).Constrain to tensor(int32). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Input tensors of wrong rank (0). BackUp() can only be called after Next(). Can't back up over more bytes than were returned by the last call to Next().Failed to write value with snprintf().ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().').The expression contained mismatched ( and ).CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.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 18 when reduction is 'max'.The type of the input 'x'.The type of the inputs 'x_zero_point' and 'x'.The shape of the convolution kernel. If not present, should be inferred from input 'w'.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.(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'.'num_return_sequences' has to be smaller or equal to 'num_beams'.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: bool 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 'min'.CPU execution provider: MLFloat16 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 'min'.CPU execution provider: string 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 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: string 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 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterND opset 16 when reduction is 'mul'.'. Must be one of 'forward', 'reverse', or 'bidirectional'.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 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 'add'.The 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'.'. Valid values are 'LEFT' or 'RIGHT'.Thresholds to do the splitting on for each node with mode that is not 'BRANCH_MEMBER'.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'.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.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".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".Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".Pixel format. Can be one of "RGB", "BGR", or "Grayscale".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".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".Conv kernal size 1 does not match conv_window_size attribute . This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote `x_resized` as the coordinate of axis x in the resized tensor, `x_original` as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, `length_resized` as the length of the resized tensor in axis x, `scale = length_resized / length_original`, `output_width` the target length on the axis x which can be a fractional number when it is calculated out of a scale factor, and `output_width_int` the effective output width as an integer. if coordinate_transformation_mode is `"half_pixel"`, ``` x_original = (x_resized + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"half_pixel_symmetric"`, ``` adjustment = output_width_int / output_width center = input_width / 2 offset = center * (1 - adjustment) x_ori = offset + (x + 0.5) / scale - 0.5 ``` if coordinate_transformation_mode is `"pytorch_half_pixel"`, ``` x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0 ``` if coordinate_transformation_mode is `"align_corners"`, ``` x_original = x_resized * (length_original - 1) / (length_resized - 1) ``` if coordinate_transformation_mode is `"asymmetric"`, ``` x_original = x_resized / scale ``` if coordinate_transformation_mode is `"tf_crop_and_resize"`, ``` x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1) ``` . This attribute describes how to 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)` .operator-session--in- is not in valid range [-operator--operator,. Value must be in range [0,),"ph" : "X",operator+a+operator++operator*operator->*::*, 4**/_ORT_MEM_ADDR_/*operator.* **************************** FATAL ERROR DETECTED ****************************zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })std::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })tensor(float8e4m3fnuz)tensor(float8e5m2fnuz)Input to 'Range' op should be scalars (Tensor with only one element and shape empty)static void onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverity(Severity)The mask index tensor type for shape inference (0: None, 1: 1D mask_index)int &onnxruntime::PlannerImpl::UseCount(OrtValueIndex)OrtValueIndex &onnxruntime::PlannerImpl::Buffer(OrtValueIndex)AllocPlanPerValue &onnxruntime::PlannerImpl::AllocPlan(OrtValueIndex)void onnxruntime::StreamExecutionContext::RecycleNodeInputs(onnxruntime::NodeIndex)bool onnxruntime::Graph::RemoveNode(NodeIndex)const KernelCreateInfo &onnxruntime::GetKernelCreateInfo(const KernelCreateInfoMap &, NodeIndex)num_explicit_inputs == static_cast(target_input_idx)optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)virtual void onnxruntime::ApiGraph::CopyValueInfo(std::string_view, std::string_view)const OrtDevice &onnxruntime::utils::FindDeviceForValue(const OrtValueNameIdxMap &, const SequentialExecutionPlan &, std::string_view)const OrtDevice &onnxruntime::utils::FindDeviceForValue(const SessionState &, std::string_view)InvStdDev2D = Reciprocal (StdDev)Normalized = Div (Deviation, StdDev)NormalizedU = Div (Deviation, StdDev)!IsNonTensor(*node_output)x_shape = Gather (x_shape_alldims, axes_input)output_data = Slice (padded_input, start_dims, end_dims, axes_input)x_shape2 = Gather (x_shape_alldims2, axes_input)padded_input = Pad (input_data, pads, , axes_input)x_shape_alldims2 = Shape (padded_input)x_shape2 = Shape (padded_input)output = Identity (input)output = Cast (input), saturate: int = @saturate> (input)X_random = RandomUniformLike (input)X_ReduceMax = ReduceMax (input)input_large_than_max = Less (max, input)output = Where (input_large_than_max, max, input)output = Where (input_less_than_min, min, input)tmp = Where (input_less_than_min, min, input)X_greater = Greater (X_random, input) inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)IsScalarOr1ElementVector(a_zero_point)IsScalarOr1ElementVector(Y_zero_point)IsScalarOr1ElementVector(X_zero_point)IsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)Tensor *onnxruntime::OpKernelContext::Output(int)virtual OrtValue *onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)void onnxruntime::ExecutionFrame::TraceFree(int)KernelDefBuilder &onnxruntime::KernelDefBuilder::VariadicAlias(int, int)void onnxruntime::Graph::RemoveEdge(NodeIndex, NodeIndex, int, int)void onnxruntime::Graph::AddEdge(NodeIndex, NodeIndex, int, int)onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, Severity, bool, const InstanceType, const std::string *, int)onnxruntime::InferenceSession::InferenceSession(const SessionOptions &, const Environment &, const void *, int)void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map &, const logging::Logger &, bool, const std::string &, int)onnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)const std::string &onnxruntime::graph_utils::GetNodeOutputName(const Node &, int)const std::string &onnxruntime::graph_utils::GetNodeInputName(const Node &, int)static std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutput(const Graph &, const Node &, int)static std::optional onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNode(const Graph &, const Node &, int)void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(ThreadPoolEvent)void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(ThreadPoolEvent)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)std::all_of(processor_str.begin(), processor_str.end(), ::isdigit)std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)pads = Concat (pad_amount_left, pad_amount_right)pad_amount_right = Sub(pad_amount, pad_amount_left)IsScalarOr1ElementVector(y_offset)IsScalarOr1ElementVector(a_offset)weight_gather = Gather (weight, target)const_zero_casted = Cast (const_zero_float)const_one_casted = Cast (const_one_float)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)sparse_tensor(float)map(string, float)map(int64, float)const Tensor &(anonymous namespace)::GetIndicesTensor(const OrtValue &, OrtSparseIndicesFormat)void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, Tensor &, ptrdiff_t, ptrdiff_t)void onnxruntime::InputBroadcaster::AdvanceBy(size_t)Status onnxruntime::PlannerImpl::ComputeSingleStreamReusePlan(size_t)Stream *onnxruntime::StreamExecutionContext::GetDeviceStream(size_t)virtual void *onnxruntime::BFCArena::Reserve(size_t)void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)SparseTensor::CooMutator onnxruntime::SparseTensor::MakeCooData(size_t, size_t)SparseTensor::CsrMutator onnxruntime::SparseTensor::MakeCsrData(size_t, size_t, size_t)void onnxruntime::BFCArena::SplitChunk(BFCArena::ChunkHandle, size_t)OrtStatusPtr OrtApis::SessionOptionsAppendExecutionProvider(OrtSessionOptions *, const char *, const char *const *, const char *const *, size_t)void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span &, const uint8_t *, uint8_t *, size_t)OrtStatusPtr OrtApis::FillSparseTensorCsr(OrtValue *, const OrtMemoryInfo *, const int64_t *, size_t, const void *, const int64_t *, size_t, const int64_t *, size_t)OrtStatusPtr OrtApis::FillSparseTensorCoo(OrtValue *, const OrtMemoryInfo *, const int64_t *, size_t, const void *, const int64_t *, size_t)OrtStatusPtr OrtApis::UseCsrIndices(OrtValue *, int64_t *, size_t, int64_t *, size_t)OrtStatusPtr OrtApis::UseCooIndices(OrtValue *, int64_t *, size_t)static void onnxruntime::IAllocator::ValidateAllocation(void *, size_t)OrtStatusPtr OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)void onnxruntime::scan::detail::ReadDirections(const OpKernelInfo &, const std::string &, TensorShapeVector &, size_t)void onnxruntime::RunSince(size_t, StreamExecutionContext &, SessionScope &, const bool &, size_t)void onnxruntime::IncrementIndexAndComputeOffsetSetup(MultiIndex &, size_t, gsl::span, const gsl::span &, size_t)void onnxruntime::ValidateNoTransposeReduce(int64_t)onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, ArenaExtendStrategy, int, int, int, int64_t)onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void *, size_t, int64_t)int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)void onnxruntime::ValidateKeepDims(const TensorShape &, int64_t)onnxruntime::SplitBase::SplitBase(const OpKernelInfo &, uint32_t)void onnxruntime::StreamExecutionContext::CountDownBarrier::Set(int32_t)num_subgraph_outputs == static_cast(num_outputs)output = NegativeLogLikelihoodLoss (X_Log, labels, weights)input_gather_element = GatherElements (input, transform_targets)weight_gather_temp = Gather (weight, transform_targets)GroupSize = Div (C, NumGroups)activations_.size() == static_cast(num_directions)output_data = Slice (padded_input, start_dims, end_dims)std::unique_ptr onnxruntime::concurrency::CreateThreadPoolHelper(Env *, OrtThreadPoolParams)output = NegativeLogLikelihoodLoss (X_Log, labels)scalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)(local_source >= source) && (local_source < source + num_blocks)(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)pads_data.size() == narrow(2 * num_axes)X_ReduceMax = ReduceMax (input, axes)expanded_target = Unsqueeze (target, axes)squeeze_mask = Squeeze (mask, axes)loss = Squeeze (loss_N1dd, axes)loss_unweighted = Squeeze (loss_N1dd, axes)loss_Ndd = Squeeze (loss_N1dd, axes)weight_gather = Squeeze (weight_gather_temp_1, axes)SuffixShape = ConstantOfShape (NumReducedAxes)scores.size() == static_cast(expected_num_scores)Encoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)padded_input = Pad (input_data, pads)BiasT = Cast (bias)X_bias = Add (X, bias) into softmax(input + bias)zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)StreamAwareArena *onnxruntime::AsStreamBasedAllocator(AllocatorPtr)std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const Tensor &, bool, AllocatorPtr)IsScalarOr1ElementVector(a_zero_point_tensor)utils::HasName(sparse_tensor)IsSameDataType(tensor)output = Cast (X_greater)loss = Mul (loss_unweighted, weight_gather)loss_Ndd = Mul (loss_unweighted, weight_gather)'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)void onnxruntime::GraphViewerToProto(const GraphViewer &, onnx::GraphProto &, bool, bool, ExecutionOrder)virtual Status onnxruntime::DeviceBasedPartitioner::PartitionGraph(const onnxruntime::GraphViewer &, const ExecutionProviders &, std::vector> &, ExecutionOrder)output_large_than_max = Less (max, tmp)output = Where (output_large_than_max, max, tmp)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)callnode.TryGetFunctionProto(inlined_fp) decltype(auto)utils::HasDataType(t_proto)!utils::HasExternalData(t_proto)utils::HasDataType(tensor_proto)utils::HasExternalData(tensor_proto)HasDataType(dense_proto)Epsilon = Cast (FloatEpsilon)StdDev = Sqrt (VarPlusEpsilon)VarPlusEpsilon = Add (Var, Epsilon)input_less_than_min = Less (input, min)tensor(float8e4m3fn)Var = Sub (MeanOfSquare, SquareOfMean)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)BFCArena::Chunk *onnxruntime::BFCArena::FindChunkPtr(BinNum, size_t, size_t, Stream *, bool, WaitNotificationFn)void *onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, Stream *, bool, WaitNotificationFn)!c->in_use() && (c->bin_num == kInvalidBinNum)!c->in_use() && (c->bin_num != kInvalidBinNum)loss_NCdd = Neg (input_gather_element_transform)zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)tensor(bool)InlinedVector onnxruntime::utils::SplitString(std::string_view, std::string_view, bool)Status onnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeeds(const Tensor &, const std::vector &, int, int, gsl::span &, OrtValue &, const OrtValue *, std::vector &, const GenerationDeviceHelper::CreateGptInputsFunc &, const GenerationDeviceHelper::AddToFeedsFunc &, IAllocatorUniquePtr &, Stream *, int, bool)Status onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeeds(AllocatorPtr, gsl::span, const std::vector &, const std::vector &, const std::vector &, std::vector &, const GenerationDeviceHelper::DeviceCopyFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, int, Stream *, bool, int, transformers::Sequences &, int, bool)Status onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeeds(AllocatorPtr, gsl::span, const std::vector &, const std::vector &, const std::vector &, std::vector &, const GenerationDeviceHelper::DeviceCopyFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, const GenerationDeviceHelper::ExpandBufferFunc &, int, Stream *, bool, int, transformers::Sequences &, int, bool)void onnxruntime::Initializer::scale_by_axis(const Initializer &, int, bool)onnxruntime::scan::detail::Info::Info(const Node &, const GraphViewer &, int, bool)void onnxruntime::contrib::(anonymous namespace)::BuildLookupTableIfFixed(const OpKernelInfo &, std::vector &, size_t, bool)Status onnxruntime::Graph::ReplaceInitializedTensorImpl(onnx::TensorProto, bool)Status onnxruntime::discrete_fourier_transform(OpKernelContext *, int64_t, bool, bool)Status onnxruntime::MatMulComputeHelper::Compute(const TensorShape &, const TensorShape &, bool, bool, bool, bool, bool)onnxruntime::LayerNormImpl::LayerNormImpl(const OpKernelInfo &, bool, bool)bool onnxruntime::CommonFastReduceCopy(OpKernelContext *, TensorShapeVector &, bool)onnxruntime::ReshapeHelper::ReshapeHelper(const TensorShape &, TensorShapeVector &, bool)onnxruntime::Graph::Graph(const Model &, GraphProto *, const std::unordered_map &, Version, IOnnxRuntimeOpSchemaCollectionPtr, Graph *, const Node *, const logging::Logger &, bool)std::unique_ptr onnxruntime::function_utils::CreateSchema(const std::string &, const std::string &, const std::unordered_map &, const std::unordered_map &, const SchemaRegistryManager &, const logging::Logger &, bool)onnxruntime::ConcatBase::ConcatBase(const OpKernelInfo &, bool)onnxruntime::contrib::AttentionBase::AttentionBase(const OpKernelInfo &, bool)onnxruntime::SliceBase::SliceBase(const OpKernelInfo &, bool)std::unique_ptr onnxruntime::function_utils::CreateSchema(const Graph &, const IndexedSubGraph &, bool)int onnxruntime::graph_utils::GetIndexFromName(const Node &, const std::string &, bool)Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string &, const KernelRegistryManager &, const Node *, const SessionOptions &, bool, InlinedHashMap &, const InlinedHashMap &, bool)(null)number of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)IsAxisInRange(axis, tensor_rank)axis must be in [-rank, rank)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)IsScalarOr1ElementVector(k)Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Input 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'query' and 'key' shall have same dim 1 (sequence length)Input 'query' and 'value' shall have same dim 1 (sequence length)'Scale' must contain exactly 2 values - (height, width)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)log_prob = Identity (X_Log)!graph.GetInitializedTensor(new_initializer.name(), existing)Input of tensor(int64) must have output of tensor(string)map(int64, string)alpha == 1.0f && (beta == 0.0f || beta == 1.0f)(local_source >= source) && (local_source < source + num_blocks * blocksize)Input 'query' and 'key' shall have same dim 2 (hidden_size)DepthToSpace requires input depth to be a multiple of (block_size * block_size)Input 'query' and 'value' shall have same dim 0 (batch_size)Custom scale will be used if specified. Default value is 1/sqrt(head_size)Corresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)Corresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Corresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)Input 'query' and 'key' shall have same dim 0 (batch size)Input 'query' and 'value' shall have same dim 0 (batch size)utils::HasName(attribute)MeanOfSquare = ReduceMean (Square)dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)IsOptionalSeqTensor(type)IsOptionalTensor(type)(unknown type)void *onnxruntime::Tensor::MutableDataRaw(MLDataType)MLDataType onnxruntime::utils::GetElementTypeFromOptionalSeqTensor(MLDataType)MLDataType onnxruntime::utils::GetElementTypeFromOptionalTensor(MLDataType)onnxruntime::utils::ContainerChecker::ContainerChecker(MLDataType)void onnxruntime::TensorSeq::SetType(MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(MLDataType)PadValue onnxruntime::PadValueFromFloat(float, MLDataType)pad_amount = Sub(padded_sh, x_shape)end_dims = Add (start_dims, shape)padded_sh = Max(x_shape, shape)sh_diff = Sub (x_shape2, shape)ReducedShape = Concat (PrefixShape, SuffixShape)XReshaped = Reshape (X, NewShape)XReshaped = Reshape (XU, NewShape)BiasReshaped = Reshape (BiasT, ScaleShape)ScaleReshaped = Reshape (ScaleT, ScaleShape)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)InvStdDev = Reshape (InvStdDev2D, ReducedShape)Mean = Reshape (Mean2D, ReducedShape)Rank = Size (XShape)Y = Reshape (Biased, XShape)NormalizedOriginalShape = Reshape (NormalizedU, XShape)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)void onnxruntime::BFCArena::InsertFreeChunkIntoBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::FreeAndMaybeCoalesce(BFCArena::ChunkHandle)void onnxruntime::BFCArena::Merge(BFCArena::ChunkHandle, BFCArena::ChunkHandle)BFCArena::Chunk *onnxruntime::BFCArena::ChunkFromHandle(ChunkHandle)BFCArena::ChunkHandle onnxruntime::BFCArena::Coalesce(ChunkHandle)sparse_tensor(double)map(string, double)map(int64, double)IsScalarOr1ElementVector(tensor_y_scale)IsScalarOr1ElementVector(y_scale)tensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)IsScalarOr1ElementVector(tensor_c_scale)IsScalarOr1ElementVector(tensor_b_scale)IsScalarOr1ElementVector(tensor_a_scale)IsScalarOr1ElementVector(a_scale)IsScalarOr1ElementVector(Y_scale)IsScalarOr1ElementVector(X_scale)ScaleT = Cast (scale)Scale2D = Flatten (Scale)(cannot determine missing fields for lite message)SetOpSchemaFromRegistryForNode(fused_node)common::Status onnxruntime::IOBinding::BindOutputImpl(const std::string &, const OrtValue &, OrtDevice)(anonymous namespace)void onnxruntime::PlannerImpl::Reuse(OrtValueIndex, OrtValueIndex, AllocKind)The id of the token that indicates decoding starts (i.e. the start of transcription token id)NormalizedV = Cast (Normalized)NormalizedT = Cast (Normalized)Scaled = Mul (ScaleReshaped, Normalized)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)index < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)Biased = Add (Scaled, BiasReshaped)Biased = Identity (Scaled)loss = ReduceMean (loss_Ndd)loss = ReduceSum (loss_Ndd)(?HaveMatch:%d)Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)BinForSize(bin_size) == BinFromIndex(b)pred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)x_shape_alldims = Shape (input_data)x_shape = Shape (input_data)arg_num < static_cast(input_count_)beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)(coor.node_id >= 0 && coor.node_id < n_nodes_)Status::OK() == info.GetAttr("block_size", &block_size_)utils::IsPrimitiveDataType(dtype_)Status::OK() == info.GetAttr("quant_type", &quant_type_)IsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)Status::OK() == info.GetAttr("N", &N_)Status::OK() == info.GetAttr("K", &K_)(?-m:^)The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)X_bias = Identity (X)XU = Cast (X)X2D = Flatten (X)XShape = Shape (X)InstanceShape = Shape (X)C = Shape (X)N = Shape (X)XShape = Shape (XU)Mean2D = ReduceMean (XU)Square = Mul (XU, XU)Scaled = Mul (ScaleReshaped, NormalizedT)IsValidQuantParam(W_zero_point, M)IsValidQuantParam(W_scale, M)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)code != static_cast(common::OK)Need output the cross attention MatMul(Q, K)The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)NormalizedNC = Reshape (NormalizedOriginalShape, Shape3D)X3D = Reshape(XReshaped, Shape3D)X3D = Reshape (XReshaped, Shape3D)Square = Mul (X3D, X3D)Deviation = Sub (XU, Mean2D)SquareOfMean = Mul (Mean2D, Mean2D)Scaled = Mul (NormalizedV, Scale2D)Scaled = Mul (NormalizedT, Scale2D)XU = Cast (X2D)Biased = Add (Scaled, B2D)NumReducedAxes = Neg (Axis1D)NumReducedAxes = Sub (Rank, Axis1D)PrefixShape = Slice (XShape, Zero1D, Axis1D)NormalizedT = Cast (NormalizedNC)B2D = Flatten (B)std::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer &, const IExecutionProvider::IKernelLookup &, gsl::span)Status onnxruntime::Graph::AddConstantProtoAsInitializer(const onnx::NodeProto &, std::optional)void onnxruntime::Graph::SetInputs(gsl::span)onnxruntime::IExecutionFrame::IExecutionFrame(const OrtValueNameIdxMap &, const NodeIndexInfo &, gsl::span)void onnxruntime::Tensor::Init(MLDataType, const TensorShape &, void *, AllocatorPtr, ptrdiff_t, gsl::span)onnxruntime::Tensor::Tensor(MLDataType, const TensorShape &, void *, std::shared_ptr, ptrdiff_t, gsl::span)onnxruntime::Tensor::Tensor(MLDataType, const TensorShape &, void *, const OrtMemoryInfo &, ptrdiff_t, gsl::span)onnxruntime::Broadcaster::Broadcaster(gsl::span, gsl::span)void onnxruntime::SliceIteratorBase::Init(gsl::span, gsl::span, gsl::span)onnxruntime::SliceSkips::SliceSkips(const TensorShape &, gsl::span, gsl::span)onnxruntime::Tensor::Tensor(MLDataType, const TensorShape &, std::shared_ptr)common::Status onnxruntime::utils::CopyInputsAcrossDevices(const SessionState &, gsl::span, std::vector &, DeviceStreamCollection *, gsl::span)void onnxruntime::SelectorActionRegistry::RegisterSelectorAndAction(const std::string &, const OpVersionsMap &, std::unique_ptr, std::unique_ptr)void onnxruntime::DeviceStreamCollectionImpl::AddDeviceStream(size_t, std::unique_ptr)Status onnxruntime::SessionOptions::AddExternalInitializers(gsl::span, gsl::span)void onnxruntime::IExecutionFrame::Init(gsl::span, gsl::span, const std::unordered_map &, const std::function &, gsl::span)void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string &, std::unique_ptr)onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)Status onnxruntime::SessionOptions::AddExternalInitializersFromFilesInMemory(gsl::span, gsl::span>)Status onnxruntime::InferenceSession::Run(const RunOptions &, gsl::span, gsl::span, gsl::span, gsl::span)Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, const Path &, gsl::span, std::function)(?:)tensor(uint8)tensor(int8)tensor(complex128)tensor(uint16)tensor(int16)tensor(bfloat16)tensor(float16)tensor(uint4)tensor(int4)tensor(complex64)sparse_tensor(uint64)sparse_tensor(int64)Input of tensor(string) must have output of tensor(int64)map(string, int64)number of bits used for weight quantization (default 4)MeanOfSquare = ReduceMean (Square, Axes2)Mean = ReduceMean (X3D, Axes2)tensor(float8e5m2)pad_amount_left = Div(pad_amount, k2)start_dims = Div (sh_diff, k2)sparse_tensor(uint32)sparse_tensor(int32)MeanOfSquare = ReduceMean (Square, Axes_1)Mean2D = ReduceMean (XU, Axes_1)Defines how to aggregate leaf values within a target.
One of 'AVERAGE' (0) 'SUM' (1) 'MIN' (2) 'MAX (3) defaults to 'SUM' (1)(fmod == 0) || (fmod == 1)pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)ratio must be in the range [0, 1)outer_num == (rows + 1)The output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)Indicates the transform to apply to the score.
One of 'NONE' (0), 'SOFTMAX' (1), 'LOGISTIC' (2), 'SOFTMAX_ZERO' (3) or 'PROBIT' (4), defaults to 'NONE' (0)Base values for regression, added to final prediction after applying aggregate_function; the size must be the same as the classes or can be left unassigned (assumed 0)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)(items % ngram_size == 0)OrtStatusPtr OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)OrtStatus *OrtCreateValueImplSeqHelper(const OrtValue *const *, size_t, OrtValue **)OrtStatusPtr OrtApis::CreateOpaqueValue(const char *, const char *, const void *, size_t, OrtValue **)static void OrtEnv::Release(OrtEnv *)void onnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputs(OpKernelContext *)void onnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputs(OpKernelContext *)void onnxruntime::contrib::transformers::SamplingParameters::ParseFromInputs(OpKernelContext *)bool onnxruntime::math::NextPosition(int64_t, const int64_t *, int64_t *)OrtStatusPtr OrtApis::FillSparseTensorBlockSparse(OrtValue *, const OrtMemoryInfo *, const int64_t *, size_t, const void *, const int64_t *, size_t, const int32_t *)OrtStatusPtr OrtApis::UseBlockSparseIndices(OrtValue *, const int64_t *, size_t, int32_t *)virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Finalize(ISequences &, gsl::span &, Tensor *, Tensor *)virtual void onnxruntime::contrib::transformers::BeamSearchScorer::OutputScores(gsl::span &, Tensor *)void onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)onnxruntime::common::Status::Status(StatusCategory, int, const char *)SparseTensor &(anonymous namespace)::ValidateFillInputArgs(OrtValue *, const TensorShape &, const OrtMemoryInfo *)void onnxruntime::DeviceStreamCollectionImpl::SetDeviceStream(size_t, Stream *)void onnxruntime::utils::UpdateWithParentStream(DeviceStreamCollection &, Stream *)onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const SessionState &, IExecutionFrame &, const OpKernel &, const logging::Logger &, const bool &, Stream *)Status onnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeeds(const Tensor &, const OrtValue *, int, const std::vector &, std::vector &, const GenerationDeviceHelper::CreateWhisperEncoderInputsFunc &, const GenerationDeviceHelper::AddToFeedsFunc &, IAllocatorUniquePtr &, OrtValue &, Stream *)Status onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeeds(const Tensor &, const OrtValue *, const std::vector &, int, int, std::vector &, const GenerationDeviceHelper::CreateEncoderInputsFunc &, const GenerationDeviceHelper::AddToFeedsFunc &, IAllocatorUniquePtr &, OrtValue &, Stream *)void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)void onnxruntime::TransposeSingleAxisOutwards(gsl::span, const Tensor &, Tensor &, size_t, size_t, const TensorShape *, concurrency::ThreadPool *)onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(ThreadPool *)void onnxruntime::math::Gemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, ptrdiff_t, ptrdiff_t, ptrdiff_t, double, const double *, const double *, double, double *, ThreadPool *)onnxruntime::GraphViewer::GraphViewer(const Graph &, const IndexedSubGraph *)void onnxruntime::PlannerImpl::ProcessDef(OrtValueIndex, const onnxruntime::NodeArg *)void onnxruntime::DoTransposeImpl(int64_t, gsl::span, size_t, size_t, const gsl::span &, const std::string *, std::string *)void onnxruntime::DoTransposeEltWise(int64_t, gsl::span, size_t, const gsl::span &, const std::string *, std::string *)Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue &, int, const TensorShape *)void *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void *)void onnxruntime::BFCArena::DeallocateRawInternal(void *)std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const Tensor &, int64_t, int64_t, AllocatorPtr, void *)Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const Tensor &, Tensor &, void *)Status onnxruntime::utils::BatchOrCopyMLValue(const SessionState &, const MLValueCopyInfo &, const OrtValue &, OrtValue &, Stream *, std::vector *, std::vector *)void onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs(const std::unordered_set *)seq(tensor(float8e4m3fnuz))optional(tensor(float8e4m3fnuz))seq(tensor(float8e5m2fnuz))optional(tensor(float8e5m2fnuz))seq(tensor(float))optional(tensor(float))seq(map(string, float))seq(map(int64, float))can only be of type(tensor) or (seq(tensor))static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o))seq(tensor(float8e4m3fn))optional(tensor(float8e4m3fn))seq(tensor(bool))optional(tensor(bool))seq(tensor(string))optional(tensor(string))seq(tensor(double))optional(tensor(double))!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))seq(tensor(uint8))optional(tensor(uint8))seq(tensor(int8))optional(tensor(int8))seq(tensor(complex128))optional(tensor(complex128))seq(tensor(uint16))optional(tensor(uint16))seq(tensor(int16))optional(tensor(int16))seq(tensor(bfloat16))optional(tensor(bfloat16))seq(tensor(float16))optional(tensor(float16))seq(tensor(uint4))optional(tensor(uint4))seq(tensor(int4))optional(tensor(int4))seq(tensor(complex64))optional(tensor(complex64))seq(tensor(uint64))optional(tensor(uint64))seq(tensor(int64))optional(tensor(int64))seq(tensor(float8e5m2))optional(tensor(float8e5m2))seq(tensor(uint32))optional(tensor(uint32))seq(tensor(int32))optional(tensor(int32))optional(seq(tensor(float)))optional(seq(tensor(bool)))optional(seq(tensor(string)))optional(seq(tensor(double)))optional(seq(tensor(uint8)))optional(seq(tensor(int8)))optional(seq(tensor(complex128)))optional(seq(tensor(uint16)))optional(seq(tensor(int16)))optional(seq(tensor(bfloat16)))optional(seq(tensor(float16)))optional(seq(tensor(complex64)))optional(seq(tensor(uint64)))optional(seq(tensor(int64)))optional(seq(tensor(uint32)))optional(seq(tensor(int32)))!(it.GetName().empty())graph.RemoveNode(sum_node.Index())graph.RemoveNode(gemm_node.Index())nodes_.size() < static_cast(std::numeric_limits::max())perm.size() == gsl::narrow_cast(shape_proto->dim_size())rank == static_cast(new_shape_proto.dim_size())offset + size <= size_t(span.size())utils::HasElemType(thisProto->sparse_tensor_type())utils::HasElemType(thisProto->tensor_type())utils::HasKeyType(thisProto->map_type())utils::HasElemType(thisProto->optional_type())utils::HasElemType(thisProto->sequence_type())ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())IsSameDataType(tensor.Get())!separators.empty()!node_consumers.empty()tmp_cats_int64s.empty() || tmp_cats_strings.empty()!pool_strings.empty()axes.empty()base_values.empty()status.IsOK() && !pool_int64s.empty()!to.affinity_str.empty()target_class_weights.empty() || target_class_weights_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()base_values.empty() || base_values_as_tensor.empty()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()!delimiter.empty()!tokenexp.empty()output_names_to_nodeinfo.empty()!op_type.empty()dst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()outer_scope_node_args_consumed.empty()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()status.IsOK() && !impl_->ngram_counts_.empty()!points_.empty()!coefficients_.empty()dilations_.empty()status.IsOK() && !input_dimensions_.empty()status.IsOK() && !impl_->ngram_indexes_.empty()strides_.empty()pads_.empty()classlabels_strings_.empty() ^ classlabels_int64s_.empty()!provider_type_.empty()!scale_.empty()imputed_values_float_.empty() ^ imputed_values_int64_.empty()void onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()right[i] <= std::numeric_limits::max()left[i] <= std::numeric_limits::max()input_size < std::numeric_limits::max()v >= 0 && static_cast(v) <= std::numeric_limits::max()len >= 0 && static_cast(len) < std::numeric_limits::max()axes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()nodes_modes.size() < std::numeric_limits::max()static void SafeIntExceptionHandler::SafeIntOnOverflow()void onnxruntime::scan::detail::LoopStateVariable::Next()Status onnxruntime::ScanImpl::TransposeOutput()::onnxruntime::TimePoint onnxruntime::profiling::Profiler::Start()Axis1D = Constant()Zero1D = Constant()Axes_1 = Constant()std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()env_ptr == p_instance_.get()bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet()Provider &onnxruntime::ProviderLibrary::Get()!helper.HaveTwoTensorInputs()input->Exists()i < input_shape.NumDimensions()Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()idx < device_stream_map_->NumStreams()session_env.EnvCreatedWithGlobalThreadPools()output.SizeInBytes() == input.SizeInBytes()src.SizeInBytes() == dst.SizeInBytes()operator()in[idx]->IsTensor()ort_value.IsTensor()last_outputs[j + 1].IsTensor()IsSparseTensor()static const Logger &onnxruntime::logging::LoggingManager::DefaultLogger()max->Shape().IsScalar()axis_tensor->Shape().IsScalar()min->Shape().IsScalar()attr->has_tp()std::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()static void SafeIntExceptionHandler::SafeIntOnDivZero()Status onnxruntime::PlannerImpl::ComputeValueLocation()external_stream.is_open()re_.ok()dtype_attribute->second.has_i()Cached EP instance for graph replay is not set yet before calling ReplayGraph()0 <= p && p_int < shape_proto->dim_size()%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()shape_size == out.size()feeds.size() == feed_mlvalue_idxs.size()node_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()index >= 0 && static_cast(index) < outputs.size()static_cast(first_arg_idx) < actual_inputs.size()static_cast(num_subgraph_inputs) == subgraph_inputs.size()node_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()index >= 0 && static_cast(index) < inputs.size()dims.size() == starts.size()arg_num < arg_counts.size()num_files == files_buffers.size()dims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()dims.size() == extents.size() && dims.size() >= steps.size()dims.size() == steps.size()it->i < (int64_t)predictions.size()this->base_values_.size() == predictions.size()next_scores.size() == next_tokens.size()axes should be less than output_dims.size()indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()actuals.size() <= formals.size()idx < input_defs.size()init_num == values.size()keys.size() == values.size()target.size() == final_scores.size()num_entries == int_categories.size()nodes_falsenodeids.size() == nodes_modes.size()next_scores.size() == next_indices.size()nodes_falsenodeids.size() == nodes_featureids.size()target_class_ids.size() == target_class_treeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()target_class_ids.size() == target_class_nodeids.size()nodes_falsenodeids.size() == nodes_nodeids.size()data_rank * 2 == pads.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()size() == other.size()length > buffer.size()feed_locations.size() == copy_info.size()fetch_alloc_info.size() == copy_info.size()input_rank == permutation.size()n >= 0 && static_cast(n) < plan_.allocation_plan.size()ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()dilations.size() == kernel_shape.size()strides.size() == kernel_shape.size()++index < c.size()scale_.size() == offset_.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()!has_axes || attr_axes_.size() == attr_starts_.size()size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()dims.size() == extents_.size()i < tensors_.size()input_count >= 0 && static_cast(input_count) == input_dimensions_.size()h < chunks_.size()buffers_.size() == buffer_sizes_.size()impl_->weights_.size() == impl_->ngram_indexes_.size()dimstart <= dimend && dimend <= values_.size()node_names_by_stream_.size() == device_types_.size()mapped_output_names_.size() == output_names_.size()mapped_feed_names_.size() == feed_names_.size()node_index < nodes_.size()has_starts && has_ends && attr_starts_.size() == attr_ends_.size()static_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()n >= 0 && static_cast(n) < ort_value_info_.size()id >= 0 && static_cast(id) < ort_value_info_.size()strides_.size() == kernel_shape_.size()proba_.size() == probb_.size()index < data_.size()predictions.size() == predictions2.size()static_cast(i) < node_op_schema->inputs().size()fetches.size() == node->OutputDefs().size()dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()void onnxruntime::DeviceBasedPartitioner::Initialize()shape_.Size() == new_shape.Size()candidate_output.Shape().Size() == output_shape.Size()right.Shape().Size() == right_shape_override.Size()left.Shape().Size() == left_shape_override.Size()fast_shape[0] * fast_shape[2] == output.Shape().Size()fast_shape[1] == output.Shape().Size()fast_shape[0] == output.Shape().Size()inner_num == src.Values().Shape().Size()!current_parallel_section.has_value()since_version.has_value()node_index_info_.has_value()!chunk->in_use()!c->in_use()void onnxruntime::ProviderSharedLibrary::Ensure()data_type() == other.data_type()tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()dst.DataType() == src.DataType()input_1.DataType() == input_2.DataType()execution_plan[plan_.node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()onnxruntime::SparseTensor *OrtValue::GetMutable()onnxruntime::Tensor *OrtValue::GetMutable()onnxruntime::TensorSeq *OrtValue::GetMutable()gsl::not_null onnxruntime::Graph::AllocateNode()IsTensorSequence()existing_entries.find(attribute_name) == existing_entries.cend()entry != kernel_create_info_map.cend()entry != node_to_subgraph_ss.second.cend()entry != kernel_create_info_map_.cend()existing_entry != mutable_initializers.pointer_end()it != opset_imports.end()trigger_point_it != node_to_trigger_points.end()dup_replacements.find(&arg) == dup_replacements.end()subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()allowed_directions.find(direction_) != allowed_directions.end()allowed_activations.find(activations_[direction]) != allowed_activations.end()itr != node_args.end()opset_import_iter != domain_to_version_map.end()iter != node_stream_map.end()entry != initialized_tensors_to_allocate.end()cur_index == indices_data.end()buffers_.find(location) == buffers_.end()specific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()entry != regions_.end()reserved_chunks_.find(ptr) == reserved_chunks_.end()ort_value.IsAllocated()void onnxruntime::ValidateMustBeOverloaded()ProviderInfo_TensorRT &onnxruntime::GetProviderInfo_TensorRT()result.IsOK()status.IsOK()info.GetAttr("keepdims", &keepdims).IsOK()info.GetAttr("batch_axis", &batch_axis).IsOK()info.GetAttr("time_axis", &time_axis).IsOK()info.GetAttr("cast_to", &attr).IsOK()info.GetAttr("map_form", &attr).IsOK()info.GetAttr("storage_order", &storage_order).IsOK()op_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("axis", &axis_temp).IsOK()op_kernel_info.GetAttr("bias", &bias_temp).IsOK()op_kernel_info.GetAttr("k", &k_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()info.GetAttr("upper", &temp).IsOK()info.GetAttr("count_include_pad", &temp).IsOK()info.GetAttr("transB", &temp).IsOK()info.GetAttr("transA", &temp).IsOK()info.GetAttr("body", &proto).IsOK()info.GetAttr("encoder", &proto).IsOK()info.GetAttr("decoder", &proto).IsOK()info.GetAttr("then_branch", &proto).IsOK()info.GetAttr("else_branch", &proto).IsOK()info.GetAttr("direction", &direction).IsOK()info.GetAttr("norm", &norm).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()info.GetAttr("size", &size).IsOK()info.GetAttr("linear_before_reset", &int64_value).IsOK()info.GetAttr("dtype", &dtype).IsOK()info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttrs("shape", shape).IsOK()info.GetAttr("mode", &mode).IsOK()info.GetAttr("metric", &metric).IsOK()info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()info.GetAttr("low", &low_).IsOK()info.GetAttr("default_int64", &default_int_).IsOK()info.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()info.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()info.GetAttrs("activations", activations_).IsOK()op_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("axis", &axis_).IsOK()info.GetAttrs("axes", axes_).IsOK()info.GetAttr("sample_size", &num_samples_).IsOK()info.GetAttr("max_map", &max_map_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()info.GetAttr("direction", &direction_).IsOK()GetFusedActivationAttr(info, activation_).IsOK()info.GetAttr("equation", &equation_).IsOK()info.GetAttr("mean", &mean_).IsOK()info.GetAttr("high", &high_).IsOK()info.GetAttr("default_string", &default_string_).IsOK()info.GetAttr("blocksize", &blocksize_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()info.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()info.GetAttr("ngram_size", &ngram_size_).IsOK()info.GetAttr("spatial_scale", &spatial_scale_).IsOK()info.GetAttr("scale", &scale_).IsOK()info.GetAttr("scale", &scale_).IsOK()info.GetAttr("beta", &beta_).IsOK()info.GetAttr("beta", &beta_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("alpha", &alpha_).IsOK()frame_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()tensor_z_scale->IsDataType()tensor_y_scale->IsDataType()tensor_x_scale->IsDataType()axes_input = Constant ()element_type == DataTypeImpl::GetType()output_scores->IsDataType()output_sequence_scores->IsDataType() is not implemented in CreateAndRegisterAllocatorV2()OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()OrtValue &onnxruntime::scan::detail::OutputIterator::operator*()Shape3D = Constant ()ScaleShape = Constant ()void onnxruntime::TensorSeq::Add(Tensor &&)void onnxruntime::TensorSeq::Add(OrtValue &&)bool onnxruntime::IsIsolatedFp16NodeOnCpu(const onnxruntime::Node &, onnxruntime::Graph &, const KernelRegistry &)Status onnxruntime::PlannerImpl::BuildExecutionPlan(const ExecutionProviders &, const IStreamCommandHandleRegistry &)onnxruntime::TensorAllocator::TensorAllocator(OpKernelContext &)static Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(OpKernelContext *, PrepareContext &)OrtShapeInferContext::OrtShapeInferContext(onnx::InferenceContext &)void onnxruntime::InferOutputTypes(const onnx::OpSchema &, gsl::span, onnx::InferenceContext &)void onnxruntime::TopkOpset9ConstructorCommon(const OpKernelInfo &, int &, unsigned int &)void onnxruntime::TopkOpset10ConstructorCommon(const OpKernelInfo &, int &)void onnxruntime::InferenceSession::ConstructorCommon(const SessionOptions &, const Environment &)bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const KernelRegistryManager &, const InitializedTensorSet &)void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const KernelRegistryManager &, InitializedTensorSet &)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 &)bool onnxruntime::GetQConstantLowerUpper(const Graph &, const Node &, float &, float &)Status onnxruntime::string_normalizer::Utf8ConverterGeneric::ComputeRequiredSizeToWideChar(const std::string &, size_t &)void onnxruntime::PrepareForQDQ(const TensorShape &, const Tensor &, const Tensor *, int64_t, int64_t &, int64_t &, int64_t &)void onnxruntime::AccumulateAllNestedSubgraphsInfo(const SessionState &, const std::string &, size_t, SubgraphsKernelCreateInfoMaps &)logging::Severity onnxruntime::GetSeverity(const SessionOptions &)Status onnxruntime::FinalizeSessionOptions(const SessionOptions &, const onnx::ModelProto &, bool, SessionOptions &)onnxruntime::Model::Model(ModelProto &&, const PathString &, const IOnnxRuntimeOpSchemaRegistryList *, const logging::Logger &, const ModelOptions &)Status onnxruntime::Graph::Resolve(const ResolveOptions &)onnxruntime::(anonymous namespace)::PosixThread::PosixThread(const char *, int, unsigned int (*)(int, Eigen::ThreadPoolInterface *), Eigen::ThreadPoolInterface *, const ThreadOptions &)std::pair onnxruntime::utils::SetNodeAttribute(AttributeProto, NodeAttributes &)void onnxruntime::UntypedExpand(OpKernelContext &, const ProcessBroadcastSpanFuncs &)void onnxruntime::ExpandBroadcastLooper(BroadcastHelper &, const ProcessBroadcastSpanFuncs &)static void onnxruntime::PadBase::ComputePads(OpKernelContext &, size_t, gsl::span, PadsVector &)static TensorShapeVector onnxruntime::SqueezeBase::ComputeOutputShape(const TensorShape &, const TensorShapeVector &)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(BFCArena::Bin::FreeChunkSet *, const BFCArena::Bin::FreeChunkSet::iterator &)size_t onnxruntime::fbs::utils::GetSizeInBytesFromFbsTensor(const fbs::Tensor &)int64_t onnxruntime::GetSeqIdx(const Tensor &)int64_t onnxruntime::GetScalarSplitInput(const Tensor &)void onnxruntime::ValidateFastReduceKR(const gsl::span &, const Tensor &)void onnxruntime::ValidateFastReduceRKR(const gsl::span &, const Tensor &)void onnxruntime::ValidateFastReduceRK(const gsl::span &, const Tensor &)void onnxruntime::ValidateFastReduceKRK(const gsl::span &, const Tensor &)Status onnxruntime::sparse_utils::SparseCsrToDenseTensor(const DataTransferManager &, const SparseTensor &, const AllocatorPtr &, const AllocatorPtr &, Tensor &)Initializer &onnxruntime::Initializer::div(const Initializer &)Initializer &onnxruntime::Initializer::mul(const Initializer &)Initializer &onnxruntime::Initializer::add(const Initializer &)Initializer &onnxruntime::Initializer::sub(const Initializer &)onnxruntime::Loop::Info::Info(const onnxruntime::Node &, const GraphViewer &)onnxruntime::If::Info::Info(const onnxruntime::Node &, const GraphViewer &)onnxruntime::KernelLookup::KernelLookup(ProviderType, gsl::span>, const IKernelTypeStrResolver &)onnxruntime::OpKernelContext::OpKernelContext(IExecutionFrame *, const OpKernel *, Stream *, concurrency::ThreadPool *, const logging::Logger &)Status onnxruntime::(anonymous namespace)::InsertQDQPair(Graph &, const ExtendedGraphEdge &, NodeArg &, NodeArg *, const std::string &, const logging::Logger &)Status onnxruntime::Graph::InlineIfSubgraph(bool, Node &, const logging::Logger &)bool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const Graph &, const Node &, const std::vector &, const std::vector &, const logging::Logger &)Tensor onnxruntime::CloneTensor(const Tensor &, OpKernelContext *, const DataTransferManager &)onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span, gsl::span, const OrtValueNameIdxMap &)onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(gsl::span, gsl::span, const OrtValueNameIdxMap &)OrtDevice onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const Node &, const KernelCreateInfoMap &)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)onnxruntime::CustomOpKernel::CustomOpKernel(const OpKernelInfo &, const OrtCustomOp &)common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto &, const Path &, onnx::SparseTensorProto &)NodeArg &onnxruntime::graph_utils::AddInitializer(Graph &, const onnx::TensorProto &)Status onnxruntime::ml::GetNumberOfElementsAttrsOrDefault(const OpKernelInfo &, const std::string &, onnx::TensorProto_DataType, size_t &, onnx::TensorProto &)void onnxruntime::Graph::AddInitializedTensor(const TensorProto &)static std::unique_ptr OrtTypeInfo::FromTypeProto(const onnx::TypeProto &)static std::unique_ptr OrtMapTypeInfo::FromTypeProto(const onnx::TypeProto &)static std::unique_ptr OrtSequenceTypeInfo::FromTypeProto(const onnx::TypeProto &)const onnx::TensorShapeProto &onnxruntime::utils::GetShape(const onnx::TypeProto &)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto &, const onnx::TypeProto &)static void onnxruntime::data_types_internal::OptionalTypeHelper::Set(const onnx::TypeProto *, onnx::TypeProto &)static void onnxruntime::data_types_internal::SequenceTypeHelper::Set(const onnx::TypeProto *, onnx::TypeProto &)static void onnxruntime::data_types_internal::MapTypeHelper::Set(onnx::TensorProto_DataType, const onnx::TypeProto *, onnx::TypeProto &)onnxruntime::contrib::QLinearSoftmax::QLinearSoftmax(const OpKernelInfo &)onnxruntime::Trilu::Trilu(const OpKernelInfo &)onnxruntime::(anonymous namespace)::Cast::Cast(const OpKernelInfo &)void onnxruntime::PoolProcessContext::init(const OpKernelInfo &)void onnxruntime::Loop::Init(const OpKernelInfo &)void onnxruntime::contrib::transformers::GreedySearch::Init(const OpKernelInfo &)void onnxruntime::contrib::transformers::BeamSearch::Init(const OpKernelInfo &)void onnxruntime::contrib::transformers::Sampling::Init(const OpKernelInfo &)void onnxruntime::If::Init(const OpKernelInfo &)onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const OpKernelInfo &)onnxruntime::contrib::QLinearConcat::QLinearConcat(const OpKernelInfo &)onnxruntime::GatherElements::GatherElements(const OpKernelInfo &)onnxruntime::contrib::MatMulNBits::MatMulNBits(const OpKernelInfo &)onnxruntime::ConvAttributes::ConvAttributes(const OpKernelInfo &)virtual void onnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributes(const OpKernelInfo &)onnxruntime::contrib::BifurcationDetector::BifurcationDetector(const OpKernelInfo &)onnxruntime::ml::LinearRegressor::LinearRegressor(const OpKernelInfo &)onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const OpKernelInfo &)onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const OpKernelInfo &)onnxruntime::contrib::Tokenizer::Tokenizer(const OpKernelInfo &)onnxruntime::StringNormalizer::StringNormalizer(const OpKernelInfo &)onnxruntime::ml::Normalizer::Normalizer(const OpKernelInfo &)onnxruntime::ml::CategoryMapper::CategoryMapper(const OpKernelInfo &)onnxruntime::ml::LinearClassifier::LinearClassifier(const OpKernelInfo &)onnxruntime::ml::SVMClassifier::SVMClassifier(const OpKernelInfo &)onnxruntime::ml::LabelEncoder::LabelEncoder(const OpKernelInfo &)onnxruntime::ml::CastMap::CastMap(const OpKernelInfo &)onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const OpKernelInfo &)onnxruntime::ml::ImputerOp::ImputerOp(const OpKernelInfo &)onnxruntime::ml::ZipMapOp::ZipMapOp(const OpKernelInfo &)onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const OpKernelInfo &)onnxruntime::ReverseSequenceOp::ReverseSequenceOp(const OpKernelInfo &)onnxruntime::ml::SVMCommon::SVMCommon(const OpKernelInfo &)onnxruntime::Flatten::Flatten(const OpKernelInfo &)onnxruntime::Einsum::Einsum(const OpKernelInfo &)onnxruntime::RandomUniform::RandomUniform(const OpKernelInfo &)onnxruntime::MaxUnpool::MaxUnpool(const OpKernelInfo &)onnxruntime::Optional::Optional(const OpKernelInfo &)onnxruntime::RandomNormal::RandomNormal(const OpKernelInfo &)onnxruntime::Multinomial::Multinomial(const OpKernelInfo &)onnxruntime::Shrink::Shrink(const OpKernelInfo &)onnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlock(const OpKernelInfo &)onnxruntime::RegexFullMatch::RegexFullMatch(const OpKernelInfo &)onnxruntime::GatherBase::GatherBase(const OpKernelInfo &)onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const OpKernelInfo &)onnxruntime::RoiAlignBase::RoiAlignBase(const OpKernelInfo &)onnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBase(const OpKernelInfo &)onnxruntime::GemmBase::GemmBase(const OpKernelInfo &)onnxruntime::SpaceDepthBase::SpaceDepthBase(const OpKernelInfo &)onnxruntime::UnsqueezeBase::UnsqueezeBase(const OpKernelInfo &)onnxruntime::TransposeBase::TransposeBase(const OpKernelInfo &)onnxruntime::UpsampleBase::UpsampleBase(const OpKernelInfo &)onnxruntime::PadBase::PadBase(const OpKernelInfo &)onnxruntime::LSTMBase::LSTMBase(const OpKernelInfo &)onnxruntime::contrib::QLinearWhere::QLinearWhere(const OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const OpKernelInfo &)onnxruntime::RandomNormalLike::RandomNormalLike(const OpKernelInfo &)onnxruntime::DepthToSpace::DepthToSpace(const OpKernelInfo &)onnxruntime::Mod::Mod(const OpKernelInfo &)onnxruntime::contrib::MatMulBnb4::MatMulBnb4(const OpKernelInfo &)onnxruntime::Reshape_1::Reshape_1(const OpKernelInfo &)std::vector> onnxruntime::PlannerImpl::GetAliasMap(const Node &, const KernelCreateInfo &)void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const NodeInfo &)common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const Env &, const std::basic_string &, const GraphViewer &, const AllocatorPtr &, const OrtValueNameIdxMap &, const std::vector &, ITensorAllocator &, const SaveTensorFunction &, const logging::Logger &, const DataTransferManager &, const ExecutionPlanBase &, const SessionOptions &, const MemoryProfileFunction &)bool onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPoint(const Node &, const GetConstantInitializerFn &, bool &)virtual Status onnxruntime::WaitOnEPStep::Execute(StreamExecutionContext &, size_t, SessionScope &, const bool &, bool &)void onnxruntime::TopkOpset11ConstructorCommon(const OpKernelInfo &, int &, bool &, bool &)common::Status onnxruntime::session_state_utils::ExtDataTensorProtoToTensor(const Env &, const std::basic_string &, const onnx::TensorProto &, Tensor &, OrtCallback &)Status onnxruntime::utils::GetExtDataFromTensorProto(const Env &, const char *, const onnx::TensorProto &, void *&, SafeInt &, OrtCallback &)onnxruntime::Initializer::Initializer(const onnx::TensorProto &, const Path &)onnxruntime::FunctionImpl::FunctionImpl(onnxruntime::Graph &, const IndexedSubGraph &)auto onnxruntime::InferenceSession::TransformGraph(onnxruntime::Graph &, bool)::(anonymous class)::operator()(const Graph &)MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)std::optional onnxruntime::(anonymous namespace)::GetScalarConstantInitializer(const Graph &, const NodeArg &)void onnxruntime::graph_utils::ReplaceNodeInput(Node &, int, NodeArg &)void onnxruntime::graph_utils::AddNodeInput(Node &, int, NodeArg &)AspectRatioPolicy onnxruntime::UpsampleBase::StringToKeepAspectRatioPolicy(const std::string &)CAST_TO onnxruntime::ml::MakeCast(const std::string &)std::wstring onnxruntime::string_normalizer::Utf8ConverterGeneric::from_bytes(const std::string &)AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const std::string &)void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string &)Direction onnxruntime::rnn::detail::MakeDirection(const std::string &)PACK_MAP onnxruntime::ml::MakePack(const std::string &)std::vector onnxruntime::concurrency::ReadThreadAffinityConfig(const std::string &)NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)AutoPadType onnxruntime::StringToAutoPadType(const std::string &)LstmMergeGatesFuncPtr onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string &)ActivationFuncPtr onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string &)GruOutputGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string &)GruResetGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string &)ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string &)ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string &)onnxruntime::common::Status::Status(StatusCategory, int, const std::string &)common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, const Path &, onnx::TensorProto &, const std::string &)Node &onnxruntime::Graph::CreateFusedSubGraphNode(const IndexedSubGraph &, const std::string &)void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(Node &, const std::string &, const std::string &)bool onnxruntime::(anonymous namespace)::MatchKernelDefTypes(const Node &, const std::unordered_map> &, const IKernelTypeStrResolver &, std::string &)static Path onnxruntime::Path::Parse(const PathString &)onnxruntime::InferenceSession::InferenceSession(const SessionOptions &, const Environment &, const PathString &)void onnxruntime::PlannerImpl::PartitionIntoStreams(const logging::Logger &, const ExecutionProviders &, const PathString &)static SparseTensor &onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(OrtValue &)static const SparseTensor &onnxruntime::SparseTensor::GetSparseTensorFromOrtValue(const OrtValue &)static std::unique_ptr OrtTypeInfo::FromOrtValue(const OrtValue &)void onnxruntime::TensorSeq::Add(const OrtValue &)common::Status onnxruntime::IOBinding::BindInput(const std::string &, const OrtValue &)Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputs(const Tensor *, const OrtValue *, int, int, AllocatorPtr, OrtValue &, OrtValue &, OrtValue &)Status onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputs(const Tensor *, const OrtValue *, int, int, gsl::span &, AllocatorPtr, OrtValue &, OrtValue &, OrtValue &)onnxruntime::ExecutionFrame::ExecutionFrame(gsl::span, gsl::span, gsl::span, gsl::span, const std::unordered_map &, const DeviceStreamCollection *, const SessionState &)virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)virtual Status onnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)virtual Status onnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)virtual Status onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)virtual common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const SessionState &, const std::string &, const SessionState &)std::unique_ptr onnxruntime::EinsumOp::Transpose(const Tensor &, const TensorShape &, const gsl::span &, AllocatorPtr, void *, const DeviceHelpers::Transpose &)static size_t onnxruntime::PlannerImpl::GetElementSize(const DataType &)onnxruntime::Status onnxruntime::ExecuteKernel(StreamExecutionContext &, NodeIndex, size_t, const bool &, SessionScope &)Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const TensorShape &)void onnxruntime::Tensor::Reshape(const TensorShape &)Tensor &onnxruntime::OpKernelContext::RequiredOutput(int, const TensorShape &)virtual OrtValue *onnxruntime::OpKernelContext::OutputMLValue(int, const TensorShape &)onnxruntime::GemmHelper::GemmHelper(const TensorShape &, bool, const TensorShape &, bool, const TensorShape &)static size_t onnxruntime::Tensor::CalculateTensorStorageSize(MLDataType, const TensorShape &)SparseTensor::BlockSparseMutator onnxruntime::SparseTensor::MakeBlockSparseData(const TensorShape &, const TensorShape &)Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue &, int, MLDataType, const OrtDevice &, const TensorShape &)OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName &)std::optional onnxruntime::(anonymous namespace)::GetNextPropagationEdge(const Graph &, const ExtendedGraphEdge &)std::optional onnxruntime::(anonymous namespace)::GetPreviousPropagationEdge(const Graph &, const ExtendedGraphEdge &)Status onnxruntime::Graph::InlineFunction(Node &)Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(const int, int, const TensorShape *, OrtValue *&, const Node &)void onnxruntime::Graph::FinalizeFuseSubGraph(const IndexedSubGraph &, Node &)bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(Graph &, Node &)bool onnxruntime::graph_utils::RemoveNode(Graph &, Node &)std::unique_ptr (anonymous namespace)::GetDataTransfer(const OrtDevice &, const OrtDevice &)int onnxruntime::GetSinceVersionForNewOp(std::string_view, std::string_view, const std::unordered_map &)bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const gsl::span &)size_t onnxruntime::UpdateConsumerCount(Graph &, NodeArg *, InlinedHashMap &)virtual void onnxruntime::ApiValueInfo::PermuteDims(const std::vector &)virtual void onnxruntime::ApiGraph::TransposeInitializer(std::string_view, const std::vector &)virtual void onnxruntime::ApiGraph::ReshapeInitializer(std::string_view, const std::vector &)bool onnxruntime::GetTransposePerms(const Node &, std::vector &)void onnxruntime::GetSplitSizesInput(const Tensor &, InlinedVector &)virtual void onnxruntime::contrib::transformers::BeamSearchScorer::Process(ISequences &, gsl::span &, gsl::span &, gsl::span &)const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions &, std::unique_ptr &)bool onnxruntime::utils::FinalizeCopyInfoForFeeds(gsl::span, std::vector &)bool onnxruntime::utils::FinalizeCopyInfoForFetches(gsl::span &, std::vector &)InlinedVector> onnxruntime::optimizer_utils::GenerateTransformersForMinimalBuild(TransformerLevel, const SessionOptions &, const SatApplyContextVariant &, const IExecutionProvider &, const InlinedHashSet &)std::optional> onnxruntime::(anonymous namespace)::GetScaleFromNode(const Graph &, const Node &, const InlinedHashSet &)std::vector onnxruntime::(anonymous namespace)::GetOutputNodeMerges(Graph &, Node &, const InlinedHashSet &)std::vector onnxruntime::(anonymous namespace)::GetInputNodeMerges(Graph &, Node &, const InlinedHashSet &)void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector &, std::vector &)Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector &)Status onnxruntime::Graph::InjectExternalInitializersFromFilesInMemory(const InlinedHashMap> &)Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector> &)void onnxruntime::PlannerImpl::GeneratePlanForWeightsHelper(const GraphViewer &, const InitializedTensorSet &, const KernelCreateInfoMap &, const std::string &, size_t, std::vector> &)onnx::OpSchema onnxruntime::CreateSchema(const std::string &, const std::vector &)void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const TensorOpCost &, const std::function &)onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const InitializedTensorSet &, const Path &, const IExecutionProvider &, const std::function &)(?-m:$)missing )unexpected )sparse_tensor(map(optional(corrupted protobuf data: tensor shape size() does not match the data size(opaque(Invalid value(), BIsSigned(Quant GEMM format: AIsSigned(Invalid sequence index (This is an invalid model. Subgraph output (This is an invalid model. Graph output (This is an invalid model. Model input () and the split dimension of the input (Input's height () vs () is not equal to number of scan outputs () has more outputs () and outputs () is not equal to number of scan inputs (Number of inputs () has more inputs (Mismatch between number of splits () shall be be no more than num_beams () does not match the number of channels () does not match the number of axes () does not match number of inputdimensions values (Sparse tensor values () bound to different types (This op has been implemented only for the following types () is different from n_nodes (num_return_sequences (Sparse tensor indices () of operator (Sparse tensor (Error: Duplicate definition-site for (' of input parameter (Type Error: Type parameter () + right_border () needs to be greater than or equal to the left_border () needs to be greater than or equal to the top_border () + bottom_border () + rightBorder () needs to be greater than or equal to the leftBorder () needs to be greater than or equal to the topBorder () + bottomBorder (New node op () Op (Invalid data location ( domain (split_size_sum (max_length () shall be greater than input sequence length (Input's width () of output arg () output arg () input arg () functions of () != split_dim_size () != new size (Bias size (Tensor size () specified for sequence of size (Either both scale and offset can be of feature size () is not equal to the existing dim value () is not equal to the existing rank value () attribute () of Optype (Op type ( but the node in the model has the following type ( and type (Unexpected type () does not match expected type (), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (Type Error: Type (Using user supplied initializer with name (Op with name (This is an invalid model. Error: Duplicate definition of name (This is an invalid model. Error: two nodes with same node name (Trying to add a domain to DomainToVersion map, but the domain is already exist with version range (This is an invalid model. The sum of input arg count is not equal to size of input defs in node () in node () of node (This is an invalid model. Node () does not match produced node op in runtime optimization record (Ranks inferred (Dimension value inferred () than declared ( ) is different from what is supplied (Number of scan output axes specified (Number of scan input axes specified (Number of nodes in nodes_ () + scale[1] () + scale_[1] () + scale[0] () + scale_[0] () != ( Encountered following errors: ( data type. Actual: (Inferred shape and existing shape differ in rank: ( broadcasting: (Scale size: (Inferred elem type differs from existing elem type: (Input rank for starts and ends should be the same: (Cannot use user supplied initializer with name: ()) , expected: (AOT inlining completed. (Mismatch between the sum of 'split' (Number of elements of input 'sizes' (Number of elements of input 'scales' (Number of elements of input 'shape' () must be same as rank of input 'X' ('}''{'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'' is expected to have field 't'Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights') for attribute 'axis'GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'' is expected to have field 'sparse_tensor'' is expected to have field 'type_proto'model format error! Missing 'location''block-literal'' is expected to have field 'g'']''['Number of elements of input 'scales' must be same as rank of input 'X'Number of elements of attribute 'scales' must be same as rank of input 'X'Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'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'Unknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'' ep:'Kernel definition was not found for node Domain:'Unable to find schema for node. Domain:'Attribute:'No attribute with name:' node. Name:' (node:'Missing session state for subgraph. Node:'':'One of 'MAX,' 'L1,' 'L2'. Valid values are '-rank <= axis && axis != -1 && axis < rank - 1'invalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/'Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'invalid number; expected digit after '.'invalid number; expected digit after '-'','' ('Session config entry 'Could not find OrtValue with idx ' but input 'The model has input 'Nodes in a graph must be topologically sorted, however input 'Invalid model. Node input 'Another version of operator 'Attribute name and type don't match for 'Subgraph SessionState was not found for 'Weight buffer for initializer 'Get preallocated buffer for initializer 'Type Error: Data in initializer 'Removing initializer 'Graph must be in single static assignment (SSA) form, however '' in custom op 'Unsupported version 'No opset import for domain 'Invalid values in 'Number of entries in 'Mismatched attribute type in 'Removing NodeArg 'number overflow parsing 'Error parsing 'Failed to find args for kernel type string 'DataTypeUtils::FromDataTypeString - Received invalid data type string 'Error compiling 'Error reverse compiling 'Invalid 'direction' argument of 'Invalid direction value of '' instead of '' of '(for attribute 'Required attribute 'Attribute 'values of data_type 'Execution type '' for node type 'This is an invalid model. Type Error: Type 'Kernel type string already exists for formal parameter name 'Could not find OrtValue with name ') node with name 'Outer scope node arg name ', but the 'for node ' in 'Constant' node 'Node '' should be stored in field 'Field 'Duplicate sparse_tensor_initializer: '', actual: 'Duplicate constant node sparse initializer name: ' with name: 'Unable to constant fold If node: ''. Expected: '; last read: 'Duplicate initializer (dense, sparse or ConstantNode): 'Duplicate initializer (dense or ConstantNode): 'operator&operator&& && &operator%$Failed to create output tensor for output #{"conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE">""name" :"node (" at NodeArg " message of type " with name "Failed to load external data file "Failed to find node "padding_mode ") for output: "). domain: ", domain: "Failed to parse value: ""thread_pool_name": ""thread_id": "{"cat" : "InputBroadcaster can only start at span boundary!Right hand side of MatMulFpQ4 must be a 2D matrix!B input for MatMul must be a 2-D matrix!failed to get first output!Number of streams does not equal to number of device types!operator!DeserializeTensorProto() takes either pre-allocated buffer or an allocator!model format error!type_proto is not of type map!delta in Range operator can not be zero!4b quantization not yet supported on this hardware platform!Encountered invalid float literal!symbolic and integer dims mismatch!TensorProtoToTensor() tensor shape mismatch!size attribute in UnfoldTensor not positive and less than the dim size!Input q4 tensors of wrong size!QLinear Pooling unsupported pooling size!Unsupported non-raw-data data type!Each input must be (tensor, scale, zero_point) tuple!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!type_proto is not of type sequence! found!] is not supported!Quantized and packed blob size differ from expected!X input is required!Compiler::Copy called!size attribute in UnfoldTensor not specified!If STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!Second input of MatMulFpQ4 must be a 1D blob!) is 0-element but contains data!Can not get shape initializer data!Opaque type is not a non_tensor type!!!Session Options { config_options: { Permutation entry Failed to load library Failed to unload handle for dynamic library Failed to load dynamic library ' doesn't support memcpy Number of dimensions for rois should be exactly Second dimension for rois should be exactly failed. Only Received invalid value of arena_extend_strategy does not have the graph for key Extended allocation by BFC Arena shrunk by was inserted using the NHWC format as requested by as the model has control flow nodes which can't be supported by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by reused by Added by [StringSplit] Unexpected output index [WordpieceTokenizer] Unexpected output index [StringRaggedTensorToDense] Unexpected output index Unexpected input index Required output at index Input type for input at index Required input at index False node must always be the next node, but it isn't at index invalid UTF-8 byte at index activate notification with index Invalid node index invalid index Unexpected index Skipping entry for missing optional value at idx throw The total allocated bytes is now ] (usually, this means you Loop 'body' subgraph scan outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors but output Scan 'body' subgraph outputs should all be tensors but output Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output )'s output Invalid value in scan_output_axes for output Mismatched tensor element type for output Mismatched type for output Loop had zero iterations and the shape of subgraph output Not eliminating output Failed to create output tensor for If output Output OrtValue has not been created for loop state variable output and Output )'s input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input Invalid value in scan_input_axes for input NULL input supplied for input Key type of map input Value type of map input Scan input Element type of optional input Dimension of input Rank of input Element type of input Node input Element type of sequence input All inputs to Concat must have same rank. Input Subgraph must have the shape set for all outputs but inputs but bumped the operator version but elements, but Only the last noexcept , but its version is not First input must have one dimension not ) yields zero outputs, but got ) takes zero inputs, but got Input 'query' is expected to have 3 or 5 dimensions when key is empty, got Attribute border needs to be specified with four border elements, got Input 0 is expected to have 1 or more dimensions, got Input 'query' is expected to have 3 or 5 dimensions, got Input 'key' is expected to have 3, 4, or 5 dimensions, got Input 'x' is expected to have 3 or 4 dimensions, got Input 'value' is expected to have 3 or 4 dimensions, got Input 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Input 'past_key' is expected to have 4 dimensions, got Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'past_value' is expected to have 4 dimensions, got Input 'query' is expected to have 3 dimensions, got Input 'key' is expected to have 3 dimensions, got Input 'input' is expected to have 3 dimensions, got Input 'input_features' is expected to have 3 dimensions, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'value' is expected to have 3 dimensions, got input is expected to have 3 or 2 dimensions, got skip is expected to have 3 or 2 dimensions, got input_ids is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_ids' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have 2 dimensions, got Input 'presence_mask' is expected to have 2 dimensions, got Input 'cos_cache' is expected to have 2 dimensions, got Input 'sin_cache' is expected to have 2 dimensions, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got beta is expected to have 1 dimensions, got gamma is expected to have 1 dimensions, got Input 1 is expected to have 1 dimensions, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'relative_position_bias' dimension 1 should be same as number of heads, got Input 'past_value' dimension 1 should be same as number of heads, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got num_return_sequences shall be a positive integer, got logits_processor shall be a non-negative integer, got subgraph past state is expected to have 5 dimension, got Input 'past' is expected to have 5 dimension, got subgraph output present_key_self_0 is expected to have 4 dimension, got subgraph logits output is expected to have 3 dimension, got bias is expected to have 1 dimension, got beta is expected to have 1 dimension, got gamma is expected to have 1 dimension, got Input 'bias' is expected to have 1 dimension, got Input 'vocab_mask' is expected to have 1 dimension, got Input 'query' is expected to have 3 dimensions when key is given, got max_sequence_length, got Input 'relative_position_bias' dimension 3 should be same as total_sequence_length, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, 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 3 should be same as head_size, got Input 'past_value' dimension 3 should be same as head_size, got Input 'presence_mask' shape[1] shall be vocab_size, got Input 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input is expected to have four dimensions corresponding to [N,C,H,W], got head_size / 2 or rotary_embedding_dim / 2, got A dimension cannot be less than -1, got Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got Input 'relative_position_bias' dimension 0 should be batch_size or 1, got repetition_penalty shall be greater than 0, got BNSH Input 'past_key' and 'past_value' should have same dimension 2 (max sequencelength or past sequence length), got Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got Size mismatch validating subgraph inputs. Got input_features shall have 3 dimensions. Got decoder_input_ids shall have 2 dimensions. Got extra_decoder_tensor shall have 2 dimensions. Got values. Got extra_decoder_tensor first dim not same as batch_size. Got Output was expected to have tensor type. Got Input was expected to have tensor or sparse tensor type. Got Input was expected to have either tensor, sequence, optional or map type. Got Data type for starts and ends inputs' is not supported in this build. Got Rank of input to Normalized must be less than 2. Got , Got list count Pads tensor size should be equal to twice the input dimension count Mismatch between input data and scale: size of scale != input channel count Mismatch between input data and B: size of B != input channel count Bad reference count Bad hex digit yet this opset 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 is till opset ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset at offset The existing summation for max mode and sampling ratios besides 1 is incorrect , expect Malformed repeat Allocated memory at Can't The number of keys vs will match the shapes produced in the model for other inputs outputs but Scan expects but usage of initializer in graph expects Real memory steps Fetches vector passed to GetOutputs contains Cannot parse the diagonal elements along dims axis Non concat axis dimensions must match: Axis this However, the number of key is StringRegexSplitWithOffsets has 4 outputs but index is type must be array, but is type must be object, but is type must be number, but is type must be string, but is . Size of dimension being split is axis must be positive and smaller than the number of dimension but it is no_speech_token_id is out of range, it is Axis is . Num args is values is num_heads must be provided if rotary_embedding_dim is and 'leaf_weights' length is Attribute 'leaf_targetids' must have same length as attribute 'leaf_weights'. 'leaf_targetids' length is , vocab_size is mode attribute is Attribute 'nodes_splits' must have same type as input. Input type is Attribute 'leaf_weights' must have same type as input. Input type is Attribute 'membership_values' must have same type as input. Input type is and attribute type is Input data type does not match the expected data type. Current data type is values, but NNZ is . Dimension 0 is DoCoalesce failed: r2->op() is DoCoalesce failed: r1->op() is Length of input 'size' is fused GPT2Gelu subgraphs fused Gelu subgraphs Fused Attention subgraphs fused SkipLayerNorm subgraphs fused EmbedLayerNorm subgraphs fused LayerNorm subgraphs with flags and the number of values removable attributes [Memory] SessionStateInitializer statically allocates outputs so the subgraph requires inputs and requires The subgraph in 'body' requires Internal error. The preallocated buffer is too small. Requires Subgraph in 'body' produces expected to be a 1D tensor but was Sampling ratio should be >=0, but it was Failing expression was axis must be in [-rank, rank-1]. input rank was . Output tensor rank was . Input tensor rank was Invalid entries in sequence_lens. Max sequence length was Scan inputs have inconsistent sequence lengths. Previous value was Scan inputs have inconsistent batch size. Previous value was The input type was Can't slice a non-tensor OrtValue. Type was Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was Number of entries in 'scan_output_axes' was Number of entries in 'scan_input_axes' was but has Input 1's dimensions size is 0 (scalar), it must has 1 element but it has Third input must contain only one element. It has num_buckets must contain only one element. It has rewrite (third input) must contain only one element. It has pattern (second input) must contain only one element. It has names has but input tensor has for SizeToDimension. Tensor has for SizeFromDimension. Tensor has inputs but subgraph has Graph has If node has 'Loop' node has 'If' node has ) has Input 'cos_cache' dimension 1 should be same as Input 'cos_cache' dimension 0 should be same as because the CPU execution path is deemed faster than overhead involved with execution on other EPs outputs which doesn't match the subgraph's Failed to close file descriptor for operator LabelEncoder could not unpack default tensor Invalid data type for split tensor Unsupported tensor data type for tensor Deserialize tensor Axes tensor should be a 1D tensor syntax error Only one node should produce an output. Existing entry for reference temporary for codecvt_byname::codecvt_byname failed to construct for time_put_byname failed to construct for time_get_byname failed to construct for moneypunct_byname failed to construct for numpunct_byname::numpunct_byname failed to construct for numpunct_byname::numpunct_byname failed to construct for collate_byname::collate_byname failed to construct for ctype_byname::ctype_byname failed to construct for ctype_byname::ctype_byname failed to construct for collate_byname::collate_byname(size_t refs) failed to construct for template parameter object for Correctly set TuningResults for List of tensors for Got invalid dimensions for bytes for n-gram counts out of bounds for Failed to get allocator for Failed to create output tensor for Allocation of memory pattern buffer for SaveValueInfoOrtFormat: value_info_proto for typeinfo for Null type info for Layout/Transpose optimization for Could not find an implementation for Graph attribute inferencing returned type information for Could not find Region for Failed to find kernel for Failed to add kernel for Invalid rank for Invalid value for thread-local wrapper routine for thread-local initialization routine for typeinfo name for construction vtable for guard variable for No Op registered for Reserving memory in BFCArena for Creating BFCArena for Extending BFCArena for VTT for Can't register custom op domains with ORT for fused Add-Dropout-(Add) for (default) or Mem pattern for initializer Type Error: Shape of initializer Cannot reshape initializer Initializer by either re-generating the model with latest exporter/converter after Expected character This session cannot use the CUDA Graph feature as requested by the user This session cannot use the graph capture feature as requested by the user for transformer GraphTransformer attributes in LabelEncoder Can not find the execution provider Cannot find execution provider found duplicated provider Provider system error number Too many inputs for op The TypeProto attribute in the Optional op and anti-aliasing is set to 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 Dynamic block base set to Setting inter_op_num_threads to Setting intra_op_num_threads to Failed to copy tensor to rotary_embedding_dim must be less than or equal to covariant return thunk to non-virtual thunk to Setting enable_profiling to Setting execution_mode to cannot be safely updated to This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to [ParseError at position Error detected in function Applying runtime optimization action Missing action with activation The old ORT format model (version than the operator set version Cannot broadcast dimension Inferred shape and existing shape differ in dimension is incompatible in the dimension ' dimension Insufficient dimensions to slice on , column 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 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 . No opset import for domain No opset registered for domain invocation function for block in kernel is not supported in ) should be stored in and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign inputs but Scan was only given Inserting Q/DQ pair between Dimension mismatch in unification between greather than num_beams shall be a positive integer no more than ) shall be no more than There's no data transfer registered for copying tensors from Failed to parse since_version from Load model from fused Gemm right operand cannot broadcast on dim left operand cannot broadcast on dim stream Failed to get symbol As long as the intermediate shapes produced in the model Load model Einsum op: Unsupported data type for Diagonal but has rank does not match rank is invalid for a tensor of rank expected to have rank cannot use operator[] with a string argument with is not compatible with cannot use insert() with cannot use at() with cannot use erase() with pattern length Permutation length Memcpy nodes are added to the graph ], could not find NodeArg custom op schemas mismatch, expecting Creating while parsing Input of int64 must have output of string Non-zero status code returned while running Unknown encoding alignof sizeof Available memory of Unexpected data type for Clip input of Unexpected data type for Clip 'min' input of One and only one attribute must be set out of Unexpected data type for QuantizeLinear input y_zero_point of Invalid dim0_offset of Concat of has mismatched dimensions of Invalid batch_axis of Invalid time_axis of is smaller than requested bytes of Invalid data type for GRU operator of Invalid data type for LSTM operator of 'has a different number of and the number of Invalid LSTM merge activation function of Invalid activation function of is deprecated in domain_version of with domain_version of for input dimension of Invalid dimension of Invalid arg_num of Expected shape from model of entries which doesn't match the number of fetches the frame was initialized with of sequence_lens length of has length of Inputs 'past' dimension 2 shall have length of Unexpected element size of and bias size of has batch size of did not match batch size of beta is expected to have size of gamma is expected to have size of . Size of scale and bias (if provided) must match this. Got scale size of Negative index values are not permitted. First entry in map has index value of Invalid num_outputs value of Another operand has a dim value of Invalid normalize value of Invalid PACK_MAP value of Invalid CAST_TO value of Unexpected CAST_TO value of Unexpected NORMALIZE value of Invalid dtype of does not match existing output type of Unsupported output type of Unsupported input element type of Input element type of Unknown tensor type of Unsupported tensor type of Unsupported attribute value type of Unsupported input data type of Unsupported sparse tensor data type of Invalid data type of Unsupported data type of 'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of should be a scalar. Got shape of does not match actual shape of dimensions or more but input had shape of Unexpected mode of which does not equal the specified override of must be 1 instead of CblasTrans Unexpected CBLAS_TRANSPOSE for TransB of CblasNoTrans Unexpected CBLAS_TRANSPOSE for TransB of Unexpected CBLAS_TRANSPOSE for TransA of Unexpected value for 'add_second_class' of ) has output size ) has input size against size Values size with a fixed dimension size program size bins of max chunk size x_zero_point must be null or 1D tensor with size scale must be 1D tensor with size does not match input batch size Grid batch size Failed to allocate memory for requested buffer of size Input 'position_ids' dimension 1 should be of size Input 'position_ids' dimension 0 should be of size expected size ' must have Inputs 'cos_cache' and 'sin_cache' are expected to have Graph in 'body' attribute of Loop should have axis value Invalid mode of value axis attribute value Attribute perm of Transpose has an invalid value. Value LabelEncoder could not unpack tensor attribute No Graph instance was found for attribute type field and data field mismatch in attribute LabelEncoder is missing attribute Value of attribute Attribute fused MatMul and Transpose Cannot parse data from external tensors. Please One path in the graph requests feature before Flush-to-zero and denormal-as-zero are time_axis and batch_axis must have different values but both are optype output_dtype and the key type Einsum op: An implementation for the input type BeamSearch op: An implementation for the input type does not match y_zero_point type has inconsistent type ' has element type The default tensor type sparse tensor type can not be written into Tensor type TensorProto type ' must have type and the value type has unsupported type Mismatch between expected type Invalid tensor data type Could not infer data type from input tensor with data type Invalid data type for input shape out of bounds for shape Error detected at line > typename Implicit input name No NodeArg found for name The custom op domain name input with name AddInitializedTensor already has tensor with name Failed to find existing initializer with name A dso with name Existing registration with name Failed to find initializer to reshape with name Trying to register schema with name Cannot infer type and shape for node name initializer for module Number of items must compose whole ], while Writing profiler data to file Error detected in file , but it is already registered from file ) from file The only supported values for the environment variable This session cannot use the graph capture feature as requested by the user as all compute graph nodes have not been partitioned to the '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 ) should be file inside the The bytemap range forgot to update the version range in DomainToVersionRange in tree is used by node Invalid input index for node Entry exists in node of node UnsqueezeElimination cannot remove node Can't remove node The kernel corresponding to the node Can not find the node Unable to find node ) and node Node but subgraphs produce Could not find a CPU kernel and hence Failed to find allocator for device ) because the ORT planned memory location device . It can only be grid dimensions must be There must be Incompatible dimension: required vector length should be segment_ids must be increasing but found ::friend Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand Size of affinity string must be between 1 and " and can't constant fold . Can't constant fold ] is not supported in this build Invalid typeid The registered allocator for device-id Did not find an arena based allocator registered for device-id removed unexpected Unexpected but expected ; expected not enough space: expected UnpackTensor: the pre-allocated size does not match the raw data size, expected outputs. Expected Tree id mismatch. Expected fused Replaying the captured This transformer is already registered . onnxruntime allows the overloading of an operator if all versions have the same number of declared unsigned Unable to constant fold. InlineIfSubgraph failed unhandled Unhandled Matched fused Matmul and Add ONNX Schema [TypeInferenceError] [ShapeInferenceError] [ T Unloading DSO ? -> output = Cast = num_heads must be a multiple of kv_num_heads. Got num_heads % kv_num_heads == Size of X.shape()[axis:] == head_size must be a multiple of 8. Got head_size % 8 == head_size shall be a multiple of 16. Got head_size % 16 == <= Invalid input var: 0th dimension != Invalid input mean: 0th dimension != Invalid input scale: 0th dimension != Invalid input B: 0th dimension != Invalid input var: NumDimensions() != Invalid input mean: NumDimensions() != Invalid input scale: NumDimensions() != Invalid input B: NumDimensions() != invalid index found, index = , opset_version = Cannot find a function builder that satisfies the requested opset version: op_type = invalid indice found, indice = njob_ = job_.size() = Hardmax inputs N, D and N * D must be < SoftmaxCPU inputs N, D and N * D must be < The mapped value of string input cannot be empty: auto_set_affinity: DFA out of memory: Cannot find word in vocabulary: arena_extend_strategy: input index: Input index: COO m index: COO k index: For ort_value with index: Input data with index: invalid value index: OrtValue index: Invalid index: ", index: TriggerDownstream - TriggerPointIndex: NodeIndex: Error context: [CustomOP type inferencing error]: no type constraint found for output: does not match type of output: [CustomOP type inferencing error]: no type constraint found for input: Found '.' not part of an ellipsis in input: Found a '.' not part of an ellipsis in input: Cannot use the same name as both a subgraph initializer and subgraph input: Cannot find missing input: [CustomOP type inferencing error]: kernel Input: Failed to compute buffer size for wchar_t. Converted only first: Failed to convert to wchar_t. Converted only first: Failed to compute size for UTF-8. Converted only first: Failed to convert to UTF-8. Converted only first: combination in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Unsupported device specified in the memory arena shrink list: channels_last: Failed to parse path root: Trying to get a SparseTensor, but got: Trying to get a Tensor, but got: Trying to get a TensorSeq, but got: Expecting to contain one index, got: encoder subgraph output 0 shall be named as logits, got: decoder subgraph output 0 shall be named as logits, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: decoder subgraph input 1 shall be named as encoder_hidden_states, got: encoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph input 0 shall be named as encoder_input_ids, got: encoder subgraph input 2 shall be named as decoder_input_ids, got: decoder subgraph input 0 shall be named as input_ids, got: subgraph input 1 shall be named as position_ids, got: encoder subgraph input 1 shall be named as encoder_attention_mask, got: decoder subgraph input 1 shall be named as encoder_attention_mask, got: subgraph input 2 shall be named as attention_mask, got: COO indices must be 2-D, got: subgraph input 3 shall be named as past_0, got: subgraph input 1 shall be named as present_0, 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: }. Got: Expecting inner indices to be same as nnz. Got: Expecting one index. Got: Must contain Coo format. Got: Must contain BlockSparse format. Got: Expecting all elements to be tensors. Got: Tensor is expected to contain one of the primitive data types. Got: Expecting two indices. Got: Outer index count must be rows + 1 or zero. Got: expected to have tensor or sparse tensor type. Got: or UNDEFINED. Got: dense shape must 2-D. Got: Outer indices must be M + 1. Got: Expecting indices to have 2-D shape . Got: }, Got: invalid node input count: Total shared scalar initializer count: Total fused Attention node count: Total fused reshape node count: Total Gelu Approximation (FastGelu) node count: , Count: Unexpected opcode in short circuit: memory limit: is out of bounds of lhs_right: is out of bounds of out_left: Value type is not supported yet: Unsupported ONNX opset: , offset: , expect: Sparse format must not be set. Already contains format: [AudioDecoder]: Unknown audio stream format: rows: with status: Graph initializer names must appear after the actual inputs: max_gram_length must be inbounds of ngram_counts: min_gram_length must be inbounds of ngram_counts: Processor id must consist of only digits: Can not digest separators: Match contains invalid utf8 chars: Input string contains invalid utf8 chars: startpos: endpos: Currently do not support dims higher than 2 dimensions: Must contain Csr format. Contains: [BertTokenizerVocab]: can not find tokens: Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: num_input_channels: kernel channels: Got input tensor and grid tensor ranks: Expecting index blocks: to be equal to values blocks: , block in memory pattern size is: but the actual size is: ]. Its actual value is: GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: . The shape is: (requested) num_bytes: with following configs: initial_chunk_size_bytes: initial_growth_chunk_size_bytes: max_power_of_two_extend_bytes: Total allocated bytes: among attributes: ) type inference failed, inferred types: Error mapping output names: Number of affinities does not equal to thread_pool_size minus one, affinities: Cleaning up back-to-back nodes: ]. Number of nodes: " because it is missing required fields: Error mapping feeds: Unsupported value for inter_op_num_threads: Unsupported value for intra_op_num_threads: Node's: typestr: affinity_str: Duplicate entry for kernel type str: Unsupported operator: combination is not an arena based allocator: ParseData type mismatch for tensor: load external data into raw data for tensor: The type of tensor: Got the last dimension of the grid tensor: Data size mismatch. Tensor: Fatal error: Failed to unload library with error: DeleteFolder(): nftw() failed with error: ", error: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: We do not expect duplicate registration of types for: Could not find Region for: MLDataType for: Attempting to load runtime optimization records for a previously loaded optimizer: Using cached version of pre-packed weight for constant initializer: Replaced external initializer: External initializer: Missing dims for sparse initializer: Missing indicies for sparse initializer: Missing case in Compiler: filter_number: Failed to parse map when processing the number: Failed to parse unknown_value when processing the number: Invalid input var: Bad final char: Can not digest tokenexp: group: Unsupported input type in SpaceToDepth op: Unsupported input type in DepthToSpace op: RE2: unexpected op: [CustomOP type inferencing error]: no kernel def matches node inputs for Op: TensorProto: set_denormal_as_zero: Invalid regex pattern: invalid rewrite pattern: ). Reason: Candidate for fallback CPU execution: Unsupported value for enable_profiling option: Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: unimplemented activation: Einsum op: Exception during MatMul operation: Wrong op_type name for running propagation: Failed to get allocator for location: Invalid data location: (node_version: node_version: kernel_end_version: Trying to add a domain to LastReleaseVersion map, but the domain is already exist with last version: kernel start version: Unsupported model IR version: , max supported IR version: Dimension: column: (domain: and domain: , domain: Invalid input mean: perm: Unsupported approximation_algorithm: Unsupported optimization level: ORT config json from the model: Invalid processor interval: Unexpected op in Regexp::Equal: }, actural: max_dead_bytes_per_chunk: does not match input rank: Got input tensor rank: Got grid tensor rank: Ranks of input data are different, cannot concatenate them. expected rank: SpaceDepth ops require a 4-D input. Provided rank: : failed validating the check: given file_length: , external_data.length: Config value is longer than maximum length: Invalid sequence length: , length: ) should be a relative path, but it is an absolute path: , path: in inlined subgraph: from graph: ' Unable to fetch: error msg: Constant initializer NodeArg shape should not be null. NodeArg: Invalid OpIdentifier string: Failed to find input name in the mapping: allow_spinning: Unsupported device allocator in the context of pre-packed weights caching: Ignoring unsupported session option in ORT config: Unsupported graph_optimization_level value in ORT config: Unsupported execution_mode value in ORT config: CUDA Graph for this model with tag: Running with tag: codepoints out of: bytes out of: Element_size of: A node cannot point to itself: dst_size: , thread_pool_size: stack_size: > dense_size: Must have the same size. Got src_size: Expecting inner index size: Index size: text size: Unsupported element size: but ngram_indexes size: the same as values size: must be equal to or twice the values size: conv filter size: must be less than total buffer size: perm size: Unsupported output datatype with size: Char embedding size: Got weights of size: external data size mismatch. Computed size: Get feed size: but expected feed size: Duplicate ngram detected, size: Required min_gram_length must be positive: max_skip_count must be non-negative: expected to have: Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: Unexpected axis value: snprintf() failed with return value: Invalid dimension value: Output dim value: Input dim value: Invalid input type of value: are '0' and '1'. The environment variable contained the value: Attribute perm for Transpose has repeated value: Unsupported 'dtype' value: conv_window_size attribute: char_embedding_size attribute: embedding_size attribute: Unrecognized attribute: Requested attribute: Attribute: Unsupported value attribute datatype: data_type: expected to have tensor or sparse tensor type: which is of op type: but is of type: ' expected to be of type: is expected to have type: SaveAttributeOrtFormat: Unsupported attribute type: Tile doesn't have an implementation yet for the type: to device type: Unable to find a data transfer for copying from device type: , has unsupported type: Unsupported type: Initialized tensor with unexpected type: Unsupported data type: Unsupported Y type: Unsupported X type: , type: OpType: kernel_shape: Invalid input shape: Right shape: Left shape: updates shape: , indices shape: segment_ids shape: Loop subgraph input 1 has unknown shape: Loop subgraph input 0 has unknown shape: The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: First dimensions of data and segment_ids should be the same, data shape: (line: [StringMapping]: Should only exist two items in one line, find error in line: Cannot know how to parse line: Failed to parse mapping_table when processing the line: NodeProto (name: Attribute (name: TensorProto (tensor name: Unrecognized data_type (tensor name: setting data_type field (tensor name: STRING data (tensor name: Location of external TensorProto ( tensor name: Data of TensorProto ( tensor name: Failed to find initializer for name: Initializer name: Unrecognized type value case (value_info name: The given function name: No attribute with name: Constant node name: , Name: Case not handled in ComputeSimple: Failed to save ORT format model to file: External file: File: Invalid input scale: Given model could not be parsed while creating inference session. Error message: ' Status Message: Could not finalize session options while constructing the inference session. Error Message: Constant node: QDQ node group cannot have an output from the target node that is consumed by a Q node and a graph output. target node: QDQ node group cannot have an output from the target node being consumed by a Q node and a non-Q node. target node: , target node: func info for node: ORT optimization- Force fallback to CPU execution for node: KernelDef is null for node: for If node: Fused reshape node: used in the node: Transpose Optimizer is adding an unexpected node: QDQ 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: mode: Unexpected opcode: unhandled opcode: munmap failed. error code: - error code: pthread_attr_setstacksize failed, error code: pthread_create failed, error code: remove() failed. Error code: Right shape override: Left shape override: Source: Subgraph has nodes running on device: while parent graph node running on device: Incompatible dimension: required vector length of unknown_value should be: unknown round: Invalid key found: Mismatch between Graph and IndexedSubGraph. Node not found: Tensor must always contain primitive types. Found: Invalid allocation kind: Output type not supported in this build: Last dimension of grid: Input axis is invalid: Failed to find op_id: Duplicate entry for op id: with graph annotation id: Failed to parse the cuda graph annotation id: wait on Notification with id: Not enough elements in dilations. Expected: Not enough elements in strides. Expected: Not enough elements in pads. Expected: Optional Type mismatch. Expected: Not enough elements in kernel shape. Expected: min_gram_length >= max_gram_length required: Transpose optimizer failed: Graph attribute inferencing failed: Einsum op: Transpose failed: GrowStack() failed: close() failed: " failed: Simplify case not handled: Invalid fd was supplied: modified: An entry for this name has already been added: An OrtValue for this name has already been added: size to read: Barrier - BarrierId: ActivateNotification - NotificationId: WaitOnEP - NotificationId: panic: does not align with rank of input data: Unknown value for `keep_aspect_ratio_policy`: dynamic_block_base_: CHECK failed: !is_closed_: X: GEMM: Dimension mismatch, W: Failed to unload DSO: M: CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: K: C: vs inner_B: exceeded maximum protobuf size of 2GB: Invalid input B: Can not multiply A and B as inner dimension does not match. inner_A: Invalid input data: number of dimensions is less than 3: Invalid RE2: Invalid input scale: number of dimensions is not 1: Invalid input B: number of dimensions is not 1: Processor id must start from 1: conv kernal size 1: Inference error(s): CHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): CHECK failed: (index) < (current_size_): CHECK failed: (buffer_used_) == (buffer_size_): CHECK failed: (count) <= (buffer_used_): CHECK failed: (index) >= (0): CHECK failed: (count) >= (0): CHECK failed: (backup_bytes_) == (0): CHECK failed: (static_cast(new_size)) <= (static_cast( (std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0]))): ': "num_run": ], "core": is absent, but not optional. Op : Unsupported kernel dimension : Unsupported pooling size : DynamicQuantizeLSTM : Conv kernal size 2 : " : Input 'position_ids' is expected to have 0, 1, or 2 All implicit inputs should have OrtValue instances by now. 'bias' is not allowed for packed kv. Inconsistent shape in loop output for output. 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. 'was added but does not exist. appears in graph inputs and will not be treated as constant value/weight. Default logger already set. vs. then_branch and else_branch produce different number of outputs. Mismatched data types between input and output Tensors. in initializers. bytes. Shape mismatch attempting to re-use buffer. The node is not placed on any Execution Provider. Parallel execution mode does not support the DML Execution Provider. Having memory pattern enabled is not supported while using the DML Execution Provider. Input dim is zero but required output dim is non-zero. Use the graph capture feature with caution. Which axis to split on. Ranks of pair-wise operands must be equal. ) must have the same length. Tensor element type mismatch. Tensor type mismatch. does not match. This may prevent some of the graph optimizations, like const folding. Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. Value used for extrapolation, when applicable. Default is 0.0f. Cannot scale 0 by any factor to generate a non-zero value. Unexpected attribute type. The override dims are not compatible with given tensor's shape. Input_ids and segment id should have the same shape. The input tensor and grid tensor must have the same rank for GridSample. ReadFileIntoBuffer - unexpected end of file. However the types are incompatible. Failed to match Shape node. execution provider is not supported in this build. Input id is not valid. Segment id is not valid. Execution providers must be registered before the session is initialized. 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]`. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. This model has shape massaging nodes that will execute on CPU. 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. 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. An attribute specifying the number of scan_inputs M. The input tensor and grid tensor ranks must be >= 3. The last dimension of the grid tensor must be the rank of the grid tensor - 2. 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. Can broadcast 0 by 0 or 1. Attempting to broadcast an axis by a dimension other than 1. Second input of Gather should be a constant with value 1. input tensor and indices tensor must has rank larger than 0. sizeof... ... 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). Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). . It might have negative impact on performance (including unable to run CUDA graph). Beta should be of shape (hidden_size). Gamma should be of shape (hidden_size). '). are '0' and '1'. LaunchKernel - RNN op: Invalid activation attribute - Move it out of graph inputs if there is no need to override it, using the representative input used to capture the graph, If type constraint names are available, Serialization of fused function body is not currently supported, `tf_half_pixel_for_nn` is deprecated since opset 13, ] out of range [0, Attention mechanism memory sequence lengths value must in (0, is not in (0, of the same shape as the representative input (common case), '), ", , ************************** FATAL ERROR DETECTED ************************** Pads has incorrect number of values. Expected 2 * Constant folded (inlined) ' operator"" Data for input X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_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) 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) { 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) } CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) 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) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } { 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) }> } { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } { 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) } { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike (Zero, X) AlphaLessThanX = Less(AlphaCast, X) Y = Where(AlphaLessThanX, X, ZeroCast) } { 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) } { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } { 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) } { 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) } { # 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) } > } { 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) } { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } { 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) } { 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) } { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { 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) } { 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) } X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) 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) 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, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } { 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) } { 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) } Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} for the following indices [libprotobuf %s %s:%d] %s The requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode attempt to nsync_mu_unlock() an nsync_mu held in read mode checking a waiter condition while unlocked 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], ] ``` 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], ] ``` ] [ The error message from this fatal error was: Stacktrace: Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes 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}. 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. 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. 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. 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 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) 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) 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() To prevent further fatal errors from being ignored this application will be ' of node: Two fatal errors have been detected, the first was inappropriately ignored. ************************** FATAL ERROR DETECTED ************************** Return elements, either from X or Y, depending on condition. 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