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rHHK8HuxHH@HL$ HL$ guHHK8HuCHH@UHL$ HL$ ؾtHHK8HuHH@ HL$ 譢HK8HtHH;H@ HC8HĀ[H\$HL$UH$HHHL$ ɟuHHK8HuHH@HL$ HL$ tCrHHK8HuJHH@\HL$ HL$ ?)vHHK8HuHH@'HL$ 贡HL$ ZjoHHK8HuHH@HL$ HL$ UuHHK8HuHH@HL$ JHL$ uHHK8HuvHH@HL$ HL$ ;qHHK8HuAHH@SHL$ HL$ kuHHK8Hu HH@HL$ 諠HL$ A vHHK8HuחHH@飁HL$ vHL$ LrHHK8HuHH@HL$ AHL$ 8rHHK8HumHH@HL$ HL$ sHHK8Hu8HH@JHL$ ןHL$ =sHHK8HuHH@HL$ 袟HL$ XuHHK8HuΖHH@ࢁHL$ mHL$ rHHK8HuHH@HL$ 8HL$ nnHHK8HudHH@vHL$ HL$ YnHHK8Hu/HH@AHL$ ΞHL$ DnHHK8HuHH@ HL$ 虞HL$ /nHHK8HuŕHH@סHL$ dHL$ nHHK8HuHH@HL$ /HL$ u@pHHK8Hu[HH@mHL$ HL$ qHHK8Hu&HH@8HL$ ŝHL$ KrHHK8HuHH@HL$ 萝HL$ tHHK8HuHH@ΠHL$ [HL$ qrHHK8HuHH@HL$ &HL$ |+qHHK8HuRHH@dHL$ HL$ TrHHK8HuHH@/HL$ 輜HL$ qHHK8Hu蓁HH@HL$ 臜HL$ IpHHK8HuHH@şHL$ RHL$ 'uHHK8Hu~HH@HL$ HK8HtHH;H@ hHC8H$HĀ]H\$HL$UH$HHHL$ mHHK8HuHH@HL$ 莛HL$ qHHK8HuHH@̞HL$ YHL$ lHHK8HuHH@HL$ $HL$ sHHK8HuPHH@bHL$ HL$ uauHHK8HuHH@-HL$ 躚HL$ rHHK8Hu摁HH@HL$ 腚HL$ ;ZuHHK8HuHH@ÝHL$ PHL$ rHHK8Hu|HH@HL$ HL$ UnHHK8HuGHH@YHL$ HL$ ,uHHK8HuHH@$HL$ 豙HL$ GnHHK8HuݐHH@HL$ |HL$ rnHHK8HuHH@HL$ GHL$ oHHK8HusHH@HL$ HL$ XoHHK8Hu>HH@PHL$ ݘHL$ uHHK8Hu HH@HL$ 記HL$ ^uHHK8HuԏHH@曁HL$ sHL$ )qHHK8HuHH@HL$ >HL$ D)vHHK8HujHH@|HL$ HL$ mHHK8Hu5HH@GHL$ ԗHL$ %vHHK8HuHH@HL$ 蟗HK8HtHH;H@ ꚁHC8H$HĀ]H\$HL$UH$HHHL$ 9tHHK8HuoHH@HL$ HL$ DpHHK8Hu:HH@LHL$ ٖHL$ OmHHK8HuHH@HL$ 褖HL$ JrHHK8HuЍHH@♁HL$ oHL$ nHHK8HuHH@HL$ :HL$ 蠮nHHK8HufHH@xHL$ HL$ 諠qHHK8Hu1HH@CHL$ ЕHL$ uHHK8HuHH@HL$ 蛕HL$ vHHK8HunjHH@٘HL$ fHL$ 謷oHHK8HuHH@HL$ 1HL$ 臉qHHK8Hu]HH@oHL$ HL$ ‚qHHK8Hu(HH@:HL$ ǔHL$ mmHHK8HuHH@HL$ 蒔HL$ hsHHK8HuHH@ЗHL$ ]HL$ CqHHK8HuHH@HL$ (HL$ woHHK8HuTHH@fHL$ HL$ )nHHK8HuHH@1HL$ 输HL$ wqHHK8HuꊁHH@HL$ 艓HL$ ArHHK8HuHH@ǖHL$ THL$ ZqHHK8HuHH@HL$ HL$ mHHK8HuKHH@]HL$ HL$ @WqHHK8HuHH@(HL$ 赒HL$ qHHK8HuቁHH@HL$ 耒HL$ qHHK8HuHH@HL$ KHL$ !uHHK8HuwHH@HL$ HL$ pHHK8HuBHH@THL$ HL$ pHHK8Hu HH@HL$ 謑HL$ 2pHHK8Hu؈HH@ꔁHL$ wHL$ }pHHK8HuHH@HL$ BHL$ ȻpHHK8HunHH@HL$ HL$ pHHK8Hu9HH@KHL$ ؐHL$ .pHHK8HuHH@HL$ 裐HL$ )pHHK8HuχHH@ᓁHL$ nHL$ DpHHK8HuHH@HL$ 9HL$ ?pHHK8HueHH@wHL$ HL$ 芴pHHK8Hu0HH@BHL$ ϏHL$ յpHHK8HuHH@ HL$ 蚏HL$ `mHHK8HuƆHH@ؒHL$ eHL$ qHHK8HuHH@HL$ 'HL$ OuHHK8HuSHH@eHL$ HL$ VuHHK8HuHH@0HL$ 轍HL$ coHHK8Hu鄁HH@HL$ 舍HL$ duHHK8HuHH@ƐHL$ SHL$ oHHK8HuHH@HL$ HL$ pHHK8HuJHH@\HL$ HL$ sHHK8HuHH@'HL$ 贌HL$ sHHK8HuHH@HL$ HL$ tHHK8HuHH@HL$ JHL$ tHHK8HuvHH@HL$ HL$ K tHHK8HuAHH@SHL$ HL$ tHHK8Hu HH@HL$ 請HL$ tHHK8HuׂHH@鎁HL$ vHL$ |tHHK8HuHH@HL$ AHL$ qHHK8HumHH@HL$ HL$ rHHK8Hu8HH@JHL$ ׊HL$ tHHK8HuHH@HL$ 袊HL$ tHHK8Hu΁HH@HL$ mHK8HtHH;H@ HC8H$HĀ]H\$HL$UH$HHHL$ pHHK8Hu?HH@QHL$ މHL$ 4pHHK8Hu HH@HL$ 詉HL$ 迬rHHK8HuՀHH@猁HL$ tHL$ nHHK8HuHH@HL$ ?HL$ oHHK8HukHH@}HL$ HL$ 萪pHHK8Hu6HH@HHL$ ՈHL$ ۫pHHK8HuHH@HL$ 蠈HL$ vqHHK8HuHH@ދHL$ kHL$ a&sHHK8HuHH@HL$ 6HL$ uHHK8HubHH@tHL$ HL$ 7vHHK8Hu-HH@?HL$ ̇HL$ OnHHK8Hu~HH@ HL$ 藇HL$ 荢rHHK8Hu~HH@ՊHL$ bHL$ 蘤rHHK8Hu~HH@HL$ -HL$ uHHK8HuY~HH@kHL$ HL$ uHHK8Hu$~HH@6HL$ ÆHL$ IsHHK8Hu}HH@HL$ 莆HL$ ėrHHK8Hu}HH@̉HL$ YHK8HtHH;H@ HC8H$HĀ]H\$HL$UH$HHHL$ uHHK8Hu/}HH@AHL$ ΅HL$ 脂pHHK8Hu|HH@ HL$ 虅HL$ τpHHK8Hu|HH@׈HL$ dHL$ uHHK8Hu|HH@HL$ /HL$ udrHHK8Hu[|HH@mHL$ HL$ erHHK8Hu&|HH@8HL$ ńHL$ frHHK8Hu{HH@HL$ 萄HL$ &hrHHK8Hu{HH@·HL$ [HL$ !ynHHK8Hu{HH@HL$ &HL$ nHHK8HuR{HH@dHL$ HL$ nHHK8Hu{HH@/HL$ 較HL$ R'nHHK8HuzHH@HL$ 臃HL$ =-nHHK8HuzHH@ņHL$ RHL$ X/nHHK8Hu~zHH@HL$ HL$ 1nHHK8HuIzHH@[HL$ HL$ 3nHHK8HuzHH@&HL$ 賂HL$ 5nHHK8HuyHH@HL$ ~HL$ t7nHHK8HuyHH@HL$ IHL$ rHHK8HuuyHH@HL$ HL$ PnHHK8Hu@yHH@RHL$ ߁HL$ RnHHK8Hu yHH@HL$ 誁HL$ TnHHK8HuxHH@脁HL$ uHL$ 諏rHHK8HuxHH@HL$ @HL$ \nHHK8HulxHH@~HL$ HL$ gnHHK8Hu7xHH@IHL$ րHL$ inHHK8HuxHH@HL$ 血HL$ knHHK8HuwHH@߃HL$ lHL$ RmnHHK8HuwHH@HL$ 7HL$ onHHK8HucwHH@uHL$ HL$ nHHK8Hu.wHH@@HL$ HL$ ÕnHHK8HuvHH@ HL$ HL$ nHHK8HuvHH@ւHL$ cHL$ 9nHHK8HuvHH@HL$ .HL$ 蔻nHHK8HuZvHH@lHL$ ~HL$ nHHK8Hu%vHH@7HL$ ~HL$ nHHK8HuuHH@HL$ ~HL$ pHHK8HuuHH@́HL$ Z~HL$ #pHHK8HuuHH@HL$ %~HL$ {(pHHK8HuQuHH@cHL$ }HL$ V1pHHK8HuuHH@.HL$ }HL$ pHHK8HutHH@HL$ }HL$ ,pHHK8HutHH@ĀHL$ Q}HL$ WlHHK8Hu}tHH@HL$ }HL$ bpHHK8HuHtHH@ZHL$ |HL$ pHHK8HutHH@%HL$ |HL$ 8pHHK8HusHH@HL$ }|HL$ pHHK8HusHH@HL$ H|HL$ pHHK8HutsHH@HL$ |HL$ ypHHK8Hu?sHH@QHL$ {HL$ pHHK8Hu sHH@HL$ {HL$ lHHK8HurHH@~HL$ t{HL$ :lHHK8HurHH@~HL$ ?{HL$ qHHK8HukrHH@}~HL$ {HL$ PqHHK8Hu6rHH@H~HL$ zHL$ qHHK8HurHH@~HL$ zHL$ ErHHK8HuqHH@}HL$ kzHL$ !mHHK8HuqHH@}HL$ 6zHL$ 3qHHK8HubqHH@t}HL$ zHL$ 5mHHK8Hu-qHH@?}HL$ yHL$ JmHHK8HupHH@ }HL$ yHL$ ZqHHK8HupHH@|HL$ byHL$ cqHHK8HupHH@|HL$ -yHL$ \mHHK8HuYpHH@k|HL$ xHL$ ~cmHHK8Hu$pHH@6|HL$ xHL$ YfmHHK8HuoHH@|HL$ xHL$ mmHHK8HuoHH@{HL$ YxHL$ vmHHK8HuoHH@{HL$ $xHL$ :{mHHK8HuPoHH@b{HL$ wHL$ mHHK8HuoHH@-{HL$ wHL$ 萏qHHK8HunHH@zHL$ wHL$ {qHHK8HunHH@zHL$ PwHL$ 趢qHHK8Hu|nHH@zHL$ wHL$ mHHK8HuGnHH@YzHL$ vHL$ mHHK8HunHH@$zHL$ vHL$ 1rHHK8HumHH@yHL$ |vHL$ uHHK8HumHH@yHL$ GvHL$ 譽uHHK8HusmHH@yHL$ vHL$ tHHK8Hu>mHH@PyHL$ uHL$ 3tHHK8Hu mHH@yHL$ uHK8HtHH;H@ xHC8H$HĀ]H\$HL$UH$HHHL$ nHHK8HulHH@xHL$ uHL$ 4,nHHK8HuJlHH@\xHL$ tHL$ ?9rHHK8HulHH@'xHL$ tHL$ 躳uHHK8HukHH@wHL$ tHL$ յuHHK8HukHH@wHL$ JtHL$ uHHK8HuvkHH@wHL$ tHL$ mHHK8HuAkHH@SwHL$ sHL$ &vuHHK8Hu kHH@wHL$ sHL$ SnHHK8HujHH@vHL$ vsHL$ nHHK8HujHH@vHL$ AsHL$ 'nHHK8HumjHH@vHL$ sHL$ nHHK8Hu8jHH@JvHL$ rHL$ nHHK8HujHH@vHL$ rHL$ H&rHHK8HuiHH@uHL$ mrHK8HtHH;H@ uHC8H$HĀ]HL$SHHHL$ fpHHK8HuLiHH@^uHL$ qHL$ AZoHHK8HuiHH@)uHL$ qHL$ FnHHK8HuhHH@tHL$ qHL$ pHHK8HuhHH@tHL$ LqHL$ tHHK8HuxhHH@tHL$ qHL$ ]tHHK8HuChHH@UtHL$ pHL$ HpHHK8HuhHH@ tHL$ pHL$ 7rHHK8HugHH@sHL$ xpHK8HtHH;H@ sHC8HĀ[H\$HL$UH$HHHL$ tHHK8HuOgHH@asHL$ oHL$ MqHHK8HugHH@,sHL$ oHL$ oVqHHK8HufHH@rHL$ oHL$ htHHK8HufHH@rHL$ OoHL$ ktHHK8Hu{fHH@rHL$ oHL$ 0/rHHK8HuFfHH@XrHL$ nHL$ kmHHK8HufHH@#rHL$ nHL$ vqHHK8HueHH@qHL$ {nHL$ qntHHK8HueHH@qHL$ FnHL$ vpHHK8HureHH@qHL$ nHL$ WypHHK8Hu=eHH@OqHL$ mHL$ R'nHHK8HueHH@qHL$ mHL$ EnHHK8HudHH@pHL$ rmHK8HtHH;H@ pHC8H$HĀ]HL$SHHHL$ &?pHHK8HuLdHH@^pHL$ lHL$ 12tHHK8HudHH@)pHL$ lHL$ sHHK8HucHH@oHL$ lHL$ gnHHK8HucHH@oHL$ LlHL$ nHHK8HuxcHH@oHL$ lHL$ =nHHK8HuCcHH@UoHL$ kHL$ oHHK8HucHH@ oHL$ kHL$ soHHK8HubHH@nHL$ xkHK8HtHH;H@ nHC8HĀ[H\$HL$UH$HHHL$ )rHHK8HuObHH@anHL$ jHL$ TmHHK8HubHH@,nHL$ jHL$ {qHHK8HuaHH@mHL$ jHL$ -nHHK8HuaHH@mHL$ OjHL$ pHHK8Hu{aHH@mHL$ jHL$ mHHK8HuFaHH@XmHL$ iHL$ KpHHK8HuaHH@#mHL$ iHL$ +pHHK8Hu`HH@lHL$ {iHL$ EmHHK8Hu`HH@lHL$ FiHL$ HmHHK8Hur`HH@lHL$ iHL$ lHHK8Hu=`HH@OlHL$ hHL$ rlHHK8Hu`HH@lHL$ hHL$ lHHK8Hu_HH@kHL$ 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T\PT\T\\\T\`mPv _\Y\]\P.]P.]0.] .]0H.].]-]-]0H`-]`-]@-]0-]0H-]-]-]-]0H]]]Ѐ] H  ]  ]  З]h^Л^____0HУ_H_P_`{_^P^^0^`_^^^_p`_`_0_ _`9HH_____H_   __  P_0ʃP_ PP _P_`````P`HHy`pPu`t`p@t`q`pq``q`p`o`@o`n`n`__@RЮ`````HH````@``````@`` RaRaHУOa`@R0` 0ʃ0ʃ0ʃ0ʃ@a`6a-a)a)a)aa0a`Ja`JaIaIaHH@aa@aa0aa aaHHbabapaa`aaH ca ca0aacaHHaaaa_ap_aH0a0aaa`9H@a[\`]\]\pY\Y\ T\PT\T\\\T\`ma a0a]\pbb@bPbУp ZcZc@Zc0ZcHH(Уcbb YcYcPXc@XcHHpWcpWcPWc@WcHYcYcYcYcHa0ʃ0ʃ0ʃWcWcWc @H(0=cWcWcWc2H0b @c ZcZcYcP4Hcp `aaaЎb Xc XcXcWc0H 0=0cc c0cc  cpc`c @HccccH0c0cccPHpc `cccc`cHH Rap cGd@] c0c ccpccddddHHddddHH`d`d ddHHp+dp:d:d:d:d H`;d;d;d;d@d@Adr p p ppPpp`pNqpppppp0ppqq@qq&q&q@*q 'q4q3q 5q 4q+q5q5qp@qCq=qGqKq 0Kq p{q{q  @pPppp`pp@pp}q q0q@q`~q~q~qPqqPqqq qpqqqq0q q HqqqqH0qqq@qH0qq qqqqqqqq0ʃq 0ʃ0ʃ0qrrrr`9Hq0PqqqpqHH0qqq qH`r`r@r0rHHPqPqPq@qHH `r 0ʃ0ʃ0ʃpp0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃ0ʃFr0ʃ0ʃ0ʃ0ʃ rer솀솀ererer@ererQsPs`Ps 0ʃ`Ps kPt `Zt ZtZtYtHttttHHttttHHttttHHPtPt@t0tHHttttHHttttHHttpt`tHH t tttHHttttHHttttHHttttHH0t0t ttHHttttHHttttHHtt@ttHHtt@t0tHHttttHHtt ttHH t tttHHPuPu@u0uHHuupu`uHH3v3v3v2vHH9v9v9v9vHH`7v`7v6v5vHH1v1v1vp1vHH9v9v9v9vHH1v1vp0v0vHH 4v 4v4v4vHHP5vP5v@5v05vHH8v8v7v7vHH`,v`,vP,v@,vHH0,v0,v ,v,vHH7v7v7vp7vHH`3v`3vP3v@3vHH`1v`1vP1v@1vHH5v5v5v5vHH 5v 5vp4v5vHHP0vP0v@0v00vHH.v.v.v-vHHP2vP2v@2v02vHH@;v@;v@:v0:vHH01v01v 1v1vHH2v2vp2v`2vHH5v5vp4v4vHH 2v 2v2v2vHH5v5v5v5vHH2v2v2v2vHH0v0vp0v`0vHH4v4vp4v`4vHH1v1v1v1vHH 0v 0v/v/vHH=v=v~>~=~=~HH0>~0>~P"} >~HH0o~0o~ o~o~HHo~o~n~n~HHn~n~n~n~HH  PvPvPvPvp PvPvPvPv Jdp0pP@P`@i@i0iHH0ʃ0ʃ0ʃ0ʃ0ʃp0ʃ0ʃ.GPyPy@y0yHH yyyxHССHHpp`PHHHH00 0Hр0pπ`PvRp @΀ @Ҁ0.΀)PvRp-,@΀P* ,р΀ PvR`@΀p  ЀPπ`PvRp @΀ @πPN΀JPvRM`M@΀ J Kрπ)PvRp-,@΀P* ,PрπЄPvR0@΀ PҀ@4΀p/PvR32@΀/ 1Ҁ0πPvR@΀𩁀 ુЀ`π𐁀PvRДP@΀0  PЀ@πЄPvR0@΀ dπ PvR@@΀ 0P g`΀PvR`@΀` @p p΀PvR@΀@ pH΀7PvRDA@΀9 >p0΀ڀPvRpꀀ@΀܀ P倀Hdpt΀`mPvRrr@΀m p`j΀SPvRfb@΀T [Ѐπ0PvR π` Ҁ}πvPvR{z@΀@w y(΀PvRP$!@΀ PπpPvR`P@΀ p*())0ʃP))`4`LMK@L0]\\\0ʃ*`mm)@n 悀0悀@悀P悀`悀p悀悀悀PЗ З0ʃ)hPP0PPP0PPP0PPP0PPP0PUnknown exceptionbad array new lengthstring too longC:\a\_work\_temp\protobuf\protobuf-21.12\src\google/protobuf/parse_context.h(cannot determine missing fields for lite message)C:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\message_lite.cc.Can't message of type "" because it is missing required fields: parse exceeded maximum protobuf size of 2GB: vector too longC:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\io\zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.C:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\io\zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: C:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\io\zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): INFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s L0X0`0h0Can't happenC:\a\_work\_temp\protobuf\protobuf-21.12\src\google\protobuf\arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899falsetrue\\x%02xinfnanty10invalid string position...\\?\\/UNKNOWNINVALID_ARGUMENT:, 'map/set too longunordered_map/set too longinvalid hash bucket count/\ was false.Stacktrace: Cpuai.onnx.trainingai.onnx.mlai.onnxai.onnx.preview.trainingnarrowing_errorVIWEFAttempt to use DefaultLogger but none has been registered.onnxruntime::logging::LoggingManager::SetDefaultLoggerSeverityC:\a\_work\1\s\include\onnxruntime\core/common/logging/logging.honnxruntime::IDataTransfer::CopyTensorAsync is not implementedC:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h!=C:\a\_work\1\s\include\onnxruntime\core/framework/tensor.h)IsTensor()Trying to get a Tensor, but got: C:\a\_work\1\s\include\onnxruntime\core/framework/ort_value.hOrtValue::GetTrying to get a TensorSeq, but got: OrtValue::GetMutableTrying to get a SparseTensor, but got: IsTensorSequence()IsSparseTensor()Integer overflowC:\a\_work\1\s\onnxruntime\core/common/safeint.hSafeIntExceptionHandler::SafeIntOnOverflowORT_LOAD_CONFIG_FROM_MODELelem_type_ != nullptrTensor sequence must contain only primitive typesC:\a\_work\1\s\onnxruntime\core/framework/TensorSeq.honnxruntime::TensorSeq::SetTypeonnxruntime::TensorSeq::GetAti < tensors_.size()IsSameDataType(tensor.Get())TensorSeq: tensor to be added has a different data type.onnxruntime::TensorSeq::Addtried creating tensor with negative value in shapeUnknown Exception, got size overflowNot able to find appropriate IDataTransfer to copy sparse datanot enough space: expected C:\a\_work\1\s\onnxruntime\core\session\onnxruntime_c_api.cc`anonymous-namespace'::GetDataTransfer`anonymous-namespace'::ValidateFillInputArgsStrings can only reside in CPU memoryOrtApis::FillSparseTensorCootried Filling sparse tensor with negative value in values shapetried Filling sparse tensor with negative value in block sparse indices shapeOrtApis::FillSparseTensorCsrCan not use strings in pre-allocated memory. Use CreateSparseTensorAsOrtValue() to allocate memory inside and copyOrtApis::FillSparseTensorBlockSparseOrtApis::UseCsrIndicesOrtApis::UseCooIndicesthe ort_value must contain a constructed tensorOrtApis::UseBlockSparseIndicesRegisterCustomOpsLibrary: Failed to load libraryUse GetStringTensor*() API to retrieve stringsRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryRegisterCustomOpsRegisterCustomOpsUsingFunction: Registration function was not foundRegisterCustomOpsUsingFunction: Registration function name must be specified.onnxruntime_profile_EnableOrtCustomOps: Custom operators in onnxruntime-extensions are not enabledstring buffer allocation failedlengths allocation failedinput array doesn't equal tensor sizeOutput buffer allocation failedOrtValue should contain a Tensor or a Sparse Tensorelement index is out of boundsThis API supports Tensors or SparseTensorsSparse Tensor does not contain sparse dataindex is out of boundsshape is invalidoutput buffer is too small. Use GetStringTensorDataLength.offsets buffer is not equal to tensor sizeout of indexbuffer size is too small for string elementindex out of rangeinternal errorInput is not of type sequence or map.Input is not of one of the supported sequence types.Input is not of one of the supported map types.input array is too shortin[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.OrtCreateValueImplSeqHelperAt least one element in the sequence is of a type different from others.Each element of the sequence should be either tensor or map.For map type num_values MUST be 2Unsupported input typeKey and value tensors have unequal number of elements.Either the key tensor or the value tensor has NumDimensions > 1Number of values should be at least 1.Key type is not supported yet.,opaque(ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typeOpaque type is not a non_tensor type!!!OrtApis::CreateOpaqueValueOrtApis::GetOpaqueValuenon_tensor_base != nullptrSpecified provider is not supported.Invalid build with no providers availablethis API does not support stringsinvalid location rangelocation dimensions do not match shape sizearena_extend_strategymax_memmax_dead_bytes_per_chunkinitial_chunk_size_bytesmax_power_of_two_extend_bytesinitial_growth_chunk_size_bytes1.17.1Invalid key found: ORT Build Info: git-branch=HEAD, git-commit-id=8f5c79cb63, build type=RelWithDebInfo, cmake cxx flags: /guard:cf /DWIN32 /D_WINDOWS /MP /DWINAPI_FAMILY=100 /DWINVER=0x0A00 /D_WIN32_WINNT=0x0A00 /DNTDDI_VERSION=0x0A000000 /Qspectre /O2 /Ob1 /DNDEBUG /EHsc /EHsc /wd26812 -DEIGEN_HAS_C99_MATHThe requested API version [%u] is not available, only API versions [1, %u] are supported in this build. Current ORT Version is: %s invalid vector subscriptTensor type mismatch. T onnxruntime::Tensor::MutableDatautils::IsPrimitiveDataType(dtype_)Invalid index requested for map type.onnxruntime::Tensor::DataAsSpanvalue_type != nullptrTensor must always contain primitive types. Found: Value type is not supported yet: OrtCreateValueImplMapHelper++index < c.size()Map is missing type entry for its valueonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,struct std::less<__int64>,class std::allocator,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,double,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,double> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,class std::basic_string,class std::allocator >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::basic_string,class std::allocator > > > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,__int64,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,__int64> > > >::checkonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator > >,class std::allocator,class std::allocator > > > > >::checkSequence is missing type entry for its elementonnxruntime::utils::ContainerChecker::IsContainerOfType,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > >,class std::allocator,class std::allocator >,float,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,float> > > > > >::checkonnxruntime::DataTypeImpl::GetType() == type_ != OrtCreateMapMLValuelen >= 0 && static_cast(len) < std::numeric_limits::max()onnxruntime::Tensor::Data: onnxruntime::logging::LoggingManager::DefaultLoggerC:\a\_work\1\s\onnxruntime\core\session\abi_session_options.ccFailed to load dynamic library OrtSessionOptions::RegisterCustomOpsLibraryFailed to unload handle for dynamic library graph_optimization_level is not validexecution_mode is not validSession config entry '' was not found. contains null pointersOutput buffer is not large enough for session config entryInput index: DMLMaximum string length for a provider options key/value is 1024. execution provider is not supported in this build. Provider options key/value cannot be emptyQNNOpenVINOOrtApis::SessionOptionsAppendExecutionProviderC:\a\_work\1\s\onnxruntime\core\session\provider_registration.ccWEBNNAZURESNPEXNNPACKUnknown provider name. Currently supported values are 'OPENVINO', 'SNPE', 'XNNPACK', 'QNN', 'WEBNN' and 'AZURE'VitisAIOutput [TypeInferenceError] expected to have tensor or sparse type expected to have tensor typeCPUExecutionProvideronnxruntime::OpKernel::ComputeAsyncvalue_case == ONNX_NAMESPACE::TypeProto::kTensorTypeOrtShapeInferContext::OrtShapeInferContextshape inference not yet supported for non-tensor typesOrtShapeInferContext::SetOutputTypeShapesymbolic and integer dims mismatch!C:\a\_work\1\s\onnxruntime\core\session\custom_ops.ccInvalid shape infoInvalid attribute.Size of data not large enough to hold a float.Failed to fetch type shape info for the index.Attribute does not exist.Size of data not large enough to hold an int64.Attribute has no int64 value.Attribute has no float value.Size of data not large enough to hold the array of floats.Size of data not large enough to hold the array of strings.Unknown attribute type.Size of data not large enough to hold the array of int64.Size of data not large enough to hold the string.Failed to fetch a stream hosting the requested resourceInvalid contextResult buffer is not large enoughNo requested allocator available::OrtKernelInfo output index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo output name::OrtKernelInfo input index is out of boundsOutput buffer is not large enough for ::OrtKernelInfo input nameOutput buffer is not large enough for ::OrtKernelInfo node name::OrtKernelInfo does not have an execution provider::OrtKernelInfo input does not have a type::OrtKernelInfo output does not have a typeUnsupported version 'onnxruntime::CustomOpKernel::CustomOpKernel::OrtKernelInfo cannot get a valid logger from its execution provider' in custom op ' to a custom op may be marked variadic.inputInputOutputi == count - 1onnxruntime::CreateSchema::::operator ()outputOnly the last . onnxruntime allows the overloading of an operator if all versions have the same number of declared 'has a different number of inputsoutputsdefined list of typesall typesAnother version of operator 'static_cast(i) != (is_input ? o->GetInputTypeCount(o) : o->GetOutputTypeCount(o))onnxruntime::CreateSchemacustom op registered at runtimeNo kernels to registers.ops.size() > 0input count does not matchonnxruntime::IsCompatibleThere 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.1 == undefinedndth input to be of optional typest input to keep same homogeneity input to keep same aritycustom op schemas mismatch, expecting input to be of variadic type output to be of optional type output to be of variadic type input to be of single typeoutput count does not match output to be of single typeonnxruntime::CreateCustomRegistry output to keep same homogeneity output to keep same arity`InlinedVector::at(size_type) const` failed bounds checkTensor type mismatch.type == dtype_) != new size (Tensor size (onnxruntime::Tensor::MutableDataRawonnxruntime::Tensor::DataRawshape_.Size() == new_shape.Size()onnxruntime::Tensor::Reshape Max:Validating no unexpected access using an invalid node_index. Got:C:\a\_work\1\s\include\onnxruntime\core/graph/graph.hnode_index < nodes_.size()onnxruntime::Graph::NodeAtIndexImplRequired output at index output_ptr is not present.Please fetch output tensor with specified shape.p_ml_valueonnxruntime::OpKernelContext::RequiredOutputC:\a\_work\1\s\include\onnxruntime\core/framework/op_kernel_context.honnxruntime::OpKernelContext::OutputIsSameDataType(tensor)onnxruntime::LoadDynamicLibraryFromProvideronnxruntime::ProviderSharedLibrary::EnsureProvider_SetHostC:\a\_work\1\s\onnxruntime\core\session\provider_bridge_ort.cconnxruntime_providers_shared.dllGetProvideronnxruntime::ProviderLibrary::Unloadonnxruntime::ProviderSharedLibrary::Unloadonnxruntime::ProviderLibrary::Getonnxruntime_providers_cann.dllonnxruntime_providers_rocm.dllonnxruntime_providers_cuda.dllonnxruntime_providers_cuda_ut.dllonnxruntime_providers_tensorrt.dllonnxruntime_providers_migraphx.dllonnxruntime_providers_dnnl.dllonnxruntime_providers_openvino.dlldevice_idnum_of_threadsdevice_typeenable_npu_fast_compileenable_opencl_throttlingdisable_dynamic_shapescache_dircontextTensorRT Provider not available, can't get interface for itonnxruntime::GetProviderInfo_TensorRTnum_streamsonnxruntime::TryGetProviderInfo_TensorRTonnxruntime::TryGetProviderInfo_CUDAonnxruntime::TryGetProviderInfo_ROCM is already in session option.The custom op domain name SessionOptionsAppendExecutionProvider_MIGraphX: Failed to load shared libraryAddTensorRTCustomOpDomainToSessionOptionCUDA and/or ROCM execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO_V2: Failed to load shared libraryTensorRT execution provider is not enabled in this build.CUDA execution provider is not enabled in this build.OrtSessionOptionsAppendExecutionProvider_Rocm: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_TensorRT: Failed to load shared librarySessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryoneDNN execution provider is not enabled in this build.SessionOptionsAppendExecutionProvider_CANN: Failed to load shared libraryCANN execution provider is not enabled in this build.ROCm execution provider is not enabled in this build.invalid unordered_map keyonnxruntime::OpKernelContext::InputRequired input at index Missing Input: input_ptronnxruntime::OpKernelContext::RequiredInputRelease_State_Create_State_Compute_onnxruntime::standalone::NodeRepo::RegisterCustomOpNodeSchemasUnable to find schema for node. Domain:'' op_type:kernel already mapped to existing nodeinvalid node input count: , expect: matching node is missingC:\a\_work\1\s\onnxruntime\core\session\standalone_op_invoker.cconnxruntime::standalone::StandAloneKernelContext::NumVariadicInputsarg_num < static_cast(input_count_)invalid arg_num.invalid node output countKernel definition was not found for node Domain:'onnxruntime::standalone::CreateOpstandalone_Attribute type not supported yet.onnxruntime::standalone::InvokeOpOrtMemoryInfo is nullEnv is nullProvided OrtMemoryInfo is nullPlease register the allocator as OrtDeviceAllocator even if the provided allocator has arena logic built-in. OrtArenaAllocator is reserved for internal arena logic based allocators only.Provided allocator is nullC:\a\_work\1\s\onnxruntime\core\session\ort_env.ccOrtEnv::Releaseenv_ptr == p_instance_.get()Size mismatch:mapped_feed_names_.size() == feed_names_.size()onnxruntime::SyncProvidersonnxruntime::IOBinding::SynchronizeInputsonnxruntime::IOBinding::BindInputC:\a\_work\1\s\onnxruntime\core\session\IOBinding.cc it.second= index=p_provideronnxruntime::IOBinding::SynchronizeOutputsSize mismatchmapped_output_names_.size() == output_names_.size()onnxruntime::IOBinding::BindOutputImplbad castinvalid stoi argument*stoi argument out of range MemoryType:DeviceType:Device:[ OrtAllocatorType:] DeviceId: OrtMemType: id:name:OrtMemoryInfo:[TensorrtExecutionProviderCUDAExecutionProviderDmlExecutionProviderPRIORITY_BASED } config_options: { use_deterministic_compute:DEFAULT graph_optimization_level: max_num_graph_transformation_steps: session_log_verbosity_level: session_log_severity_level: thread_pool_allow_spinning: use_per_session_threads: inter_op_param: intra_op_param: enable_mem_pattern: optimized_model_filepath: enable_profiling: execution_order: session_logid: profile_file_prefix: enable_cpu_mem_arena: enable_mem_reuse:Provider onnxruntime::ExecutionProviders::AddC:\a\_work\1\s\onnxruntime\core/framework/execution_providers.h execution_mode:Session Options { has already been registered.Could not find OrtValue with name 'onnxruntime::FeedsFetchesInfo::FeedsFetchesInfoC:\a\_work\1\s\onnxruntime\core/framework/feeds_fetches_manager.hIsGraphCaptured()onnxruntime::InferenceSession::CachedExecutionProviderForGraphReplay::ReplayGraphCached EP instance for graph replay is not set yet before calling ReplayGraph()C:\a\_work\1\s\onnxruntime\core/session/inference_session.hProvided type is not an optional sequence tensorIsOptionalSeqTensor(type)onnxruntime::utils::GetElementTypeFromOptionalSeqTensorProvided type is not an optional tensorIsOptionalTensor(type)onnxruntime::utils::GetElementTypeFromOptionalTensorC:\a\_work\1\s\onnxruntime\core/framework/mldata_type_utils.hMemcpyTransformer/ORTM] [json.exception.session.disable_quant_qdqsession.use_env_allocatorssession.load_model_formatsession.save_model_formatsession.set_denormal_as_zerosession.use_ort_model_bytes_directlysession.use_ort_model_bytes_for_initializerssession.disable_aot_function_inliningsession.inter_op.allow_spinningsession.intra_op.allow_spinningsession.strict_shape_type_inferencesession.allow_released_opsets_onlysession.intra_op_thread_affinitiesoptimization.minimal_build_optimizationssession.dynamic_block_basesession.force_spinning_stopsession.debug_layout_transformationsession.disable_cpu_ep_fallbacksession.optimized_model_external_initializers_file_namesession.optimized_model_external_initializers_min_size_in_bytes%Y-%m-%d_%H-%M-%SMemcpyFromHostMemcpyToHostShapememory.enable_memory_arena_shrinkagedisable_synchronize_execution_providersThe environment variable contained the value: are '0' and '1'. The only supported values for the environment variable Reading the provided model for the ORT configsave value of 'save' is only valid when saving an ORT format model.applyInvalid value for Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options.session_log_severity_level >= 0 && session_options.session_log_severity_level <= static_cast(logging::Severity::kFATAL)onnxruntime::GetSeverityononnxruntime::FinalizeSessionOptionsC:\a\_work\1\s\onnxruntime\core\session\inference_session.ccModelProto needs to be parsed to check for ORT config within itis_model_proto_parsedstatus.IsOK()onnxruntime::InferenceSession::ConstructorCommongraph_transformer_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()Creating and using per session threadpools since use_per_session_threads_ is trueoffFlush-to-zero and denormal-as-zero are onnxruntime::InferenceSession::ConstructorCommon::::operator ()Could not finalize session options while constructing the inference session. Error Message: Affinity string must not be empty!to.affinity_str.empty()custom join thread function not set for intra op thread poolto.custom_join_thread_fn--intra-opsession-Dynamic block base set to When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.session_env.EnvCreatedWithGlobalThreadPools()onnxruntime::InferenceSession::TraceSessionOptionsGiven model could not be parsed while creating inference session. Error message: -inter-opcustom join thread function not set for inter op thread poolFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falseError during EndProfiling(): onnxruntime::InferenceSession::{dtor}::::operator ()Unknown error during EndProfiling()onnxruntime::InferenceSession::~InferenceSessiononnxruntime::InferenceSession::InferenceSessionCould not parse model successfully while constructing the inference sessionresultSo disabling it for this session since it uses the DML Execution Provider.Having memory pattern enabled is not supported while using the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. Received nullptr for exec providerExecution providers must be registered before the session is initialized. onnxruntime::InferenceSession::RegisterExecutionProviderExecution providers must be registered before the session is initialized.onnxruntime::InferenceSession::SaveToOrtFormatCan't register custom op domains with ORT for onnxruntime::InferenceSession::AddCustomOpDomainsReceived nullptr for custom registryEncountered unknown exception in LoadWithLoader()model_loading_uri failed:Load model from Failed to save ORT format model to file: Exception during loading: This session already contains a loaded model.onnxruntime::InferenceSession::LoadWithLoaderModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.model_loading_from_saved_protoORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().Failed to load model because protobuf parsing failed.model_loading_arrayonnxruntime::InferenceSession::TransformGraphCastFloat16Transformeronnxruntime::LoadOrtModelBytes bytes were able to be read.onnxruntime::InferenceSession::TransformGraph::::operator ().onnxpost_layout_transform_step_onnxruntime::InferenceSession::TransformGraph::::operator ()ORT model verification failed.InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.) has saved runtime optimizations. They will be ignored. failed. Only onnxruntime::InferenceSession::LoadOrtModel::::operator ()onnxruntime::InferenceSession::LoadOrtModelWithLoaderThis session has already been initialized.The provided PrePackedWeightsContainer instance to be added to the session is nullThe session already has a PrePackedWeightsContainer instanceonnxruntime::`anonymous-namespace'::PartitionOrtFormatModelonnxruntime::`anonymous-namespace'::ApplyOrtFormatModelRuntimeOptimizationsThe old ORT format model (version ] is not supported in this build The ORT format model version [Missing Model. Invalid ORT format model.onnxruntime::InferenceSession::InitializeModel was not loadedModel was not loaded.Session has already been initialized.Exception during initialization: onnxruntime::InferenceSession::Initialize::::operator ()onnxruntime::InferenceSession::Initialize::::operator ()Initializing session.This session cannot use the CUDA Graph feature as requested by the user as the model has control flow nodes which can't be supported by CUDA Graphs. as all compute graph nodes have not been partitioned to the CUDA EP.This session cannot use the CUDA Graph feature as requested by the user as all compute graph nodes have not been partitioned to the CUDA EP.it is safe to use the CUDA Graph feature.Adding default CPU execution provider.This session will use the allocator registered with the environment.as the model has control flow nodes which can't be supported by CUDA Graphs.This session cannot use the CUDA Graph feature as requested by the user Use the CUDA Graph feature with caution. This model has shape massaging nodes that will execute on CPU. as all the graph nodes have not been assigned to This session cannot use the CUDA Graph feature as requested by the user as all the graph nodes have not been assigned to of the same shape as the representative input (common case), will match the shapes produced in the model for other inputs using the representative input used to capture the CUDA graph, As long as the intermediate shapes produced in the model NchwcTransformerSerializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.1024Session successfully initialized.This session will use the CUDA Graph feature as requested by the user.Conflicting session configuration: explicitly added the CPU EP to the session, but also disabled fallback to the CPU EP via session configuration options.This session contains graph nodes that are assigned to the default CPU EP, but fallback to CPU EP has been explicitly disabled by the user.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes. Got: Invalid rank for for the following indices Got invalid dimensions for Encountered unknown exception in Initialize()session_initialization Please fix either the inputs/outputs or the model. Expected: data type. Actual: (Unexpected Valid values kInput, kOutputarg_type == ArgType::kInput || arg_type == ArgType::kOutput index: ))()) , expected: ( has elements, but names has name: onnxruntime::InferenceSession::ValidateInputsOutputsfeedfetch elements.tensorsparse_tensor but received a sparse tensor but received a tensor sequenceInvalid but received a tensor' expected to be of type: with name: 'Replaying the captured onnxruntime::InferenceSession::RunSession was not initializedSession not initialized.seqAt least one output should be requested.Invalid output name: CUDA Graph for this model with tag: input name cannot be emptyNULL input supplied for input output name cannot be emptyfetch_unique_ptrs[i] != nullptrRunning with tag: Encountered unknown exception in Run()model_runStart another run for necessary memory allocation or graph capture.onnxruntime::InferenceSession::GetOverridableInitializersonnxruntime::InferenceSession::GetModelOutputsonnxruntime::InferenceSession::NewIOBindingProfiler is disabled.unknown exceptionintra op thread pool must have at least one thread for RunAsynconnxruntime::InferenceSession::GetModelMetadataonnxruntime::InferenceSession::GetModelInputs does not support TunableOp.). Invalid TuningResults (index=). Reason: onnxruntime::InferenceSession::EndProfilingCould not write a profile because no model was loaded.Cannot find execution provider onnxruntime::InferenceSession::SetTuningResultsUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: combination in the memory arena shrink list: Failed to load TuningResults (index=, enable TunableOp for usingCorrectly set TuningResults for Unable to shrink arena: onnxruntime::InferenceSession::ShrinkMemoryArenasInvalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Did not find an arena based allocator registered for device-id combination is not an arena based allocator: The registered allocator for device-id error message: onnxruntime::InferenceSession::CreateLoggerForRunonnxruntime::InferenceSession::AddPredefinedTransformersbad conversion961c151d2e87f2686a955a9be24d316f1362bf21 3.10.5list too longother_error.json_onnxruntime::ParseStringWithClassicLocaleC:\a\_work\1\s\include\onnxruntime\core/common/parse_string.hlocaltime_s(&local_tm, &in_time_t) == 0onnxruntime::`anonymous-namespace'::GetCurrentTimeString"Failed to parse value: "com.microsoftorg.pytorch.atencom.microsoft.experimentalcom.microsoft.nchwccom.ms.internal.nhwcMismatch between inferred and declared type. Inferred=[ShapeInferenceError] Unsupported Source/Target type= Declared=tensor(int64)tensor(float16)tensor(float)tensor(double)unknowntensor(uint32)tensor(uint64)tensor(int32)tensor(float8e5m2fnuz)tensor(uint8)tensor(uint16)tensor(int8)tensor(bfloat16)tensor(float8e4m3fn)tensor(float8e4m3fnuz)tensor(float8e5m2)seq(tensor(uint64))seq(tensor(int8))seq(tensor(int16))seq(tensor(int32))tensor(int16)seq(tensor(uint8))seq(tensor(uint16))seq(tensor(uint32))tensor(string)tensor(bool)tensor(complex64)tensor(complex128)seq(tensor(int64))seq(tensor(float16))seq(tensor(float))seq(tensor(double))seq(tensor(bfloat16))seq(tensor(string))seq(tensor(bool))seq(tensor(complex64))seq(tensor(complex128))Schema error: line , but it is already registered from file known by the checker., but its domain is notin onnx/defs/schema.h).forgot to update the version range in DomainToVersionRange ) from file version: (domain: Trying to register schema with name bumped the operator version but ] (usually, this means you in the inclusive range [, but its version is not An allocator for this device has already been registered for sharing.Only CPU devices are supported. Please call CreateAndRegisterAllocatorV2() for other device.intra-opinter-opC:\a\_work\1\s\onnxruntime\core\session\environment.ccTReceived invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.No allocator for this device has been registered for sharing.stringException caught: is not implemented in CreateAndRegisterAllocatorV2()XYConstrain to all fixed size tensor and sequence types. If the dtype attribute is not provided this must be a valid output type.valueort_configsession_optionstuning_resultsUnsupported value for intra_op_num_threads: , column at line onnxruntime::SetInterOpNumThreadsSetting inter_op_num_threads to Unsupported execution_mode value in ORT config: onnxruntime::SetExecutionModeonnxruntime::SetIntraOpNumThreadsC:\a\_work\1\s\onnxruntime\core\session\inference_session_utils.ccSetting intra_op_num_threads to Unsupported value for inter_op_num_threads: onnxruntime::SetGraphOptimizationLevelSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLSequential modeParallel modeSetting execution_mode to Setting graph_optimization_level to ORT_DISABLE_ALLepresultsvalidatorsJson stored in the `ort_config` key cannot be parsed. Error message: Unsupported graph_optimization_level value in ORT config: Unsupported value for enable_profiling option: onnxruntime::SetEnableProfilingSetting enable_profiling to ORT config json from the model: The Model Proto hasn't been checked for the ORT config json.Did not find session options in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseSessionOptionsFromModelProtoonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProto::::operator ()The Model Proto has already been checked for the ORT config json.Found session/run/environment configuration in the model file to be used while running the modelonnxruntime::inference_session_utils::JsonConfigParser::ParseOrtConfigJsonInModelProtoexecution_modeexecution_mode option in the model file must be an integergraph_optimization_levelgraph_optimization_level option in the model file must be an integerintra_op_num_threadsintra_op_num_threads option in the model file must be an integerinter_op_num_threadsinter_op_num_threads option in the model file must be an integer. Ignoring...Tuning results stored in the model file cannot be parsed. Error message: Found tuning results in the model file to be used while loading the modelonnxruntime::inference_session_utils::ParseTuningResultsFromModelMetadataenable_profilingenable_profiling option in the model file must be an integerIgnoring unsupported session option in ORT config: ' not foundkey 'cannot use at() with cannot use key() for non-object iteratorsbooleanbinarydiscardednumberinvalid map keynullobjectarraycannot get valuetype_errorinvalid_iteratorparse errorout_of_range; last read: 'unexpected ; expected parse_errorsyntax error while parsing - false literalnull literalstring literalnumber literalinvalid BOM; must be 0xEF 0xBB 0xBF if giveninvalid literaltrue literal':'','end of input'[''{'']''}'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signinvalid comment; missing closing '*/'invalid comment; expecting '/' or '*' after '/''[', '{', or a literalunknown tokeninvalid number; expected digit after '-'invalid number; expected digit after '.'invalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+D800..U+DBFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Enumber overflow parsing 'vector too longcannot compare iterators of different containersobject keyobject separatortype must be string, but is type must be object, but is type must be number, but is iterator does not fit current valueiterator out of rangecannot use erase() with type must be array, but is AzureExecutionProviderC:\a\_work\1\s\include\onnxruntime\core/optimizer/graph_transformer.honnxruntime::GraphTransformer::RecursetoCastonnxruntime::IsIsolatedFp16NodeOnCpuidx < input_defs.size()cast node to cast from float16 to float32 on cpuInsertedPrecisionFreeCast_InsertCastTransformer works on the assumption that `dtype` attribute holds an integer.dtypedtype_attribute->second.has_i()RemoveDuplicateCastTransformerC:\a\_work\1\s\onnxruntime\core\optimizer\insert_cast_transformer.cconnxruntime::InsertCastTransformer::ApplyImplonnxruntime::RemoveDuplicateCastTransformer::ApplyImplJsExecutionProviderArmNNExecutionProviderACLExecutionProviderROCMExecutionProviderQDQFinalCleanupTransformersession.qdqisint8allowedsession.x64quantprecisionsession.disable_double_qdq_removeroptimization.enable_gelu_approximationsession.enable_quant_qdq_cleanupAttentionFusionBiasGeluFusionBiasDropoutFusionCastEliminationBiasSoftmaxFusionCommonSubexpressionEliminationConvBNFusionConvDivMulFusionConvMulFusionConvAddFusionConstantSharingEmbedLayerNormFusionDynamicQuantizeMatMulFusionExpandExpandEliminationDoubleQDQPairsRemoverDivDropoutEliminateDropoutGemmActivationFusionGeluFusionGemmGemmSumFusionGatherToSplitFusionFastGeluFusionGeluApproximationGatherToSliceFusionIdentityEliminateIdentityGemmTransposeFusionMatMulScaleFusionMatMulIntegerToFloatFusionMatMul_BatchNormalization_FusionMatmulTransposeFusionSimplifiedLayerNormFusionLayerNormFusionMatMulAddFusionMatMulActivationFusionSubAddNotWhereFusionMulPad_FusionMatMulNoopEliminationPadQDQPropagationTransformerClipReluQuantRewriteQDQS8ToU8TransformerPreShapeNodeEliminationWhereClipQuantRewriteAvx2WeightS8ToU8TransformerSkipLayerNormFusionRocmBlasAltImplSliceEliminateSliceQuickGeluFusionReluReshapeFusionFuseReluClipLevel_RuleBasedTransformeronnxruntime::optimizer_utils::GenerateRewriteRulesUnsupported optimization level: TransposeOptimizer_TransposeOptimizerUnsqueezeUnsqueezeEliminationonnxruntime::optimizer_utils::GenerateRuleBasedGraphTransformerC:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_utils.cconnxruntime::optimizer_utils::GenerateTransformersForMinimalBuildonnxruntime::optimizer_utils::GenerateTransformersC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\ensure_unique_dq_for_node_unit.cconnxruntime::`anonymous-namespace'::EnsureUniqueDQForEachExplicitOutputEdgeonnxruntime::EnsureUniqueDQForNodeUnit::ApplyImplEnsureUniqueDQForNodeUnitDequantizeLinearAdded by /duplicated㎀VitisAIExecutionProviderMIGraphXExecutionProvideronnxruntime::Node::ForEachWithIndexSet session_options.log_severity_level=1 to see the detail logs before this message.. It might have negative impact on performance (including unable to run CUDA graph). Execution type 'Memcpyonnxruntime::TransformerMemcpyImpl::ProcessDefs' doesn't support memcpy onnxruntime::MemcpyTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\transformer_memcpy.cc for Memcpy nodes are added to the graph onnxruntime::TransformerMemcpyImpl::ProcessInitializers::::operator ()onnxruntime::TransformerMemcpyImpl::ProcessInitializersCopy from/to host memorydup_replacements.find(&arg) == dup_replacements.end()Add onnxruntime::TransformerMemcpyImpl::AddCopyNode after before onnxruntime::GraphTransformer::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer.cc modified: GraphTransformer with status: Bad optional accessFusedConvQLinearAveragePoolConcatLayout/Transpose optimization for channels_last failed: QLinearGlobalAveragePoolResizeC:\a\_work\1\s\onnxruntime\core\optimizer\graph_transformer_mgr.ccThis transformer is already registered onnxruntime::GraphTransformerManager::ApplyTransformersSplitSplit for Fused Gather nodessplitC:\a\_work\1\s\onnxruntime\core\optimizer\gather_fusion.cconnxruntime::GatherToSplitFusion::ApplyImplaxisGatherrange_input_defs.size() == 3Rangeonnxruntime::GatherToSliceFusion::ApplyImplSqueezeAxesInitializernum_outputsaxesSqueezeSqueeze for Fused Gather nodesSlice for Fused Gather nodesSliceAxesInitializerUnsqueezeAxesInitializerUnsqueeze_Unsqueeze for Fused Gather nodesunsqueeze_output_startConstantFoldingUnable to constant fold. InlineIfSubgraph failed node '': C:\a\_work\1\s\onnxruntime\core\optimizer\constant_folding.cconnxruntime::ConstantFoldIfNodeIf node condition initializer: `' is expected to have a single boolean elementendUnsupported output type of . Can't constant fold fetches.size() == node->OutputDefs().size()Could not find a CPU kernel and hence can't constant fold QuantizeLinearIfonnxruntime::ConstantFolding::ApplyImplUnsupported data type: C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_scale_fusion.cconnxruntime::`anonymous-namespace'::GetScalarConstantInitializershapeConstant initializer NodeArg shape should not be null. NodeArg: FusedMatMulonnxruntime::`anonymous-namespace'::GetOutputNodeMergesoutput_node.OutputDefs().size() == 1onnxruntime::`anonymous-namespace'::GetInputNodeMergesinput_node.InputDefs().size() == 2 && scale_and_index->second < 2mul_inputs.size() == 2onnxruntime::`anonymous-namespace'::GetScaleFromNodediv_inputs.size() == 2onnxruntime::MatMulScaleFusion::ApplyImpl_FusedMatMulAndScaleFused MatMul and Scalealphaonnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget<__int64>::operator ()onnxruntime::`anonymous-namespace'::ExtractScalarAsFloatDispatchTarget::operator ()Start MatchGemmSubgraphSlice parameter is not expected. Input index:Expected value:Slice ends is less than INT_MAXSlice does not have enough number of inputsC:\a\_work\1\s\onnxruntime\core/optimizer/attention_fusion_helper.honnxruntime::AttentionFusionHelper::CheckSliceParametersinput_indices.size() == expected_values.size() && input_indices.size() > 0Gemm bias shape not expectedGemm bias is not constantGemm does not have 3 inputsOutput edge count not expected for nodes in gemm pathInput of reshape_before_gemm is not the input of subgraphFaild to match gemm pathReshapeonnxruntime::AttentionFusionHelper::MatchGemmSubgraphunsqueeze_after_gather axes value not expectedOutput edge count not expected for nodes in gemm gather pathFaild to match gemm gather pathconcat_after_gather input 2 does not have expected valueconcat_after_gather does not have expected number of inputs or output edgesFaild to match concat node for Gather pathsconcat first input value is not -1CheckSliceParameters return falseGemm weight is not constant initializerGemm bias shape is not expectedGemm bias is not constant initializeronnxruntime::AttentionFusionHelper::ValidateGemmInitializerStart ValidateGemmInitializerPass MatchGemmSubgraphgather input 1 value is not expectedgather axis value not expectedExpect mask data type is uint8 or floatMask is neither unidirectional nor all onesThis optimizer does not support external data for unidirectional mask right nowunidir mask shape not expectedonnxruntime::AttentionFusionHelper::ValidateUnidirMaskunidir mask is not constantPass ValidateGemmInitializerGemm weight shape is not expectedCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for last_sliceDiv and Shape1 does not have edgeOutput edge count not expected for nodes in path 1 of unidirectional maskFaild to match path 1 for unidirectional maskFaild to match the path (Div-->Where-->Add) for unidirectional maskonnxruntime::AttentionFusionHelper::MatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphDiv and Shape does not have edgeFaild to match path 4 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 2 for unidirectional maskCheckSliceParameters returns false for slice1ValidateUnidirMask returns false for mask_sliceOutput edge count not expected for SoftmaxFailed to find Softmax nodeSoftmaxonnxruntime::AttentionFusionHelper::MatchInputMaskSubgraphStart MatchInputMaskSubgraphPass MatchUnidirMaskSubgraphCheckSliceParameters return false for slice2Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskPass MatchInputMaskSubgraphmask_mul const input not matchedmask_sub const input not matchedmask_unsqueeze_2 axes not matched. Expect: 2mask_unsqueeze_1 axes not matched. Expect: 1Softmax attribute axis is expected to be 3Output edge count not expected for mask nodesFailed to find path for maskFailed to find reshape shape path 2Failed to find reshape shape path 1equal const not matched.Failed to find shape pathwhere const not matched.Failed to find mask pathEqualStart MatchInputMaskSubgraphDistilBertFailed to find path for present_v and past_vFailed to find path for present_kFailed to find path for past_kTransposeonnxruntime::AttentionFusionHelper::MatchPastSubgraphStart MatchPastSubgraphPass MatchInputMaskSubgraphDistilBertgather indices not matched.past_k_gather indices != 0past_v_gather indices != 1present_v_unsqueeze axes value not expectedpresent_k_unsqueeze axes value not expectedpresent_k_transpose perm attribute not matchedpast_k_transpose perm attribute not matchedpermFailed to match v_concatFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Output edge count not expected for nodes in path vonnxruntime::AttentionFusionHelper::CheckNodesInPathVStart CheckNodesInPathVonnxruntime::AttentionFusionHelper::CheckDistilBertReshapeShapePass MatchPastSubgraphOutput edge count not expected for nodes in past subgraphpast_v_gather and past_k_gather does not have same past inputq_reshape const not matchedonnxruntime::AttentionFusionHelper::CheckNodesInPathQStart CheckNodesInPathQreshape initializer value is not expectedPass CheckNodesInPathVv_reshape initializer value is not expectedhidden_size != num_heads * head_sizeFailed in match v_transpose attribute perm. Expected: 0, 2, 1, 3k_reshape const not matchedk_transpose perm attribute not matchedk_transpose has not perm attributeonnxruntime::AttentionFusionHelper::CheckNodesInPathKStart CheckNodesInPathKPass CheckNodesInPathQq_transpose perm attribute not matchedqk_div const not matched.Faild to find path v to SplitFaild to find path to qkv_matmulonnxruntime::AttentionFusionHelper::FuseGptAttentionStart FuseGptAttentionCast mask from int64 to int32Mask_Int32Pass CheckNodesInPathKopt_k_transpose perm attribute not matchedUsing transpose optimized patternCheckNodesInPathQ returns falseq and v are not from same Split nodeFailed to find path for qMatchUnidirMaskSubgraph returns NULLMatchInputMaskSubgraph returns falseCheckNodesInPathV return falseunidirectionalnum_headsAttentionFused Attention subgraphs MatchPastSubgraph returns falseCheckNodesInPathK returns falsek and v are not from same Split nodeFailed to find path for kMask data type is not int32 or int64 or float32C:\a\_work\1\s\onnxruntime\core\optimizer\attention_fusion.cconnxruntime::ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownqkv_biasqkv_weightsFused an attention node for GPT.mask_filter_valuek root is not layer normq_matmul and q_add shape not matchedq root should be layer normalizationonnxruntime::FuseSubGraphQKImplTotal fused Attention node count: shape of layer norm bias tensor not expectedLayerNormalizationonnxruntime::AttentionFusion::ApplyImplFaild to find path vonnxruntime::FuseSubGraphQKDistilBertFused an attention node.onnxruntime::FuseSubGraphQKFailed to convert mask to int32Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to load Q, K and V weights, or data type is not float or float16.k_matmul and k_add shape not matchedFailed in match input mask subgraphFailed in match v_matmul and v_add input shapeOutput edge count not expected for Add or MatMul in path vonnxruntime::AttentionFusion::FuseSubGraphbn_scale_tensor_protoepsilonConvBnFusion_W_conv_B_tensor_protoconv_W_tensor_protobn_var_tensor_protobn_mean_tensor_protobn_B_tensor_protoC:\a\_work\1\s\onnxruntime\core\optimizer\conv_bn_fusion.cconnxruntime::ConvBNFusion::ApplyBatchNormalizationConvBnFusion_BN_B_C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_s8_to_u8.cconnxruntime::QDQS8ToU8Transformer::ApplyImplqdq_s8_to_u8_quantqdq_s8_to_u8_zp_conversiondropDQArgMax1DQAveragePoolLeakyReluGlobalAveragePoolSigmoid2DQdropSplitQDQdropdrop_no_int16_supportMaxPool*DQQDQSelectorActionTransformerauto_padNOTSETpadsmodeconstantTanhindex < nodes_.size() && ((node = nodes_[index]) != nullptr || !required)onnxruntime::NodesToOptimize::GetNodeC:\a\_work\1\s\onnxruntime\core/optimizer/selectors_actions/helpers.hactivation != nullptronnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::ExtraAttributesactivationFailed to get Clip min/max constants.optimizer_utils::GetClipConstantMinMax(state.graph, *activation, min, max)betaactivation_paramsExpected Conv then Add.HardSigmoidNhwcConvNhwcFusedConv and domain: Unsupported operator: onnxruntime::`anonymous-namespace'::actions::FuseConvActivationAction::OpTypeC:\a\_work\1\s\onnxruntime\core\optimizer\conv_activation_fusion.ccExpected activation node.conv.GetOutputEdgesCount() == 1 && conv.OutputNodesBegin()->OpType() == "Add"onnxruntime::`anonymous-namespace'::actions::FuseConvAddRelu::ValueMovesConvActConvAddReluConvActivationFusiononnxruntime::MatMulActivationFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_activation_fusion.cc with activation FusedMatMulActivation_FusedActivationactivation_UnsqueezeElimination_UnsqueezeElimination cannot remove node onnxruntime::UnsqueezeElimination::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\unsqueeze_elimination.cconnxruntime::QuickGeluFusion::ApplyImplQuickGeluC:\a\_work\1\s\onnxruntime\core\optimizer\quick_gelu_fusion.cc_min_zero_constantC:\a\_work\1\s\onnxruntime\core\optimizer\relu_clip_fusion.ccFuseReluClip_minonnxruntime::FuseReluClip::ApplyUnexpected data type for Clip 'min' input of @Gelu approximationC:\a\_work\1\s\onnxruntime\core\optimizer\gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: FastGeluBiasGeluonnxruntime::GeluApproximation::ApplyImplGeluCannot replace concat node with initializer:ConcatTrainingonnxruntime::ReshapeFusion::Fuse_Subgraphonnxruntime::ReshapeFusion::ApplyImplallowzeroC:\a\_work\1\s\onnxruntime\core\optimizer\reshape_fusion.ccTotal fused reshape node count: Fused reshape node: endsstartsnew_gemm_input_defs.size() == 3transBC:\a\_work\1\s\onnxruntime\core\optimizer\gemm_sum_fusion.cconnxruntime::GemmSumFusion::ApplyFused Gemm with Sumnew_gemm_output_defs.size() == 1gemm_input_edge.src_arg_index < 2_sum_transformedtransA!sum_input_movedsum_output_edge.src_arg_index == 0sum_node.GetOutputEdgesCount() == 0graph.RemoveNode(gemm_node.Index())Sumgraph.RemoveNode(sum_node.Index())onnxruntime::GemmSumFusion::SatisfyConditionother_sum_input != nullptr and onnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromNodeToOutputnode_output_def_idx >= 0 && static_cast(node_output_def_idx) < node_outputs.size()Invalid node index onnxruntime::graph_utils::ExtendedGraphEdge::GetNodeAtEndnode != nullptronnxruntime::graph_utils::ExtendedGraphEdge::GetMutableNodeAtEndC:\a\_work\1\s\onnxruntime\core/graph/extended_graph_edge.honnxruntime::graph_utils::ExtendedGraphEdge::TryCreateFromInputOrInitializerToNodenode_input_def_idx >= 0 && static_cast(node_input_def_idx) < node_inputs.size()Inserting Q/DQ pair between node ("_pre_qInserted by QDQPropagationTransformer_post_dqFailed to set op schema for added Q node.At least one graph node must be specified in the propagation edge.onnxruntime::`anonymous-namespace'::InsertQDQPairsrc_node || dst_node".C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_propagation.cc", index: at NodeArg "onnxruntime::QDQPropagationTransformer::ApplyImplFailed to set op schema for added DQ node._dqonnxruntime::`anonymous-namespace'::GetPreviousPropagationEdgesrc_node != nullptronnxruntime::`anonymous-namespace'::GetNextPropagationEdgedst_node != nullptronnxruntime::`anonymous-namespace'::PropagateQBackwardonnxruntime::`anonymous-namespace'::PropagateDQForward_Int32Cast Input from int64 to int32onnxruntime::CheckInputC:\a\_work\1\s\onnxruntime\core\optimizer\embed_layer_norm_fusion.cc_CastInput shape is unknown or not 2D, or data type unknowntwo paths share the same shapeNonZeroSecond input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.onnxruntime::MatchPositionEmbeddingSubgraphsFromGatherThe first input of Range should be a constant with value 0.ConstantOfShapeOutput edge count not expected for nodes in path1.onnxruntime::MatchInputToConcatSubgraphOutput edge count not expected for nodes in path 1 of position shape.Input data type is not int32 or int64Failed to find path 1 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.EmbedLayerNormalizationWord embedding shape not expected.mask_indexfused EmbedLayerNorm subgraphs Failed to get initializer tensor.Position embedding shape not matched.onnxruntime::FuseSubGraphInput is expected to have dim value in all dimensions.Failed to match Shape node. The parent of shape nodes are expected to be input_ids.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match position subgraph.position_embeddingsOptional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.onnxruntime::EmbedLayerNormFusion::ApplyImplBeta should be of shape (hidden_size). onnxruntime::FuseSubGraphDistilBertFailed to get position embedding weights.Position embedding shape is not expected.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Input id is not valid. Segment id is not valid. YieldOponnxruntime::DynamicQuantizeMatMulFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\dynamic_quantize_matmul_fusion.ccDynamicQuantizeMatMulMatMulIntegerToFloatDynamicQuantizeLinearThe model has input 'onnxruntime::FreeDimensionOverrideTransformer::ApplyImplonnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformerC:\a\_work\1\s\onnxruntime\core\optimizer\free_dim_override_transformer.ccFreeDimensionOverrideTransformerInvalid free dimension override.with a fixed dimension size ' Conflicting free dimension overrides.which does not equal the specified override of fused Matmul and Add C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_add_fusion.cconnxruntime::MatMulAddFusion::ApplyImplFusedGemmfused Gemm with activation C:\a\_work\1\s\onnxruntime\core\optimizer\gemm_activation_fusion.ccfused SoftsignSoftplusSeluEluonnxruntime::GemmActivationFusion::ApplyImplThresholdedReluParametricSoftplusScaledTanhC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\clip_quantizelinear.cconnxruntime::GetQConstantLowerUpperUnexpected data type for QuantizeLinear input y_zero_point of ConvAddActivationFusionConvAddActonnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ValueMovesC:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_act_fusion.cconnxruntime::`anonymous-namespace'::actions::FuseConvAddActivationAction::ExtraAttributesQGemmQAttentionweight_zp_s8_2_u8QLinearConvQLinearMatMulMatMulInteger unable to locate recurrence tensor or its zero point value,DynamicQuantizeLSTMonnxruntime::Avx2WeightS8ToU8Transformer::ApplyImpl int8 overflow might impact precision !C:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\avx2_weight_s8_to_u8.cconnxruntime::TryConvertDynamicQuantizeLSTMUnable transforming DynamicQuantizeLSTM operator, cannot locate recurrence tensor of const int8 type,Fused Gemm with Transpose_transformedtransBatchAtransBatchBfused MatMul and Transpose MatMul_With_Transposeonnxruntime::ReorderCastAndTransposeCreated a new Cast node to interchange Cast and Transpose nodesCreated a new Transpose node to interchange Cast and Transpose nodesonnxruntime::MatmulTransposeFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_transpose_fusion.cc!node_consumers.empty()onnxruntime::UpdateConsumerCountcast != nullptrtranspose_node.InputDefs().size() == 1onnxruntime::GetTransposePermsdeque too longmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_onnxruntime::ConvMulFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\conv_mul_fusion.cconnxruntime::RocmBlasAltImpl::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\rocm_blas_alt_impl.cc__backwardpassSimplifiedLayerNormalizationPowfused LayerNorm subgraphs stash_typeonnxruntime::SimplifiedLayerNormFusion::ApplyImplonnxruntime::LayerNormFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\layer_norm_fusion.ccReduceMeanSqrt SkipLayerNormalizationCast Input to floatonnxruntime::SkipLayerNormFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\skip_layer_norm_fusion.ccfused SkipLayerNorm subgraphs _Float@dilationskernel_shapeSAME_UPPERSAME_LOWERreorderReorderInputstridesReorderOutputchannelsnearestlinearspatial_bn_nchwconnxruntime::NchwcTransformerImpl::TransformConvC:\a\_work\1\s\onnxruntime\core\optimizer\nchwc_transformer.cc_nchwcreshapeVALIDgroupBuffer overflowoutput_channels <= nchwc_output_channelsGlobalMaxPoolonnxruntime::NchwcTransformer::ApplyImplnearest_modefloorUpsamplescalescoordinate_transformation_modeasymmetricalign_cornershalf_pixelC:\a\_work\1\s\onnxruntime\core/providers/common.honnxruntime::HandleNegativeAxis is not in valid range [-IsAxisInRange(axis, tensor_rank)axis is_inner_broadcastBiasSoftmaxC:\a\_work\1\s\onnxruntime\core\optimizer\bias_softmax_fusion.cconnxruntime::BiasSoftmaxFusion::ApplyImpl into softmax(input + bias)xێDoubleQDQRemoved_ of node [Not eliminating output C:\a\_work\1\s\onnxruntime\core\optimizer\common_subexpression_elimination.cconnxruntime::CommonSubexpressionElimination::ApplyImpl] because it's the graph's output.representative.output_index != kInvalidOutputIndexonnxruntime::`anonymous-namespace'::CleanUpNodeSequenceCleaning up back-to-back nodes: onnxruntime::QDQFinalCleanupTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_final_cleanup.cc" and with name "NotTranspose optimizer failed: C:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimizer.cconnxruntime::TransposeOptimizer::ApplyImplvar_tensormatmul_b_tensorbias_tensormean_tensoronnxruntime::MatmulBNFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\matmul_bn_fusion.ccscale_tensorGenerated from Matmul BatchNormalization fusionMatMulBnFusion_GemmMatMulBnFusion_GemmB_MatMulBnFusion_GemmBiasonnxruntime::NhwcTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\nhwc_transformer.ccNhwcTransformerT1BitmaskDropoutBiasDropoutonnxruntime::BiasDropoutFusion::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\bias_dropout_fusion.ccBitmaskBiasDropoutfused Add-Dropout-(Add) for C:\a\_work\1\s\onnxruntime\core\optimizer\matmul_integer_to_float.cconnxruntime::MatMulIntegerToFloatFusion::ApplyImplonnxruntime::RuleBasedGraphTransformer::ApplyImplC:\a\_work\1\s\onnxruntime\core\optimizer\rule_based_graph_transformer.cconnxruntime::RuleBasedGraphTransformer::ApplyRulesOnNodeC:\a\_work\1\s\onnxruntime\core\optimizer\gelu_fusion.cconnxruntime::GeluFusion::ApplyImplfused Gelu subgraphs Erffused Add and GeluC:\a\_work\1\s\onnxruntime\core\optimizer\bias_gelu_fusion.cconnxruntime::BiasGeluFusion::ApplyImplfused GPT2Gelu subgraphs fast_gelu_outputC:\a\_work\1\s\onnxruntime\core\optimizer\fast_gelu_fusion.cconnxruntime::FastGeluFusion::ApplyImpl@onnxruntime::ConstantSharing::ApplyImplTotal shared scalar initializer count: C:\a\_work\1\s\onnxruntime\core\optimizer\constant_sharing.ccortshared_  onnxruntime::ConvAddFusion::ApplyC:\a\_work\1\s\onnxruntime\core\optimizer\conv_add_fusion.ccadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_q_or_dq_input_defs.size() >= 2onnxruntime::QDQ::QOrDQNodeHasConstantScalarScaleAndZeroPointC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\qdq_util.ccNhwcMaxPoolstorage_ordercom.microsoft.QuantizeLinearcom.microsoft.DequantizeLinearcom.microsoft.QLinearAddcom.microsoft.QLinearAveragePoolcom.microsoft.QLinearConcatcom.microsoft.QLinearGlobalAveragePoolcom.microsoft.QLinearLeakyRelucom.microsoft.QLinearMulcom.microsoft.QLinearReduceMeancom.microsoft.QLinearSigmoidnode_arg_ != nullptronnxruntime::ApiGraph::GetValueInfoFailed to find initializer for name: successonnxruntime::ApiGraph::TransposeInitializerFailed to find initializer to reshape with name onnxruntime::ApiGraph::ReshapeInitializer to have different number of elementsCannot reshape initializer new_num_elts == old_num_elts_outAdded in transpose optimizer domain not found in opset imports.opset_import_iter != domain_to_version_map.end()onnxruntime::GetSinceVersionForNewOpAn entry for this node should be added in kLayoutTransformationPotentiallyAddedOps. does not match rank Permutation length perm.size() == gsl::narrow_cast(shape_proto->dim_size())onnxruntime::ApiValueInfo::PermuteDimsC:\a\_work\1\s\onnxruntime\core\optimizer\transpose_optimization\ort_optimizer_api_impl.cc out of bounds for shape Permutation entry 0 <= p && p_int < shape_proto->dim_size()Failed to get size of TensorProtosize >= 0onnxruntime::ApiTensor::NumElementsonnxruntime::ApiTensor::DataNo NodeArg found for name Transpose Optimizer is adding an unexpected node: since_version.has_value()const_transpose_optimizerExisting destination type is not compatible with source type.dst_type->value_case() == src_type->value_case() && (!dst_data_element_type_present || dst_data_element_type == src_data_element_type)onnxruntime::ApiGraph::CopyValueInfoI0㎀ I0㎀ I0㎀I0\ꎀI0\ꎀ I0\ꎀ I0pގI0pގ I0pގI0pގI0pގI0펀 I0펀 I0펀I0ꎀI0ꎀ I0ꎀ I0 I0 I0ᎀ I0ᎀ I0ᎀ  ㎀ XI  p3  펀AbsCeilFloorNegRoundIsInfIsNaNShrinkSignCeluHardSwishSinCosTanSinhCoshkeepdimsReduceSumnoop_with_empty_axesExpLogReciprocalGreaterOrEqualLessOrEqualMeanCastLikeReduceLogSumReduceLogSumExpReduceMaxReduceMinReduceProdReduceSumSquareReduceL1ReduceL2ArgMinTileHardmaxLogSoftmaxAsinAcosAtanAsinhAcoshAtanhMaxMinAndOrXorModPReluBitShiftGreaterLessUnsupported ONNX opset: InstanceNormalizationConvTransposeLpPoolMaxUnpoolGlobalLpPoolLRNGridSampleDepthToSpaceSpaceToDepthcalled_ == 1onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnceExpecting the same sizesize() == other.size()onnxruntime::Initializer::subonnxruntime::Initializer::mulonnxruntime::Initializer::divAxis must be non-negativeaxis >= 0onnxruntime::Initializer::scale_by_axisInvalid other(scalers) sizescalers.size() == 1 || (column_major ? scalers.size() == block_size : scalers.size() == num_blocks)Initializer must have a datatypeutils::HasDataType(tensor_proto)onnxruntime::Initializer::InitializerC:\a\_work\1\s\onnxruntime\core\optimizer\initializer.ccmodel_path must not be empty. Ensure that a path is provided when the model is created or loaded.!model_path.IsEmpty()Expecting the same data typedata_type() == other.data_type()onnxruntime::Initializer::addonnxruntime::Tensor::MutableDataAsSpanRandomUniformRandomUniformLikeRandomNormalMultinomialRandomNormalLikeonnxruntime::optimizer_utils::GetClipConstantMinMax::::operator ()Unexpected data type for Clip input of maxC:\a\_work\1\s\onnxruntime\core\optimizer\utils.cc펀㎀@[h[P[[x[Failed to get allocator for optimizerC:\a\_work\1\s\onnxruntime\core\optimizer\optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Infoallocator_ptr_Tried to allocate without valid type information, ort_value index=onnxruntime::TryCreateKernelonnxruntime::OptimizerExecutionFrame::Info::{ctor}::::operator ()C:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\selector_action_transformer.cconnxruntime::SelectorActionRegistry::RegisterSelectorAndActiononnxruntime::MatchAndProcessMatched Not enough produced nodes in the runtime optimization record.onnxruntime::SelectorActionTransformer::ApplySelectorsAndActions).onnxruntime::SetOpSinceVersionForProducedNodesNew node op () does not match produced node op in runtime optimization record (onnxruntime::SelectorActionTransformer::ApplySavedRuntimeOptimizationsToo many produced nodes in the runtime optimization record.Applying runtime optimization action for transformer Nodes to optimize are not valid, skipping action.Missing action inserted_in_name_to_entryExisting registration with name QLineargenerated at runtimeinit_optional_zero_point_uint8_b33f88f7-c464-43e3-8692-97ac832bb14ainit_optional_zero_point_int8_b33fd0fa-cd7b-4b10-ae5a-df64cabfe1f8opsetC:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\actions.cconnxruntime::CreateReplacementNodeonnxruntime::MergeIntoTarget::RunFailed to set node op schema.Failed to remove node.onnxruntime::ReplaceWithNew::Runonnxruntime::ReplaceWithNew::RunForSaveIndex out of rangeNode index value is too large to save to ORT format model: node_idx <= NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::`anonymous-namespace'::MoveInputOutputImplC:\a\_work\1\s\onnxruntime\core\optimizer\selectors_actions\helpers.cctarget_node != NodesToOptimizeIndices::kEmptyNodeIndexonnxruntime::NodesToOptimizeIndicesBuilder::Buildonnxruntime::GetNodesToOptimizeIndices::::operator ()A target node must be set.onnxruntime::MoveInputOutputFlattenLpNormalizationTopKC:\a\_work\1\s\onnxruntime\core\optimizer\qdq_transformer\selectors_actions\shared\utils.cconnxruntime::QDQ::ValidateNodeGroupDQNodesQDQ node group cannot have DQ that doesn't have a single output edge to the target node. DQ node: , target node: QDQ node group cannot have DQ node that produces a graph output. DQ node: DnnlExecutionProviderQNNExecutionProviderRknpuExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderTvmExecutionProviderXnnpackExecutionProviderCoreMLExecutionProviderWebNNExecutionProviderCANNExecutionProviderprovider.name.size() <= kMaxExecutionProviderNameLen name shorterMake the EP:C:\a\_work\1\s\onnxruntime\core\providers\get_execution_providers.cconnxruntime::GetAvailableExecutionProviderNames::::operator ()xH䎀xێxg0gg(䎀Hgg0ێgHێ`ێ`ghgh؎^onnxruntime::RegisterOnnxOperatorKernelsonnxruntime::CPUExecutionProvider::GetKernelRegistryonnxruntime::RegisterCPUKernelsonnxruntime::ml::RegisterOnnxMLOperatorKernelsC:\a\_work\1\s\onnxruntime\core\providers\cpu\cpu_execution_provider.ccv >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range.C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBase[ perm size: does not match input rank: is repeated.perm: does not align with rank of input data: Transpose not implemented for empty tensors.onnxruntime::DoTransposeImpl(local_source >= source) && (local_source < source + num_blocks * blocksize)C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\transpose.cconnxruntime::DoTransposeEltWiseTranspose of element size not supported in this build. Size=(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)num_axes > 0input_tensor_ptr != nullptrMismatched data types between input and output Tensors. (local_source >= source) && (local_source < source + num_blocks)onnxruntime::Transpose::Computeonnxruntime::IncrementIndexAndComputeOffsetSetupnaxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.onnxruntime::TypedDoTransposeEltWise(local_source >= source) && (local_source < source + sizeof(T) * num_blocks)0 == center_point_box_ || 1 == center_point_box_center_point_box only support 0 or 1center_point_boxboxes_tensorNonMaxSuppressionC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBaseboxes must be a 3D tensor.scores_tensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\non_max_suppression.cconnxruntime::NonMaxSuppressionBase::PrepareComputeThe most inner dimension in boxes must have 4 data.boxes and scores should have same spatial_dimension.boxes and scores should have same num_batches.scores must be a 3D tensor.output != nullptronnxruntime::NonMaxSuppression::Computeonnxruntime::NonMaxSuppressionBase::GetThresholdsFromInputsiou_threshold must be in range [0, 1].onnxruntime::GatherBase::GatherBaseinfo.GetAttr("axis", &axis_).IsOK()Missing/Invalid 'axis' attribute valueC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather.cconnxruntime::Gather::ComputeTindC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gatherbase.hGather Tind type not supported in this build.indices element out of data bounds, idx= must be within the inclusive range [C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBaseinfo.GetAttrs("axes", axes_).IsOK()Missing/Invalid 'axes' attribute valueC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\unsqueeze.cconnxruntime::UnsqueezeBase::PrepareComputeX != nullptraxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a scalar or a 1-D tensor.axes_tensor != nullptrAxes input is nullonnxruntime::Unsqueeze::Computenullptr != p.output_tensor'axes' has a duplicate axis'axes' has an out of range axisT3T2delimitermaxsplitStringSplitinvalid string_view positionRegexFullMatchpatternInvalid regex pattern: re_.ok()onnxruntime::RegexFullMatch::RegexFullMatchC:\a\_work\1\s\onnxruntime\core\providers\cpu\text\regex_full_match.cconnxruntime::OpNodeProtoHelper::GetAttrC:\a\_work\1\s\include\onnxruntime\core/framework/op_node_proto_helper.hStringConcatdetect_positiveFailed to obtain detect_positiveonnxruntime::IsInf::IsInfC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\isinf.ccdetect_negativeFailed to obtain detect_negativeNull input ptrbad allocationAffineGridAffineGrid : Input theta tensor dimension is not 3AffineGrid : Invalidate size - length of size should be 4 or 5.input_dims.NumDimensions() == 4 || input_dims.NumDimensions() == 5Only 4-D or 5-D tensor is supportedGrid batch size does not match input batch size , expect C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\grid_sample.ccgrid_dims[grid_dims.NumDimensions() - 1] == data_dimsLast dimension of grid: onnxruntime::GridSample::Compute" not supported, expect linear, nearest or cubiccubicOnly support GridSample Cubic mode in 4-D cases.grid_dims[0] == NOnly support GirdSample in 4-D or 5-D cases.mode_ != Cubicgrid dimensions must be for input dimension of onnxruntime::GridSample::Computestatic_cast(grid_dims.NumDimensions()) == data_dims + 2" not supported, expect bilinear, nearest or bicubicbicubicpadding_modezerosonnxruntime::GridSample::GridSamplemode "bilinearC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/grid_sample.honnxruntime::GridSample::GridSampleborderreflectionpadding_mode "" not supported, expect zeros, border or reflectiononesidedDivide by zeroinverseSafeIntExceptionHandler::SafeIntOnDivZeroonnxruntime::DFT::ComputeUnsupported input data type of 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.onnxruntime::STFT::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\dft.cconnxruntime::discrete_fourier_transformUnsupported 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.dft_length must be greater than zero.DFTdft_length must be a scalar value.STFTC:\a\_work\1\s\onnxruntime\core/providers/cpu/signal/utils.honnxruntime::signal::get_scalar_value_from_tensorsignal_components == 1 || signal_components == 2signal shape must end in 1 (real) or 2 (real, imaginary).tensor->Shape().Size() == 1ratio input should have a single value.window_size < signal_sizeIf both frame_length and window are set, then the size of the window must be equal to the frame_length.onnxruntime::short_time_fourier_transformEnsure that the dft size is smaller than the signal.frame_length == window_lengthonnxruntime::dft_bluestein_z_chirponnxruntime::bit_reverseUnsupported bit size.@ `P0pH(hX8xD$dT4t L,l\<|B"bR2r J*jZ:zF&fV6vN.n^>~A!aQ1q I)iY9yE%eU5u M-m]=}C#cS3s K+k[;{G'gW7wO/o_?onnxruntime::ValidateFastReduceKRfast_shape.size() == 2fast_shape[0] == output.Shape().Size()Output size mismatch.must be overloaded.projected_index.size() > 0Only works on matrices with two dimensions.onnxruntime::ValidateMustBeOverloadedfast_shape[0] * fast_shape[2] == output.Shape().Size()onnxruntime::ValidateFastReduceKRKReduction on all axes, output size should be 1.onnxruntime::ValidateFastReduceRKRfast_shape[1] == output.Shape().Size()onnxruntime::ValidateFastReduceRKfast_shape.size() == 3Only works on matrices with three dimensions.onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmptylast_loop_red_size > 0last_loop_size > 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\reduction\reduction_ops.ccselect_last_indexC:\a\_work\1\s\onnxruntime\core/providers/cpu/reduction/reduction_kernel_base.honnxruntime::ReduceKernelBase<1>::ReduceKernelBaseinfo.GetAttr("keepdims", &keepdims).IsOK()Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::CommonFastReduceCopyonnxruntime::ValidateKeepDimsonnxruntime::ValidateNoTransposeReducecount == 1axes_tensor->Shape().NumDimensions() == 1An axes tensor must be a vector tensor.axes.empty()Axes input and attribute should not both be present for reduction.onnxruntime::check_and_reduce_empty_set_inputonnxruntime::ReduceKernelBase<0>::ReduceKernelBaseonnxruntime::`anonymous-namespace'::ConstantOfShape::ComputeMust have a single dimensionC:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::ConstantOfShapeBaset_proto_p->dims_size() == 1Unsupported output datatype with size: C:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\constant_of_shape.cconnxruntime::ConstantOfShapeBase >::PrepareComputeMust have a valid input shape.onnxruntime::ConstantOfShapeBase >::SetValueUnsupported value attribute datatype with size: onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProtoutils::HasDataType(t_proto)Tensor proto with external data for value attribute is not supported.ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())t_proto_p->dims()[0] == 1Must have a single dimension of 1Unsupported value attribute datatype: !utils::HasExternalData(t_proto)onnxruntime::scan::detail::OutputIterator::GetOutputC:\a\_work\1\s\onnxruntime\core/providers/cpu/controlflow/scan_utils.hAttempt to retrieve final output before it was set.final_output_mlvalue_onnxruntime::Scan<9>::InitC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_9.ccbodyinfo.GetAttr("body", &proto).IsOK()Number of entries in 'scan_input_axes' was gsl::narrow_cast(input_axes_.size()) == num_scan_inputs_scan_input_axes but expected scan_input_directionsscan_output_directionsnum_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()onnxruntime::ScanImpl::ValidateInputonnxruntime::ScanImpl::SetupInputs of Invalid value in scan_input_axes for input Scan inputs have inconsistent sequence lengths. Previous value was . Input tensor rank was ' dimension but input 'onnxruntime::ScanImpl::Execute. Output tensor rank was Output OrtValue has not been created for loop state variable output output_mlvalueonnxruntime::ScanImpl::AllocateOutputTensorsonnxruntime::ScanImpl::CreateLoopStateVariables outputs but Scan expects Subgraph in 'body' produces CreateFeedsFetchesManager must be called prior to execution of graph.feeds_fetches_manager_ && info_info_ == nullptronnxruntime::Scan<9>::SetupSubgraphExecutionInfogsl::narrow_cast(output_axes_.size()) == num_scan_outputsSetupSubgraphExecutionInfo should only be called once for each subgraph.scan_output_axesNumber of entries in 'scan_output_axes' was Invalid scan input: has length of dimensions or more but input had shape of Expected session_stateonnxruntime::ScanImpl::Initializeonnxruntime::Scan<9>::ComputeSubgraph SessionState was not found for 'body' attribute.Scanonnxruntime::ScanImpl::TransposeOutputVInvalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null.mode attribute is onnxruntime::UpsampleBase::StringToUpsampleMode(default) or . It can only be 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 or exclude_outside and anti-aliasing is set to tf_half_pixel_for_nntf_crop_and_resizeonnxruntime::UpsampleBase::StringToKeepAspectRatioPolicypytorch_half_pixel] is not supported!keep_aspect_ratio of [not_largernot_smallerwhen anti-aliasing is set, Resize only supports mode `LINEAR` and `CUBIC`.(UpsampleMode::LINEAR == mode_ || UpsampleMode::CUBIC == mode_)C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/upsamplebase.hantialiasinfo.GetAttr("mode", &mode).IsOK()onnxruntime::UpsampleBase::UpsampleBaseround_prefer_floorcubic_coeff_ayet this opset `tf_half_pixel_for_nn` is deprecated since opset 13, extrapolation_value model uses the deprecated attributestretchkeep_aspect_ratio_policyall values in axes should be less than rank of the dataonnxruntime::UpsampleBase::StringToNearestModeScale value should be greater than or equal to 1.ceilnearest_mode:[onnxruntime::UpsampleBase::StringToCoordinateTransformationModeround_prefer_ceilhalf_pixel_symmetriccoordinate_transform_mode:[scales size should be greater than 0.onnxruntime::UpsampleBase::ParseScalesData'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 Resize operatorUpsample operatoronnxruntime::UpsampleBase::ScalesValidationScale value should be greater than 0.requested_shape[i] >= -1At most one dimension can be -1.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.hA dimension cannot be less than -1, got input_shape_size != -1onnxruntime::ReshapeHelper::ReshapeHelperThe input tensor must not have any dynamic (-1) dimensions. Input shape:input_shape_size == sizeThe input tensor cannot be reshaped to the requested shape. Input shape:size != 0 && (input_shape_size % size) == 0i < input_shape.NumDimensions(), requested shape:unknown_dim == -1The dimension with value zero exceeds the dimension size of the input tensor.onnxruntime::Reshape::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reshape.hA shape tensor must be a vector tensor.shapeTensor->Shape().NumDimensions() == 1Attribute shape is not set.onnxruntime::Reshape_1::Reshape_1scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimx_zero_point must be null or 1D tensor with size C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear.ccscale must be 1D tensor with size zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)onnxruntime::PrepareForQDQx_zero_point must be null or a scalar or 1D tensor or size 1.zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)onnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::ComputeUnsupported input type.onnxruntime::QuantizeLinear::Computeonnxruntime::QuantizeLinear::Computesaturateonnxruntime::DequantizeLinear::ComputeDequantizeLinear with type int32 or float8 should have no zero point or all zero points should be 0onnxruntime::DequantizeLinear::ComputeDequantizeLinear only outputs FLOAT16, FLOAT or BFLOAT16.DequantizeLinear into BFLOAT16 is not implemented yet.onnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computeonnxruntime::DequantizeLinear::Computezero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](T zp) { return zp == T{0}; })onnxruntime::DequantizeLinear::Computeonnxruntime::PadBase::PadBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\padbase.hwrapInvalid 'mode' attribute valuereflectedgeInvalid 'pads' attribute valueC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/utils.hdims_size == starts.size() && dims_size == extents_.size() && dims_size >= steps.size()dims.size() == extents.size() && dims.size() >= steps.size()onnxruntime::SliceSkips::SliceSkipsonnxruntime::PadValueFromFloatC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\pad.ccCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of Cannot use 'edge' mode to pad dimension with a value of 0. Input shape:onnxruntime::SliceIteratorBase::Initpads_size == 2 * data_rankValue tensor should be a 1D tensor of size 1 with the same type as that of the input tensoraxes_tensor_dims.size() == 1Pads tensor size should be equal to twice the input dimension count onnxruntime::Pad::ComputeAxes tensor should be a 1D tensor Pads tensor should be a 1D tensor of shape [2 * num_axes] or a 2D tensor of shape [1, 2 * num_axes]pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1onnxruntime::PadInputWithDimValueOfZero'pads' has wrong number of valuesdata_rank * 2 == pads.size()data_rank > 0onnxruntime::PadImplInput tensor has no dimensions Got:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has C:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\loop.cconnxruntime::Loop::Info::Infostatic_cast(num_subgraph_inputs) == subgraph_inputs.size()'Loop' input 'cond' should be a scalar tensor. Got shape of 'Loop' input 'M' should be a scalar tensor. Got shape of feeds_fetches_manager_onnxruntime::Loop::Computeonnxruntime::Loop::SetupSubgraphExecutionInfoonnxruntime::Loop::InitInconsistent shape in loop output for output. Expected:Graph in 'body' attribute of Loop should have inputs. Found:LoopBIonnxruntime::LoopImpl::Execute::::operator ()onnxruntime::LoopImpl::ConcatenateLoopOutputonnxruntime::LoopImpl::SaveOutputsAndUpdateFeedslast_outputs[j + 1].IsTensor()All scan outputs MUST be tensorsLoop subgraph input 1 has unknown shape: onnxruntime::LoopImpl::InitializeLoop subgraph input 0 has unknown shape: Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.onnxruntime::LoopImpl::Execute outputs which doesn't match the subgraph's outputs.else_branchonnxruntime::If::Initinfo.GetAttr("then_branch", &proto).IsOK()then_branchC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\if.cconnxruntime::If::Info::Infonum_subgraph_outputs == static_cast(num_outputs)'If' node has Subgraph SessionState was not found for '' attribute.If nodes condition input must have exactly one elementonnxruntime::If::Computethen_feeds_fetches_manager_ && else_feeds_fetches_manager_onnxruntime::If::SetupSubgraphExecutionInfoinfo == nullptrinfo.GetAttr("else_branch", &proto).IsOK()onnxruntime::IfImpl::ExecuteFailed to create output tensor for If output Only tensors, tensor sequence, optional tensor, and optional tensor sequence types are supportedFailed to create output tensor for onnxruntime::IfImpl::Initializeonnxruntime::IdentityOp<1>::ComputeUnable to get an allocatorC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/identity_op.honnxruntime::IdentityOp<0>::Computeonnxruntime::BroadcastIterator::AppendC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/element_wise_ops.honnxruntime::BroadcastIterator::Initaxis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by ) for tensor of length:onnxruntime::InputBroadcaster::AdvanceByoffset % span_size_ == 0InputBroadcaster can only start at span boundary!onnxruntime::Broadcaster::Broadcasterlargest <= 1Can broadcast 0 by 0 or 1. is invalid.onnxruntime::TensorAllocator::TensorAllocatorstart_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) are not at boundary of span with size:onnxruntime::OutputBroadcaster::OutputBroadcasterstart_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [BitwiseAndC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\element_wise_ops.cconnxruntime::Min_6::ComputeinputCount >= 1Must have 1 or more inputsUnsupported X type: BitwiseXorBitwiseOrBitwiseNotonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/element_wise_ranged_transform.honnxruntime::ElementWiseKernel >::Computeinput_size < std::numeric_limits::max()onnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ExpandBroadcastLooper!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.data_1.Shape() == shapeonnxruntime::Mean_6::Computeonnxruntime::Max_6::Computedata_n.Shape() == shapeAll inputs must have the same shapefmod attribute must be true for floating point typesonnxruntime::Mod::Mod(fmod == 0) || (fmod == 1)fmod must have value either 0 or 1fmodonnxruntime::UntypedExpandshape_data_tensor.Shape().GetDims().size() == 1Tensor with shape information must be 1 dimensional.onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::BitShift::Compute::::operator ()onnxruntime::BitShift::Compute::::operator ()cur_out == end_outonnxruntime::BitShift::Compute::::operator ()cur1 == end1onnxruntime::Sum_6::Computeonnxruntime::Sum_6::Computeinput_count >= 1onnxruntime::BroadcastLooperhelper.HaveTwoTensorInputs()BroadcastLooper requires two tensors as input.onnxruntime::Expand_8,class std::allocator > >::Compute::::operator ()Invalid usage. Input 1 is a shape with no data.Unsupported Y type: onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::mod_internal::CallModImpl::operator ()onnxruntime::BitShift::BitShiftdirectiononnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::BitShift::BitShiftonnxruntime::BitShift::BitShiftInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.RIGHTLEFT((`(`(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfOinvalid stoll argumentstoll argument out of rangeinvalid stoull argumentstoull argument out of rangeinvalid stod argumentstod argument out of rangeonnxruntime::`anonymous-namespace'::GetIntermediateMLFloat16ToFloatTensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\cast_op.ccAttribute to is not set.onnxruntime::`anonymous-namespace'::Cast::CastAttribute saturate is only used for cast to float 8 types.NaN-INFINF%.8gsnprintf() failed with return value: snprintf_result > 0onnxruntime::`anonymous-namespace'::CastToStringFailed to write value with snprintf().snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1pinput_shape.Size() > 0 || input_shape[0] == 0onnxruntime::PoolAttributes::SetOutputSizeinput_dims.size() >= 2onnxruntime::PoolAttributes::InferOutputSizeUnsupported AutoPad Type.onnxruntime::PoolAttributes::ComputeSizePadDilationsinfo.GetAttr("p", &p_).IsOK()onnxruntime::PoolProcessContext::initinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0Pad should be smaller than kernel.pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()Dilations dimensions should match kernel shapedilations.size() == kernel_shape.size()Invalid input shape. Only N can be zero. Got:No kernel shape is set.info.GetAttrs("kernel_shape", kernel_shape).IsOK()onnxruntime::PoolAttributes::PoolAttributesC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_attributes.hinfo.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()Unknown AutoPadType Stringonnxruntime::StringToAutoPadType`InlinedVector::at(size_type)` failed bounds checkC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool_base.hInput dimension cannot be less than 3.onnxruntime::PoolBase::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\pool.ccUnsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.onnxruntime::MaxPoolV8::ComputeImplonnxruntime::AveragePoolV19::ComputeUnsupported kernel dimension : onnxruntime::Pool::ComputeUnsupported pooling size : onnxruntime::LpPoolV18::Computeonnxruntime::LpPoolV18::LpPoolV18C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/pool.hSizeinput count mismatchC:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.honnxruntime::Optional::Optionaltypeattr->has_tp()Optional op must have a TypeProto in the 'type' attribute if the attribute is presentonnxruntime::Optional::ComputeOnly Optional type OrtValues containing Tensors and Sequence Tensors are acceptableThe TypeProto attribute in the Optional op can only be of type(tensor) or (seq(tensor))onnxruntime::OptionalGetElement::ComputeTrying to use OptionalGetElement on an optional type OrtValue which contains no dataOptionalOOptionalGetElementOptionalHasElementC:\a\_work\1\s\onnxruntime\core\providers\cpu\optional\optional_ops.cconnxruntime::PropagateInputOrtValueToFirstOutputstd::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })Invalid value in 'split' attribute. All values must be > 0C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBaseIf 'num_outputs' is specified, the 'split' input should not be provided.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\split.cconnxruntime::SplitImpl::ComputeInput cannot be split evenly on selected axis. Input shape= Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was Cannot split using values in 'split' attribute. Axis= Input shape=split_tensor->Shape().NumDimensions() == 1The split tensor must be a vector tensor.. Size of dimension being split is NumOutputs=Invalid num_outputs value of onnxruntime::DispatchStridedCopydst.DataType() == src.DataType()C:\a\_work\1\s\onnxruntime\core/framework/copy.hlast >= firstsrc and dst types must matchonnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopy::::operator ()counter.current_offset == lastonnxruntime::StridedCopy::::operator ()src and dst must have same shape and not be rank 0.onnxruntime::StridedCopy::::operator ()onnxruntime::StridedCopydst_strides.size() == src_strides.size() && src_strides.size() == copy_shape.size() && !copy_shape.empty()total_num_elements_to_copy >= 0copy shape must have non-negative sizeScatterElementsScatterCPU 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 ScatterElements opset 16 when reduction is 'add'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: string data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 16 when reduction is 'mul'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: BFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'min'.CPU execution provider: MLFloat16 data type is not supported with ScatterElements opset 18 when reduction is 'max'.CPU execution provider: string data type is not supported with ScatterElements opset 18 when reduction is 'max'.onnxruntime::ScatterDataScatterElements op: input tensor must have at least one dimensionmuladd at pos= is greater than input dim=Indices type is not supported.Indices dim=C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter.cconnxruntime::Scatter,class std::allocator > > >::ScatternonereductionIndices and updates must have the same rankdata type is different from updates typeIndices vs updates dimensions differs at position= vs Indices must have the same rank as Input. Indices rank=. Input rank=CPU execution provider: string 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 'add'.CPU execution provider: BFloat16 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: string data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'min'.CPU execution provider: BFloat16 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'.input shape: , indices shape: last dimension of indices must not be larger than rank of input tensorinput tensor and indices tensor must has rank larger than 0. updates shape: , data shape: CPU execution provider: MLFloat16 data type is not supported with ScatterND opset 16 when reduction is 'add'.updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. ScatterNDCPU execution provider: string data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: bool data type is not supported with ScatterND opset 18 when reduction is 'max'.CPU execution provider: BFloat16 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'.onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget<__int64>::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()invalid indice found, indice = onnxruntime::PrepareForComputeonnxruntime::ScatterNDDispatchTarget::operator ()C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\scatter_nd.cconnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget::operator ()onnxruntime::ScatterNDDispatchTarget,class std::allocator > >::operator ()Col2Impads_.empty()pads_.size() == 2 * image_dim_numbersize of 'pads' attribute, if provided, should equal to twice the number of image dimmensions.strides_.size() == image_dim_numbersize of 'strides' attribute, if provided, should equal to the number of image dimmensions.onnxruntime::Col2Im::Col2Imstrides_.empty()dilations_.empty()C:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/col2im.hdilations_.size() == image_dim_numbersize of 'dilations' attribute, if provided, should equal to the number of image dimmensions.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\col2im.cconnxruntime::Col2Im::ComputeUHannWindowMelWeightMatrixHammingWindowBlackmanWindowoutput_datatypeperiodicupper_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.highest_index >= 0 && highest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()C:\a\_work\1\s\onnxruntime\core\providers\cpu\signal\window_functions.cconnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix<__int64>::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()lower_edge_hertz produces a mel triangle filter bank that is out of range given the dft_length and the sample_rate.lowest_index >= 0 && lowest_index < num_spectrogram_binsonnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::CreateMelWeightMatrix::operator ()onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeAttribute name and type don't match for 'onnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::Compute'is defined.No attribute with name:'C:\a\_work\1\s\onnxruntime\core\providers\cpu\activation\activations.ccunknown kernel typeonnxruntime::functors::ElementWiseRangedTransform::Createonnxruntime::functors::ScaledTanh::InitC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/activations.honnxruntime::ElementWiseKernel >::Computeonnxruntime::functors::HardSigmoid::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelC:\a\_work\1\s\onnxruntime\core/providers/cpu/activation/activations.hgammaonnxruntime::functors::Selu::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::functors::ParametricSoftplus::Initonnxruntime::ElementWiseKernel >::ElementWiseKernelavgRoiAlignNull input X ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Null rois_ptrNull batch_indices_ptrwill be different.and will be fixed in the next ORT 1.13 release. Thus the results of RoiAlign sampling_ratio_ >= 0spatial_scaleThe existing summation for max mode and sampling ratios besides 1 is incorrect onnxruntime::RoiAlignBase::RoiAlignBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\object_detection\roialign.h specified. It should be either avg or maxInvalid mode of value sampling_ratioSampling ratio should be >=0, but it was output_heightoutput_widthSecond dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match!is_train_ || ((!saved_mean && !saved_inv_std) || (saved_mean && saved_inv_std))onnxruntime::BatchNorm::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/batch_norm.hInvalid number of outputs for BN trainingInvalid input var: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != onnxruntime::BatchNorm::ComputeInvalid input B: NumDimensions() != Invalid input B: 0th dimension != dimension != Invalid input scale: Invalid input mean: 0th dimension != Invalid input mean: Invalid input B: Invalid input mean: NumDimensions() != Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != onnxruntime::BatchNorm::BatchNormonnxruntime::BatchNorm::BatchNormTraining mode only supports spatial BNis_spatial_training_modemomentumC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn.ccbidirectionalreverseonnxruntime::RNN::ComputeRNNforwardallowed_directions.find(direction_) != allowed_directions.end()C:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNNinfo.GetAttr("direction", &direction_).IsOK()info.GetAttrs("activations", activations_).IsOK()activationsactivation_betaactivation_alphalayoutclipAffineRNN op: Invalid activation attribute - activations_.size() == static_cast(num_directions)info.GetAttr("hidden_size", &hidden_size_).IsOK()hidden_sizelayout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.allowed_activations.find(activations_[direction]) != allowed_activations.end()onnxruntime::IAllocator::ValidateAllocationp != nullptr || size == 0Memory allocation failed. Size=C:\a\_work\1\s\include\onnxruntime\core/framework/allocator.honnxruntime::rnn::detail::MakeDirectionInvalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.C:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.honnxruntime::LSTMBase::LSTMBaseinfo.GetAttr("direction", &direction).IsOK()C:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/rnn_helpers.honnxruntime::DeepCpuLstmOp::ComputeInvalid data type for LSTM operator of LSTM operator does not support double yetC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_lstm.ccsigmoidinput_forgetclip_ > 0.finfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0onnxruntime::DeepCpuLstmOp::PrePackLSTMactivation_func_names.size() == static_cast(num_directions_) * 3tanhonnxruntime::IAllocator::ValidateAllocatorallocator != nullptronnxruntime::IAllocator::ValidatedCalcMemSizeForArrayInvalid size requested for allocation: * onnxruntime::DeepCpuGruOp::ComputeInvalid data type for GRU operator of GRU operator does not support double yetGRUC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\deep_cpu_gru.cconnxruntime::DeepCpuGruOp::DeepCpuGruOpactivation_func_names.size() == static_cast(num_directions_) * 2info.GetAttr("linear_before_reset", &int64_value).IsOK()linear_before_resetC:\a\_work\1\s\onnxruntime\core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::rnn::detail::ComputeGemmlda >= K && ldb >= K && ldc >= Nonnxruntime::DeepCpuGruOp::ComputeImplonnxruntime::rnn::detail::GemmWeights::GetUnpackedSpan!is_prepacked_Can not get unpacked span from prepacked weightsC + (M * ldc - (ldc - N)) <= C_endB + (N * ldb - (ldb - K)) <= B_endA + (M * lda - (lda - K)) <= A_endonnxruntime::rnn::detail::SafeRawPointeroffset + size <= size_t(span.size())onnxruntime::rnn::detail::SafeRawConstPointercur + size <= endInput tensor should have a rank of at least 2C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.cconnxruntime::Trilu::ComputeIsScalarOr1ElementVector(k)info.GetAttr("upper", &temp).IsOK()upperk should be a 1-D or 0-D tensor.TriluC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\trilu.honnxruntime::Trilu::TriluAxis tensor should be 0D or 1DAxis tensor must be provided to the CumSum opCumSumAxis tensor should be of type `int32_t` or `int64_t`extents.size()=onnxruntime::WritableSliceIterator<__int64>::Initdims.size() == starts.size()dims.size()=onnxruntime::WritableSliceIterator::Initdims.size() == steps.size()steps.size()=dims.size() == extents_.size()starts.size()=onnxruntime::CumSum<__int64>::Computeonnxruntime::CumSum::Computeonnxruntime::CumSum::ComputeexclusiveC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\cumsum.cconnxruntime::CumSum::ComputeCannot apply CumSum operator on a scalaronnxruntime::WritableSliceIterator::Initonnxruntime::WritableSliceIterator::Initonnxruntime::Softmax::ComputeImplOpset13onnxruntime::Softmax::ComputeImplOpset13C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\softmax.ccC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\hardmax.cc, D=. N=Hardmax inputs N, D and N * D must be < onnxruntime::Hardmax::Computebatch_dimsGatherNDindicesindices tensor must has rank larger than 0invalid index found, index = indices tensor data type not supportedGatherNDBase PrepareForCompute: Input count mismatchinput_tensor != nullptr && indices_tensor != nullptronnxruntime::GatherND::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_nd.cconnxruntime::NonZero::Computefailed to get first output!onnxruntime::NonZero::Computeonnxruntime::NonZero::Computeonnxruntime::NonZero<__int64>::ComputeX input is required!onnxruntime::NonZero::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\nonzero_op.ccC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/space_depth_ops.hSpaceDepth ops require a 4-D input. Provided rank: SpaceToDepth requires input height to be a multiple of block_sizeSpaceToDepth requires input width to be a multiple of block_sizeblocksizeAttribute blocksize is not set.info.GetAttr("blocksize", &blocksize_).IsOK()onnxruntime::SpaceDepthBase::SpaceDepthBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\space_depth_ops.ccUnsupported input type in SpaceToDepth op: onnxruntime::DepthToSpace::ComputeUnsupported input type in DepthToSpace op: onnxruntime::SpaceToDepth::Computeonnxruntime::DepthToSpace::DepthToSpaceDepthToSpace requires input depth to be a multiple of (block_size * blok_size)CRDDCRDepthToSpace op: only 'DCR' and 'CRD' modes are supportedC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.hGatherElementsGatherElements op: Cannot operate on scalar inputGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'onnxruntime::GatherElements::GatherElementsGatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the outputonnxruntime::GetIndexGatherElements op: Unsupported tensor type, size:onnxruntime::core_implC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\gather_elements.ccGatherElements op: Out of range value in index tensorC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\tile.cc'repeat' input tensor must have the same length as the 'input' tensor'repeat' input tensor must be 1 dimensionalonnxruntime::Tile::ComputeTile doesn't have an implementation yet for the type: Input count of Tile OP mismatch, the first one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensionalInput count of Tile OP mismatch, the second one is emptyC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/squeeze.honnxruntime::SqueezeBase::ComputeOutputShapeonnxruntime::Squeeze::Compute must be 1 instead of . shape=input_shape[i] == 1Dimension of input 'axes' has an axis outside of the tensor dimension count'step' value cannot be 0'axes' has duplicates!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributeshas_starts && has_ends && attr_starts_.size() == attr_ends_.size()Missing or invalid starts and ends attributeC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBaseStarts and ends shape mismatchEnds must be a 1-D arrayStarts and steps shape mismatchStarts and axes shape mismatchC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\slice.cconnxruntime::SliceBase::PrepareForComputeonnxruntime::SliceBase::FillVectorsFromInputStarts must be a 1-D arrayonnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStepUnexpected element size of Cannot slice scalarsData type for starts and ends inputs' is not supported in this build. Got onnxruntime::SliceBase::Computeonnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()onnxruntime::SliceImpl::::operator ()output == output_endC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concat.cconnxruntime::ConcatBase::PrepareForComputeCannot concatenate scalarsinput != nullptrnew_axisMust have valid 'axis' attributeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\concatbase.honnxruntime::ConcatBase::ConcatBaseData type mismatchNon concat axis dimensions must match: Axis onnxruntime::ConcatBase::ComputeImplRanks of input data are different, cannot concatenate them. expected rank: got: has mismatched dimensions of input_rank == reference_rankonnxruntime::Dropout::Computeonnxruntime::Dropout::Computeonnxruntime::Dropout::Compute!mask || mask->Shape() == X_shapeonnxruntime::Dropout::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/dropout_op.hX and mask should have the same shape0.0f <= ratio_value && ratio_value < 1.0fratio must be in the range [0, 1)onnxruntime::`anonymous-namespace'::GetRatioOrDefaultratio_tensor->Shape().Size() == 1seedMeanVarianceNormalizationNo valid axes are specified. This is not handled now.onnxruntime::MeanVarianceNormalization::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\mean_variance_normalization.ccnormalize_varianceX and Y must both have M * N elements.onnxruntime::`anonymous-namespace'::ComputeMeanVarianceNormalization2Dacross_channelssizeinfo.GetAttr("size", &size).IsOK()C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\lrn.ccX->Shape().NumDimensions() == 4onnxruntime::LRN::Computebiasinfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0finfo.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0fsize_ > 0size_ % 2 == 1onnxruntime::LRN::LRNC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lrn.hgsl::narrow_cast(X_shape.NumDimensions()) >= axisonnxruntime::Flatten::ComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/flatten.hThe rank of input tensor must be >= axisonnxruntime::Flatten::Flattenleft.NumDimensions() == 2 || left.NumDimensions() == 1onnxruntime::GemmHelper::GemmHelperinfo.GetAttr("transB", &temp).IsOK()onnxruntime::GemmBase::GemmBaseC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_base.hinfo.GetAttr("transA", &temp).IsOK()M_ >= 0 && K_ > 0 && N_ >= 0GEMM: Dimension mismatch, W: Gemm: Invalid bias shape for broadcast N: K: right[i] >= 0left[i] >= 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\gemm_helper.hright.NumDimensions() == 2c_shape != nullptronnxruntime::GemmBroadcastBiasc_shape is required if c_data is providedonnxruntime::MatMul::Computenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsnum_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falseleft operand cannot broadcast on dim right operand cannot broadcast on dim Two inputs should have same rank and rank >= 3 if transBatchA or transBatchB is trueleft_num_dims > 2 && left_num_dims == right_num_dimsC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/matmul_helper.hMatMul dimension mismatchleft_num_dims and right_num_dims must be >= 1onnxruntime::MatMulComputeHelper::Computeonnxruntime::MatMul::Computeonnxruntime::MatMul<__int64>::Computeonnxruntime::MatMul::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\matmul.cconnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl::operator ()onnxruntime::Clip::ComputeImpl<__int64>::operator ()max->Shape().IsScalar()max should be a scalar.C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\clip.cconnxruntime::Clip::ComputeImpl::operator ()min->Shape().IsScalar()min should be a scalar.C:\a\_work\1\s\onnxruntime\core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Basemin_ <= max_invalid expand shapeinfo.GetAttr("equation", &equation_).IsOK()Missing 'equation' attributeequation->Einsum op: An implementation for the input type is not supported yetC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.cconnxruntime::Einsum::DeviceComputeThere was a problem acquiring temporary memory allocator in Einsum opEinsum op: There must be atleast one inputEinsumC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum.honnxruntime::Einsum::Einsumonnxruntime::OneHotOp<__int64,class std::basic_string,class std::allocator >,__int64>::Computeonnxruntime::OneHotOp::ComputeDepth is negative.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\onehot.cconnxruntime::OneHotOp<__int64,__int64,__int64>::ComputeInvalid argument for values; either it's rank is more than 1 or it has more than 2 elementsInvalid argument for depth; it's not a scalar.OneHotonnxruntime::OneHotOp<__int64,float,int>::Computeonnxruntime::OneHotOp<__int64,float,float>::Computeonnxruntime::OneHotOp<__int64,int,float>::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp::Computeonnxruntime::OneHotOp<__int64,float,__int64>::Computeonnxruntime::OneHotOp,class std::allocator >,__int64>::Computelargestonnxruntime::TopkOpset11ConstructorCommononnxruntime::TopkOpset10ConstructorCommonop_kernel_info.GetAttr("axis", &axis_temp).IsOK()k_temp > 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\top_k.cconnxruntime::TopkOpset9ConstructorCommonop_kernel_info.GetAttr("k", &k_temp).IsOK()kinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueinput count mismatch, expected 1 input - the tensor to be processedk argument [] should not be greater than specified axis dim value [op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()sortedop_kernel_info.GetAttr("largest", &largest_temp).IsOK()output count mismatch, expected 2 outputs to be present for TopK operatorvalue of k must not be negativek tensor should be a 1D tensor of size 1Data type is not supported in this build.UniqueUnsupported tensor type of delta in Range operator can not be zero!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:SSequenceLengthSequenceAtonnxruntime::GetSeqIdxC:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\sequence_ops.cc) specified for sequence of size (Invalid sequence index (onnxruntime::SequenceAt::ComputeSequenceEmptyUnsupported 'dtype' value: onnxruntime::SequenceEmpty::ComputeSequenceInsertonnxruntime::CloneTensor), input tensor data type (Data type of the input tensor MUST be same as that of the input sequence. Sequence data type (SequenceEraseSequenceConstructnum_inputs >= 1onnxruntime::SequenceConstruct::ComputeViolation of the requirment that all input tensors must have the same data type.SplitToSequence) != split_dim_size (split_size_sum (Invalid data type for split tensor onnxruntime::GetScalarSplitInputonnxruntime::GetSplitSizesInputSplit should be > 0split_scalar > 0onnxruntime::SplitToSequence::ComputeImplInvalid value in 'split' input. All values must be >= 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })ConcatFromSequenceGot nullptr for sequence input.onnxruntime::ConcatFromSequence::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\sequence\concat_from_sequence.ccA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesauto_pad == AutoPadType::NOTSETonnxruntime::ConvAttributes::ConvAttributesC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_attributes.h W: kernel_shape: kernel_shape num_dims is not compatible with W num_dims. channels_last: kernel_shape is not compatible with W shape. X: X num_dims does not match W num_dims. group: Invalid input shape: output_paddingoutput_shapegroup count is <= 0 num_input_channels: filter_number: filter number not equal to input channel number.Input channels is not divisible by group.onnxruntime::ConvTransposeAttributes::PrepareForComputeC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/conv_transpose_attributes.hdim_size > 0onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape*out_size >= 0onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShapeonnxruntime::ConvTranspose::DoConvTransposeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv_transpose.cconnxruntime::ComputePadAndOutputShape kernel channels: C: Input channels C is not equal to kernel channels * group. M: Output channels M is not divisible by group.Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: onnxruntime::ConvAttributes::InferPadsAndOutputShapeonnxruntime::Conv::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\conv.ccoutput and sum shape must matchDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]kernel_shape_[dim] > 0strides_.size() == kernel_shape_.size()C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpoolonnxruntime::MaxUnpool::ComputeIndex tensor shape should be same as that of the input data tensor to unpool.C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\Unpool.cc inferred output shape:Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape:DetC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\det.cconnxruntime::Det::ComputeMatrix dimensions are not equal. Square matrix is expectedCompressonnxruntime::Compress::Computeaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\compress.cconnxruntime::DynamicQuantizeLinear::Computex_ptr != nullptrC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\dynamicquantizelinear.ccbatch_axisonnxruntime::ReverseSequenceOp::ReverseSequenceOpinfo.GetAttr("batch_axis", &batch_axis).IsOK()time_axisC:\a\_work\1\s\onnxruntime\core/providers/cpu/tensor/reverse_sequence.h. Must be 0 or 1info.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are . batch_size=ReverseSequenceUnknown tensor type of sequence_lens shape must be {batch_size}. Got:C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\reverse_sequence.cconnxruntime::ReverseSequenceOp::Compute. Value must be in range [0,Invalid sequence length: T4onnxruntime::QLinearConv::Computeonnxruntime::QLinearConv::UseSharedPrePackedBuffersonnxruntime::QLinearConv::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\qlinearconv.ccprepacked_buffers[0].get() == nullptrIsScalarOr1ElementVector(X_scale)onnxruntime::QLinearConv::ComputeOutputScaleonnxruntime::QLinearConv::UseSharedPrePackedBuffersQLinearConv : input scale must be a scalar or 1D tensor of size 1QLinearConv : filter scale shape invalidIsValidQuantParam(W_scale, M)QLinearConv : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)onnxruntime::QLinearConv::ComputeOffsetQLinearConv : result zero point must be a scalar or 1D tensor of size 1QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_zero_point)IsValidQuantParam(W_zero_point, M)QLinearConv : zero point of per-channel filter must be same. This happens by design if the quantization is symmetric.IsScalarOr1ElementVector(Y_zero_point)QLinearConv : filter zero point shape invalidonnxruntime::QLinearConv::ComputeOffsetW_zero_point_data[i] == W_zero_point_valueonnxruntime::QLinearConv::ComputeOutputScaleConvIntegeronnxruntime::ConvInteger::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\conv_integer.ccMust be a scalar or 1D tensor or size 1.IsScalarOr1ElementVector(X_Zero_Point)Non per-tensor quantization is not supported now.IsScalarOr1ElementVector(W_Zero_Point)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.onnxruntime::MatMulComputeHelper::Compute::::operator ()MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1C:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\matmul_integer.ccMatmulInteger : B zero point is not validIsScalarOr1ElementVector(a_zero_point)onnxruntime::MatMulInteger::ComputeIsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)onnxruntime::QLinearMatMul::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\quantization\quantize_linear_matmul.ccQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)Conversion ErrorConversion ErrorStringNormalizeronnxruntime::string_normalizer::Locale::LocaleC:\a\_work\1\s\onnxruntime\core\providers\cpu\text\string_normalizer.cc:Please, install necessary language-pack-XX and configure localesFailed to construct locale with name:attribute is_case_sensitive is not setonnxruntime::StringNormalizer::StringNormalizeren-USis_case_sensitiveLOWERUPPERcase_change_actionattribute case_change_action is not setlocalestopwordsNONEattribute case_change_action has invalid valueDuplicate stopwords not allowedp.secondEmpty stopwords not allowed!sw.empty()Single dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedStopword contains invalid utf8 charswstr != wconv_errorInput contains invalid utf8 charsResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.C:\a\_work\1\s\onnxruntime\core\providers\cpu\tensor\upsample.ccOnly one of scales or sizes must be provided as input.Invalid roi input index.onnxruntime::Upsample::ComputeResize: input tensor's rank does not match the output tensor's rank. Output dim value: Input dim value: onnxruntime::UpsampleBase::ParseSizesDataaxes should be less than output_dims.size()Input dim is zero but required output dim is non-zero. onnxruntime::UpsampleBase::ParseScalesDataAndAdjustOutputSizeDimension: Cannot scale 0 by any factor to generate a non-zero value. Resize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input/output value is nullptrUpsample: input/output value is nullptronnxruntime::Upsample::Computeonnxruntime::Upsample::ComputeEither scales or sizes MUST be provided as input.onnxruntime::Upsample::Computeonnxruntime::Upsample::BaseComputeResize: input tensor's dimension does not match the scales.Rank of input and output tensor should be same.4-D input with innermost scale (usually channel of NHWC) as 1.: 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.Upsample: unexpected modeonnxruntime::Upsample::BaseCompute: 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1.Resize: unexpected modeonnxruntime::Upsample::BaseComputeonnxruntime::Upsample::BaseComputeonnxruntime::UpsampleNearestmax_skip_count must be non-negative: max_skip_count is requiredmax_skip_countimpl_->max_gram_length_ >= impl_->min_gram_length_status.IsOK() && !impl_->ngram_counts_.empty()Non-empty ngram_counts is requiredngram_countsimpl_->max_skip_count_ >= 0max_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= status.IsOK() && !impl_->ngram_indexes_.empty()Non-empty ngram_indexes is requiredngram_indexessize_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() must be of equal sizeweightsstd::all_of(impl_->ngram_indexes_.begin(), impl_->ngram_indexes_.end(), [](int64_t i) { return i >= 0; })Negative ngram_indexes values are not allowedpool_stringsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: non-empty pool_int64s is required if pool_strings not providedpool_int64s!pool_strings.empty()pool_strings must not be empty if specifiedend_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -gramsstatus.IsOK() && !pool_int64s.empty()TfIdfVectorizerTFC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizermode is requiredmode: is unrecognized, acceptable values are TF,IDF,TFIDFTFIDFIDFRequired min_gram_length must be positive: min_gram_length is requiredmin_gram_lengthimpl_->weighting_criteria_ != kNonemin_gram_length >= max_gram_length required: >= max_gram_lengthimpl_->min_gram_length_ > 0onnxruntime::ngram_details::PopulateGramsp.first->second->id_ == 0Duplicate ngram detected, size: id: Input shape must have either [C] or [B,C] dimensions with B > 0.(items % ngram_size == 0)Number of items must compose whole op_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()lambdC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrinkop_kernel_info.GetAttr("bias", &bias_temp).IsOK()EyeLikeEyeLike : Input tensor dimension is not 2onnxruntime::Scan8Impl::CreateLoopStateVariablesp_mlvalueonnxruntime::Scan8Impl::AllocateOutputTensorsInvalid entries in sequence_lens. Max sequence length was C:\a\_work\1\s\onnxruntime\core/framework/ort_value_tensor_slicer.honnxruntime::OrtValueTensorSlicer::Iterator::operator *position_ >= 0 && position_ < sequence_length_onnxruntime::Scan8Impl::ExecuteScan<8> spec does not support transpose of output. This should never be called.onnxruntime::Scan<8>::SetupSubgraphExecutionInfodirectionsC:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_8.cconnxruntime::Scan<8>::Init but has batch size of onnxruntime::Scan8Impl::Initializeonnxruntime::Scan<8>::Computesequence_lens length of did not match batch size of onnxruntime::Scan8Impl::ValidateInputScan inputs have inconsistent batch size. Previous value was MaxRoiPoolroi_batch_id >= 0C:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\roi_pool.cconnxruntime::RoiPool::ComputeR->Shape()[1] == 5spatial_scale_ > 0info.GetAttr("spatial_scale", &spatial_scale_).IsOK()roi_batch_id < batch_sizeonnxruntime::RoiPool::RoiPoolpooled_shapepooled_width_ > 0pooled_height_ > 0pooled_shape.size() == 2C:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/roi_pool.hop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LpNorm::LpNormonnxruntime::LpNorm::LpNormC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/lp_norm.hop_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: vs. Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()onnxruntime::InstanceNorm::InstanceNormC:\a\_work\1\s\onnxruntime\core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\instance_norm.ccmeaninfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()Invalid dtype of ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDinfo.GetAttr("mean", &mean_).IsOK()onnxruntime::RandomNormal::RandomNormalC:\a\_work\1\s\onnxruntime\core/providers/cpu/generator/random.hscaleInvalid data type of Output type not supported in this build: Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1onnxruntime::MultinomialComputeonnxruntime::RandomUniform::RandomUniformlowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomUniformLike::RandomUniformLikeinfo.GetAttrs("shape", shape).IsOK()onnxruntime::RandomNormalLike::RandomNormalLikehighinfo.GetAttr("high", &high_).IsOK()sample_sizeinfo.GetAttr("sample_size", &num_samples_).IsOK()onnxruntime::Multinomial::MultinomialONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::RandomNormalLike::ComputeC:\a\_work\1\s\onnxruntime\core\providers\cpu\generator\random.ccCould not infer data type from input tensor with data type onnxruntime::RandomUniformLike::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cpu_contrib_kernels.cconnxruntime::contrib::RegisterQuantizationKernelsonnxruntime::contrib::RegisterCpuContribKernelsonnxruntime::contrib::RegisterNchwcKernelsdefault_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()classes_stringsonnxruntime::ml::LabelEncoder::LabelEncoderC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/label_encoder.honnxruntime::ml::GetDefaultkeys_tensorvalues_tensorKeys and values must have the same length.default_tensorLabelEncoder could not unpack default tensor result.IsOK()onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,short>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_4keys.size() == values.size()onnxruntime::ml::LabelEncoder_4<__int64,double>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,double>::LabelEncoder_4values_strings_Unusedkeys_stringsvalues_floatsLabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsdefault_floatkeys_int64svalues_int64sonnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_4onnxruntime::ml::LabelEncoder_4<__int64,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,float>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator >,__int64>::LabelEncoder_4onnxruntime::ml::LabelEncoder_4::LabelEncoder_4onnxruntime::ml::LabelEncoder_4,class std::allocator > >::LabelEncoder_4) must have the same length. (name: attributes in LabelEncoder The onnxruntime::ml::LabelEncoder_2<__int64,__int64>::LabelEncoder_2values is and the number of However, the number of key is onnxruntime::ml::LabelEncoder_2,class std::allocator >,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,float>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2,class std::allocator >,float>::LabelEncoder_2num_keys == num_valuesonnxruntime::ml::LabelEncoder_2,class std::allocator >,__int64>::LabelEncoder_2onnxruntime::ml::LabelEncoder_2<__int64,class std::basic_string,class std::allocator > >::LabelEncoder_2onnxruntime::ml::LabelEncoder_2::LabelEncoder_2LabelEncoder is missing attribute onnxruntime::ml::GetAttributeLabelEncoder could not unpack tensor attribute onnxruntime::ml::LabelEncoder_2,class std::allocator > >::LabelEncoder_2AVERAGESOFTMAX_ZEROMINSUMBRANCH_EQBRANCH_GTSOFTMAXLOGISTICTreeEnsembleRegressorLEAFBRANCH_LEQBRANCH_GTEBRANCH_LTtarget_weightsbase_values_as_tensortarget_nodeidsnodes_values_as_tensornodes_hitrates_as_tensornodes_valuesnodes_truenodeidstarget_treeidstarget_idsonnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressoronnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\treeregressor.ccclass_weights_as_tensortarget_weightstarget_weights_as_tensorn_targetspost_transformbase_values_as_tensor-C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::Initn_targets_or_classes > 0onnxruntime::ml::detail::TreeEnsembleCommon::computenodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_featureids.size()Unknown aggregation function in TreeEnsemble.aggregate_functiononnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommon::computeInput shape needs to be at least a single dimension.nodes_modesnodes_missing_value_tracks_truenodes_treeidsnodes_nodeidsnodes_falsenodeidsbase_valuesnodes_hitratesnodes_featureidsonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictionit->i < (int64_t)predictions.size()onnxruntime::ml::detail::TreeAggregator::FinalizeScorespredictions.size() == (size_t)n_targets_or_classes_this->base_values_.size() == predictions.size()onnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScoresonnxruntime::ml::detail::TreeAggregator::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorMin::MergePredictiononnxruntime::ml::detail::TreeAggregatorMax::MergePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores with flags onnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeEnsembleCommon::AddNodesFalse node must always be the next node, but it isn't at index at position Tree id mismatch. Expected but got target_class_ids.size() == target_class_treeids.size()target_class_ids.size() == target_class_nodeids.size()nodes_hitrates.empty() || nodes_hitrates_as_tensor.empty()base_values.empty() || base_values_as_tensor.empty()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_values.size() || nodes_falsenodeids.size() == nodes_values_as_tensor.size()nodes_falsenodeids.size() == nodes_truenodeids.size() in tree is already there.(coor.node_id >= 0 && coor.node_id < n_nodes_)Node target_class_weights.empty() || target_class_weights_as_tensor.empty()nodes_values.empty() || nodes_values_as_tensor.empty()nodes_modes.size() < std::numeric_limits::max()base_values.empty()Number of nodes in nodes_ () is different from n_nodes (TreeEnsemble only works on 1D, 2D tensors. (weights).Unable to find node (truenode). (falsenode).A node cannot point to itself: predictions.size() == predictions2.size()onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_aggregator.honnxruntime::ml::detail::TreeAggregatorMax::MergePrediction features.onnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggOne path in the graph requests feature but input tensor has TreeEnsembleClassifieronnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScorespredictions.size() == 2onnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::ComputeAggonnxruntime::ml::detail::TreeEnsembleCommon::ComputeAggonnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorSum::MergePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclasses.size() == 2 || classes.size() == 1onnxruntime::ml::detail::TreeAggregatorSum<__int64,float,float>::ProcessTreeNodePredictiononnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScoresonnxruntime::ml::detail::TreeEnsembleCommon::AddNodesonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::AddNodesonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier<__int64,float,float>::_set_score_binaryonnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binaryclass_treeidsclass_idsclass_weightsclass_nodeidsX dims is empty.onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifierC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_classifier.ccclasslabels_int64sbase_values_as_tensorclasslabels_stringsonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifieronnxruntime::ml::TreeEnsembleClassifier<__int64>::TreeEnsembleClassifierclasslabels_int64sonnxruntime::ml::detail::TreeEnsembleCommonClassifier::Initonnxruntime::ml::detail::TreeEnsembleCommon::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier::computeonnxruntime::ml::detail::TreeEnsembleCommon<__int64,float,float>::Initonnxruntime::ml::detail::TreeEnsembleCommonClassifier<__int64,float,float>::computeonnxruntime::ml::detail::TreeEnsembleCommonClassifier::InitZipMapZipmap only supports 1D or 2D input tensorsZipmap does not support empty dim countInput features_per_batch[] != number of classlabels[classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOpRBFPOLYLINEARUnexpected mode:Invalid argumentC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmregressor.cconnxruntime::ml::SVMRegressor::Compute!coefficients_.empty()coefficientsone_classn_supportssupport_vectorsrhoonnxruntime::ml::SVMRegressor::SVMRegressorC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.honnxruntime::ml::SVMCommon::SVMCommonSVMRegressorkernel_paramskernel_typeonnxruntime::IAllocator::AllocArrayC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\ml_common.honnxruntime::ml::batched_update_scores_inplacescores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of SVMClassifierproba_.size() == probb_.size()C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\svmclassifier.ccinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_intsprob_avectors_per_classonnxruntime::ml::SVMClassifier::SVMClassifierprob_bcoefficients_.size() > 0classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0Unsupported data type of onnxruntime::ml::SVMClassifier::Compute) != (Scale size: (onnxruntime::ml::ScalerOp::ScalerOpC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\scaler.ccEmpty scale in attributes!scale_.empty()offsetonnxruntime::ml::ScalerOp<__int64>::ScalerOponnxruntime::ml::ScalerOp::ScalerOpscale_.size() == offset_.size()onnxruntime::ml::ScalerOp::ScalerOpScaler) or 1Either both scale and offset can be of feature size (Invalid argument: input has empty dimensions.One and only one of the 'cats_*' attributes must be definedtmp_cats_int64s.empty() || tmp_cats_strings.empty()cats_int64scats_stringsonnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp,class std::allocator > >::OneHotEncoderOpnum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOponnxruntime::ml::OneHotEncoderOp<__int64>::OneHotEncoderOpC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\onehotencoder.ccOneHotEncoderUnknown Category and zeros = 0.C:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/ml_common.hInvalid normalize value of onnxruntime::ml::MakeNormalizeL1L2MAXNormalizeronnxruntime::ml::Normalizer::NormalizerC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/normalizer.hnorminfo.GetAttr("norm", &norm).IsOK()Unexpected NORMALIZE value of Rank of input to Normalized must be less than 2. Got C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearregressor.ccInput shape had more than 2 dimension. Dims=targetsonnxruntime::ml::LinearRegressor::LinearRegressorLinearRegressorinterceptsshape_size == out.size()onnxruntime::ml::CastInputToFloatonnxruntime::ml::LinearClassifier::LinearClassifierC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\linearclassifier.ccLinearClassifiermulti_classUnsupported input element type of scores_output_data.size() >= scores_output_sizeonnxruntime::ml::LinearClassifier::ComputeImpl Found:Scores output is incorrect size. Expected:imputed_value_floatsimputed_value_int64sImputerMust provide imputed_values_float_ or imputed_values_int64_ but not both.imputed_values_float_.empty() ^ imputed_values_int64_.empty()replaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedonnxruntime::ml::ImputerOp::ImputerOpC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\imputer.ccreplaced_value_floatExpected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedonnxruntime::ml::ImputerOp::ComputeInvalid typeEmpty input dimensions.Empty value of imputed values.Invalid input type:status.IsOK() && !input_dimensions_.empty()inputdimensions attribute must be providedinputdimensionsC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\feature_vectorizer.cconnxruntime::ml::FeatureVectorizer::Computeinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (FeatureVectorizerC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizerDictVectorizerint64_vocabularystring_vocabularyonnxruntime::ml::DictVectorizerOp,class std::allocator >,__int64>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,float>::DictVectorizerOponnxruntime::ml::DictVectorizerOp,class std::allocator >,double>::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,class std::basic_string,class std::allocator > >::DictVectorizerOponnxruntime::ml::DictVectorizerOp<__int64,float>::DictVectorizerOpC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp<__int64,double>::DictVectorizerOpinfo.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()CategoryMappernum_entries == int_categories.size()C:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapperInput of int64 must have output of string Input of string must have output of int64stof argument out of rangeinvalid stof argument%fonnxruntime::ml::CastMap::ComputeImplcur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of C:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\cast_map.ccDENSEonnxruntime::ml::MakeCastInvalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64TO_INT64TO_STRINGTO_FLOATC:\a\_work\1\s\onnxruntime\core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMapinfo.GetAttr("cast_to", &attr).IsOK()cast_toonnxruntime::ml::MakePackInvalid PACK_MAP value of Expected DENSE or SPARSESPARSE Expected std::map or std::mapCastMapmap_form_ != PACK_MAP::SPARSE || max_map_ > 0max_map must be > 0 if map_form is SPARSEinfo.GetAttr("max_map", &max_map_).IsOK()max_mapinfo.GetAttr("map_form", &attr).IsOK()map_formUnexpected CAST_TO value of 0.fInvalid input type of value: Input data with index: is NaNthresholdBinarizerInvalid Y argument: index is out of range: Y[] () >=Invalid Y argument: num_indices = 0Invalid argument: X input has empty dimensions.ArrayFeatureExtractornum_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given Invalid values in ''. 0 == forward. 1 == reverse.onnxruntime::scan::detail::ReadDirectionsdirections.size() == num_entriesNumber of entries in '' was C:\a\_work\1\s\onnxruntime\core\providers\cpu\controlflow\scan_utils.cconnxruntime::scan::detail::Info::InfoMisuse of LoopStateVariable. Attempt to move beyond end of sequenceonnxruntime::scan::detail::IterateSequenceonnxruntime::scan::detail::IterateSequence::::operator ()onnxruntime::scan::detail::CreateFeedsFetchesManageronnxruntime::scan::detail::AllocateOutputSubgraph must have the shape set for all outputs but did not.validIf shape was concrete we shouldn't be using a custom allocatoronnxruntime::scan::detail::OutputIterator::AllocateFinalBufferFailed to create output tensor for output #onnxruntime::scan::detail::OutputIterator::InitializeMismatch between expected shape and shape from first output is not compatible with onnxruntime::scan::detail::LoopStateVariable::Nextiteration_num_ < sequence_len_onnxruntime::scan::detail::OutputIterator::operator ++Expected AllocateFinalOutput to have been called to before we increment the iteratoris_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator::operator *cur_iteration_ < num_iterations_onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput!is_concrete_shape_onnxruntime::OrtValueTensorSlicer::Iterator::operator *Unsupported type and bias size of . Size of scale and bias (if provided) must match this. Got scale size of Size of X.shape()[axis:] == onnxruntime::`anonymous-namespace'::ComputeImplop_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::LayerNormImpl::LayerNormImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\nn\layer_norm_impl.cconnxruntime::rnn::detail::deepcpu::ActivationFuncByNameInvalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByNameInvalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByNameInvalid GRU hidden gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByNameExpecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCountC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\rnn_helpers.ccA + (M * K) <= A_endweights.quant_para_Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0falpha == 1.0f && (beta == 0.0f || beta == 1.0f)Invalid activation function of Input X must have 3 dimensions only. Actual:reluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus}. Actual:Input W must have shape {Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affineInput initial_c must have shape {Input P must have shape {onnxruntime::LSTMBase::ComputeImplC:\a\_work\1\s\onnxruntime\core\providers\cpu\rnn\lstm_base.ccSoftmaxCPU inputs N, D and N * D must be < onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessonnxruntime::EinsumTypedComputeProcessor<__int64>::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor<__int64>::PairwiseOperandProcesscandidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputNot all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputEinsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: candidate_output_dims[iter] == 1onnxruntime::EinsumTypedComputeProcessor::FinalizeOutputonnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessright.Shape().Size() == right_shape_override.Size()Right shape: Ranks of pair-wise operands must be equal. Right shape: Einsum op: Input dimensions must be equal along an axis to be reduced across all inputsleft_rank == right_rankEinsum op: Input shapes do not alignleft_dim == right_dim Left shape override: The override dims are not compatible with given tensor's shape. Left shape: onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcessleft.Shape().Size() == left_shape_override.Size() Right shape override: C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_typed_compute_processor.ccThe only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Found '.' not part of an ellipsis in input: Another operand has a dim value of in the same dimension is incompatible in the dimension . The shape is: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand C:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_compute_preprocessor.cconnxruntime::EinsumComputePreprocessor::RunFound a '.' not part of an ellipsis in input: Number of subscripts in the input equation does not match number of input tensorsEllipsis must indicate a fixed number of dimensions across all inputsEinsum subscripts string contains too many subscript labels when compared to the rank of the inputOutput subscript contains repeated lettersFound '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the inputOutput subscript contains letters not seen in the inputsnum_broadcasted_indices < num_of_ellipsis_dims_Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input The broadcasted dimensions of the inputs are incompatibleonnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims...dim_iter == rankFound a '.' not part of an ellipsis in the output subscript providedInputs have ellipses in them but the provided output subscript does not contain an ellipsis for input shape Einsum op: Unsupported data type for Diagonal rank >= 2 && dim_1 != dim_2 && input_dims[onnxruntime::narrow(dim_1)] == input_dims[onnxruntime::narrow(dim_2)]Cannot parse the diagonal elements along dims The rank of the input must match permutation size for Transposeonnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonalonnxruntime::EinsumOp::IsTransposeRequiredinput_rank == permutation.size()Einsum op: The candidate output does not match the actual output's shapeonnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopyoutput.SizeInBytes() == input.SizeInBytes()The innermost dims should have the same dim value to parse the diagonal elementsC:\a\_work\1\s\onnxruntime\core\providers\cpu\math\einsum_utils\einsum_auxiliary_ops.cconnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDimsinput_dims[rank - 2] == input_dims[rank - 1]onnxruntime::EinsumOp::TransposeLength of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: input_1.DataType() == input_2.DataType()Data types of the inputs must match for MatMulOnly 1 batch dimension is allowed for MatMulonnxruntime::EinsumOp::MatMulBatch dimension should match for MatMul;input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3Incompatible matrix dimensions for matMulinput_shape_1_override[0] == input_shape_2_override[0]Einsum op: Exception during MatMul operation: input_shape_1_override[2] == input_shape_2_override[1]InverseSkipSimplifiedLayerNormalizationgamma is expected to have 1 dimension, got last two dimensions of skip needs to be same as inputbeta is expected to have 1 dimension, got Last dimension of gamma and input does not matchbias is expected to have 1 dimension, got Last dimension of beta and input does not matchLast dimension of bias and input does not matchepsilon_ >= 0onnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormonnxruntime::contrib::SkipLayerNorm::SkipLayerNormskip is expected to have 3 or 2 dimensions, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNorminput is expected to have 3 or 2 dimensions, got skip is expected to have same shape as input or, a batch size of 1 or no batch size when input has 3 dimensionsonnxruntime::contrib::SkipLayerNorm::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::Computeonnxruntime::contrib::SkipLayerNorm::ComputeScaleinfo.GetAttr("alpha", &alpha_).IsOK()onnxruntime::contrib::Affine::Affineonnxruntime::contrib::Scale::ScaleC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/element_wise_ops.hinfo.GetAttr("beta", &beta_).IsOK()info.GetAttr("scale", &scale_).IsOK()onnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernelonnxruntime::ElementWiseKernel >::Computeonnxruntime::ElementWiseKernel >::ComputeImageScalerInput is expected to have four dimensions corresponding to [N,C,H,W], got ) does not match the number of channels (Bias size (onnxruntime::contrib::ImageScaler::ImageScalerC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\image_scaler.hDynamicSlice input dimensions instead) + bottomBorder (Attribute border needs to be specified with four border elements, got onnxruntime::contrib::Crop::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\crop.h) + rightBorder () needs to be greater than or equal to the leftBorder () needs to be greater than or equal to the topBorder (Input's height () + scale_[1] (CropInput's width () + scale_[0] (src_tokens_len >= prev_suffix_match_idx_datamax_ngram_size_ >= min_ngram_size_max_ngram_size_ > 0info.GetAttr("max_ngram_size", &max_ngram_size_).IsOK()BifurcationDetectorpred_tokens_len == (src_tokens_len + 1 - prev_suffix_match_idx_data)onnxruntime::contrib::BifurcationDetector::Computeinfo.GetAttr("min_ngram_size", &min_ngram_size_).IsOK()min_ngram_sizemax_ngram_sizemin_ngram_size_ > 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bifurcation_detector.honnxruntime::contrib::BifurcationDetector::BifurcationDetectorscores_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Computeinput_ids_dims.size() == 2onnxruntime::contrib::NGramRepeatBlock::Compute::::operator ()NGramRepeatBlockTidscores_dims[0] == batch_sizeinfo.GetAttr("ngram_size", &ngram_size_).IsOK()ngram_sizetoken_id < vocab_sizengram_size_ > 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\ngram_repeat_block.honnxruntime::contrib::NGramRepeatBlock::NGramRepeatBlockonnxruntime::contrib::BiasGelu::Computeuse_approximationC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\bias_gelu.cconnxruntime::contrib::BiasGelu::ComputeInput shape dimensions mismatch:The second input of CDist kernel has wrong shape: The first input of CDist kernel has wrong shape: C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\cdist.honnxruntime::contrib::CDist::CDistinfo.GetAttr("metric", &metric).IsOK()metriconnxruntime::contrib::CDist::CDisteuclideansqeuclideanCDistCropAndResizeC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/crop_and_resize.hNull crop_size_ptronnxruntime::contrib::CropAndResize::CropAndResize specified. It should be either bilinear or nearestNumber of dimensions for crop size should be exactly 1ConvTransposeWithDynamicPadsInput tensor to Unique op should be 1Donnxruntime::contrib::MaxpoolWithMask::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\maxpool_with_mask.hMaxpoolWithMaskbuf_size > 0Quantized and packed blob size differ from expected!(size_t)blob_len == buf_sizeMatMulFpQ4C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\matmul_fpq4.ccRight hand side of MatMulFpQ4 must be a 2D matrix!b_shape.NumDimensions() == 2Operator MatMulFpQ4 not yet supported on this hardware platform.blk_quant_typeSecond input of MatMulFpQ4 must be a 1D blob!blob_shape.NumDimensions() == 1onnxruntime::contrib::MatMulFpQ4::Computeonnxruntime::contrib::MatMulBnb4::MatMulBnb4C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_bnb4.ccNStatus::OK() == info.GetAttr("N", &N_)KStatus::OK() == info.GetAttr("K", &K_)only block size 16, 32, 64, 128, 256 are supported.quant_type == FP4 || quant_type == NF4onnxruntime::contrib::DequantizeBlockwiseBnb4C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dequantize_blockwise_bnb4.hInvalid quant_type, only 0 (FP4) and 1 (NF4) are supported.quant_type_ == FP4 || quant_type_ == NF4onnxruntime::contrib::MatMulBnb4::ComputeMatMulBnb4block_sizeStatus::OK() == info.GetAttr("block_size", &block_size_)quant_typeStatus::OK() == info.GetAttr("quant_type", &quant_type_)920k2ʾM?5=qx=$>|>:>>?9??;*??>?*>>**C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_nbits.cconnxruntime::contrib::MatMulNBits::ComputeMatMulNBitsaccuracy_levelOnly 4b quantization is supported for MatMulNBits op, additional bits support is planned.nbits_ == 4onnxruntime::contrib::MatMulNBits::MatMulNBitsbitsTransposeMatMulInvalid assumption of output element sizeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\murmur_hash3.ccinput_num_bytes % 4 == 0sizeof(uint32_t) == output_element_bytesMurmurHash3positiveonnxruntime::contrib::MurmurHash3::ComputekeysCOO k index: is out of bounds of lhs_right: COO m index: is out of bounds of out_left: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()COO indices must be 2-D, got: onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo<__int64>::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCoo::operator ()Outer size must be M + 1Expecting the same number NNZ == size of Inner indicesCurrently support only COO and CSR(x64) formatsCan not multiply A and B as inner dimension does not match. inner_A: vs inner_B: Expecting 2xValues == indicesExpecting COO 2-D indices shapeCurrently supporting only 2-D matricesSparseToDenseMatMulC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cconnxruntime::contrib::SparseToDenseMatMul::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\word_conv_embedding.cconnxruntime::contrib::WordConvEmbedding::ComputeWordConvEmbeddingChar embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 1 does not match conv_window_size attribute .embedding_sizechar_embedding_sizeconv_window_size conv filter size: Conv filter size does not match embedding_size attribute. embedding_size attribute: attribute mincharnum must have a positive valueattribute mincharnum is not setseparatorsmincharnum_ > 0pad_valueC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\tokenizer.ccmincharnumattribute pad_value is not setmarkTokenizeronnxruntime::contrib::Tokenizer::Tokenizerattribute mark is not settensor(string) expected as inputMatch contains invalid utf8 chars: Input dimensions are either [C] or [N][C] allowedCan not digest tokenexp: Can not digest separators: Input string contains invalid utf8 chars: Input string contains invalid utf8 chars!separators.empty()separators must not be empty!char_tokenezation_ || mincharnum_ < 2mincharnum is too big for char level tokenezationEither one of the separators OR tokenexp attributes required but none is settokenexp!tokenexp.empty()Expecting a non-empty tokenexpAttention mechanism memory sequence lengths must have shape {Attention mechanism memory shape error! Expected: {], while found!onnxruntime::contrib::DeepCpuAttnLstmOp::ComputeAttnLSTM}, actural: C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.cconnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOpstatic_cast(activation_func_names.size()) == num_directions_ * 3C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::ComputeImplonnxruntime::contrib::DeepCpuAttnLstmOp::ValidateInputsAttention layer weight shape error! Expected: {}. Got: Attention query layer weight shape error! Expected:{, aw_attn_size}. Got:Attention v weight shape error! Expected:{, am_attn_size}, Got:Attention mechanism memory sequence lengths value must in (0, }, Got: Attention memory layer weight shape error! Expected:{ should be a scalar. Got shape of Not ImplementedSubgraph SessionState was not found for 'decoder' attribute.onnxruntime::contrib::transformers::Sampling::SetupSubgraphExecutionInfoinit_run_gpt_subgraph_ == nullptrinfo.GetAttr("decoder", &proto).IsOK()gpt_subgraph_ == nullptrinit_decoderdecoderencoderinfo.GetAttr("encoder", &proto).IsOK()onnxruntime::contrib::transformers::Sampling::InitC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling.ccSamplingparameters_.model_type == IGenerationParameters::kModelTypeGptNode input is requiredonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executemax_lengthmin_lengthonnxruntime::contrib::transformers::GreedySearchBase::InitializeC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_base.honnxruntime::contrib::transformers::GreedySearchGpt::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/greedy_search_impl_gpt.hInput 'presence_mask' is expected to have 2 dimensions, got input_ids and presence_mask must have the same batch_sizeInput 'attention_mask' is expected to have 2 dimensions, got Input 'attention_mask' is expected to have same shape as input_idsInput 'prefix_vocab_mask' shape[1] shall be vocab_size, got Input 'attention_mask' is expected to have 3 dimensions, got Input 'prefix_vocab_mask' is expected to be 2 dimensions, got input_ids and prefix_vocab_mask must have the same batch_sizeInput 'vocab_mask' is expected to have 1 dimension, got Input 'vocab_mask' dimension 0 does not match with vocab_size's, got Input 'decoder_input_ids' is expected to have 2 dimensions, got Input 'input_ids' is expected to have 2 dimensions, got Input 'input_features' is expected to have 3 dimensions, got onnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokeninit_run_gpt_subgraph_ && gpt_subgraph_ && init_run_gpt_subgraph_->past_present_share_buffer_ == gpt_subgraph_->past_present_share_buffer_init_run_decoder_feeds_fetches_manager_past_present_share_buffer mode must be same for init decoder and decoder subgraphesdecoder_feeds_fetches_manager_init_run_decoder_session_statedecoder_session_stateonnxruntime::contrib::transformers::Sampling::ComputeInput 'presence_mask' shape[1] shall be vocab_size, got num_heads > 0onnxruntime::contrib::RotaryEmbedding::RotaryEmbeddinginterleavednum_heads must be provided if rotary_embedding_dim is specifiedrotary_embedding_dimUpdating cos_cache and sin_cache in RotaryEmbedding is not currently supportedonnxruntime::contrib::RotaryEmbedding::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\rotary_embedding.ccInput 'position_ids' dimension 1 should be of size max_sequence_length, got Input 'position_ids' dimension 0 should be of size sequence_length, got rotary_embedding_dim must be less than or equal to batch_size, got num_heads must be provided if rotary_embedding_dim is head_sizeInputs 'cos_cache' and 'sin_cache' are expected to have specifiedInput 'sin_cache' is expected to have 2 dimensions, got the same shapeInput 'position_ids' is expected to have 0, 1, or 2 Input 'cos_cache' is expected to have 2 dimensions, got Input 'x' is expected to have 3 or 4 dimensions, got dimensions, got MRotaryEmbeddingInput 'cos_cache' dimension 1 should be same as Input 'cos_cache' dimension 0 should be same as head_size / 2 or rotary_embedding_dim / 2, got qkv_hidden_sizespast_present_share_bufferC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.hdo_rotaryinfo.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0onnxruntime::contrib::AttentionBase::AttentionBaseonnxruntime::contrib::MultiHeadAttention::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\multihead_attention.ccPacked QKV of shape (B, L, N, 3, H) not implemented for CPUPacked KV not implemented for CPUpast_sequence_length tensor must be of one element when past_present_share_buffer is setInput 'past_key' and 'past_value' shall be both present or both absentInput 'past_key' dimension 3 should be same as head_size, got Input 'past_value' dimension 3 should be same as head_size, got Input 'past_value' dimension 1 should be same as number of heads, got Input 'past_key' and 'past_value' shall have same dim 2 (past_sequence_length). Input 'past_value' dimension 0 should be batch_size, got Input 'past_key' dimension 1 should be same as number of heads, got Input 'past_value' is expected to have 4 dimensions, got Input 'past_key' dimension 0 should be batch_size, got Input 'query' is expected to have 3 or 5 dimensions, got Input 'past_key' is expected to have 4 dimensions, got onnxruntime::contrib::MultiHeadAttention::MultiHeadAttentionMultiHeadAttentionInput 'key' and 'value' shall have the same dim 1 (kv_sequence_length)Input 'past_key' and 'past_value' shall have the same dim 2 (kv_sequence_length)Input 'value' is expected to have 3 or 4 dimensions, got Input 'query' and 'value' shall have same dim 0 (batch_size)'bias' is not allowed for packed kv. Input 'key_padding_mask' shape shall be 1D, 2D, or 3DExpect 'query' shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kvInput 'bias' is expected to have 1 dimension, got Expect 'key' shape (batch_size, num_heads, kv_sequence_length, head_size) for past_keyInput 'query' is expected to have 3 or 5 dimensions when key is empty, got Expect 'key' shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kvExpect 'value' be none when 'key' has packed kv format.Input 'query' and 'key' shall have same dim 0 (batch size)Input 'query' and 'key' shall have same dim 2 (hidden_size)Input 'query' is expected to have 3 dimensions when key is given, got Input 'key' is expected to have 3, 4, or 5 dimensions, got 4D mask in attention cpu kernel is not supportedonnxruntime::contrib::AddBiasReshapeonnxruntime::contrib::AddBiasTransposeonnxruntime::contrib::AttentionCPUBase::ApplyAttentionC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_cpu_base.hInput 'relative_position_bias' dimension 3 should be same as total_sequence_length, got onnxruntime::contrib::MaybeTransposeToBNSHAndAddBiasInput 'relative_position_bias' dimension 1 should be same as number of heads, got Input 'relative_position_bias' dimension 2 should be same as sequence_length, got Input 'relative_position_bias' is expected to have 4 dimensions, got Input 'relative_position_bias' dimension 0 should be batch_size or 1, got onnxruntime::contrib::transformers::GreedySearch::SetupSubgraphExecutionInfoonnxruntime::contrib::transformers::GreedySearch::Computeonnxruntime::contrib::transformers::GreedySearch::InitC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search.ccGreedySearchonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::GenerateNextTokenonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::Executeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::Initializeonnxruntime::contrib::transformers::GreedySearchBase::CheckInputsonnxruntime::contrib::transformers::GreedySearchGpt::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemmC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\fused_conv.ccGetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::FusedConvFloat::FusedConvFloataxis <= X_NumDims && axis >= -X_NumDimsAxis must be within range []. Axis is C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\expand_dims.honnxruntime::contrib::ExpandDims::Computeaxis_tensor->Shape().IsScalar()An axis tensor must be a scalar tensor.ExpandDimsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\embed_layer_norm.cconnxruntime::contrib::EmbedLayerNormBase::EmbedLayerNormBaseinput index out of rangeonnxruntime::contrib::EmbedLayerNorm::ComputeEncoder subgraph shall have 2 inputs (encoder_input_ids, decoder_input_ids)whisper_encoder_subgraph_ == nullptrt5_decoder_subgraph_ == nullptrEncoder subgraph shall have 3 inputs when decoder_start_token_id attribute is availableEncoder subgraph shall have 2 inputs when decoder_start_token_id attribute is emptyt5_encoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearch::SetupSubgraphExecutionInfoC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search.cconnxruntime::contrib::transformers::BeamSearch::Initparameters_->model_type == IGenerationParameters::kModelTypeGpt || parameters_->model_type == IGenerationParameters::kModelTypeT5 || parameters_->model_type == IGenerationParameters::kModelTypeWhisperWhisperBeamSearchBeamSearchdecoder_output_cross_qkonnxruntime::contrib::transformers::BeamSearchGpt::Executeonnxruntime::contrib::transformers::BeamSearchBase::CheckInputs'num_return_sequences' has to be smaller or equal to 'num_beams'.length_penaltynum_return_sequencesnum_beamsC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_base.honnxruntime::contrib::transformers::BeamSearchBase::InitializeC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_gpt.honnxruntime::contrib::transformers::BeamSearchGpt::ExecuteMust specify input cross_qk_layer_headdecoder_subgraph_.past_present_share_buffer_decoder subgraph: output_cross_qk could only work with past_present_share_bufferdecoder_subgraph_.has_decoder_masked_attention_decoder subgraph: output_cross_qk could only work with has_decoder_masked_attentionC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_whisper.honnxruntime::contrib::transformers::BeamSearchWhisper::Executeparameters->no_speech_token_id >= 0 && parameters->no_speech_token_id < parameters->vocab_sizeno_speech_token_id is out of range, it is , vocab_size is onnxruntime::contrib::transformers::BeamSearchT5::ExecuteC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/beam_search_impl_t5.honnxruntime::contrib::transformers::BeamSearchT5::Executeparameters->sequence_length == 1onnxruntime::contrib::transformers::BeamSearchBase::CheckInputsonnxruntime::contrib::transformers::BeamSearchBase::InitializeModel type is not supported.encoder_feeds_fetches_manager_encoder_session_stateSubgraph SessionState was not found for 'encoder' attribute.onnxruntime::contrib::transformers::BeamSearch::Computewhisper_decoder_subgraph_ == nullptronnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchBase::GenerateNextTokenonnxruntime::contrib::transformers::BeamSearchWhisper::Executeinput_tensor_cross_qk_layer_head != nullptrRotary embedding is not supported in Attention CPU kernel. Please fuse the model with MHA + RotaryEmbedding.C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention.cconnxruntime::contrib::Attention::ComputeSampleOpQGemm : zero point and scale of input b should have same shape sizeQGemm : zero point of y must be null or a scalar or 1D tensor of size 1QGemm : scale of y must be null or a scalar or 1D tensor of size 1TATBTCTYZTYC:\a\_work\1\s\onnxruntime\core/providers/cpu/math/gemm_helper.honnxruntime::contrib::QGemm::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\quant_gemm.ccQGemm : scale of input a must be a scalar or 1D tensor of size 1onnxruntime::contrib::QGemm::CheckInputsQGemm : zero point of input a must be a scalar or 1D tensor of size 1QGemm : zero point of input b must be a scalar or 1D tensor of size 1 or NQGemm : scale of input b must be a scalar or 1D tensor of size 1 or NC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qembed_layer_norm.cconnxruntime::contrib::QEmbedLayerNorm::ComputeBeta scale must be a scalar or 1D tensor of size 1Word embedding zero point must be a scalar or 1D tensor of size 1Position embedding zero point must be a scalar or 1D tensor of size 1Segment embedding zero point must be a scalar or 1D tensor of size 1Gamma zero point must be a scalar or 1D tensor of size 1Beta zero point must be a scalar or 1D tensor of size 1QEmbedLayerNormalizationWord embedding scale must be a scalar or 1D tensor of size 1Position embedding scale must be a scalar or 1D tensor of size 1Segment embedding scale must be a scalar or 1D tensor of size 1Gamma scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::NhwcMaxPool::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\nhwc_max_pool.ccinput_shape.Size() > 0 || N == 0onnxruntime::contrib::NhwcMaxPool::Computeonnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommonC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_matmul.ccMatmulInteger : b zero point is not validIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)onnxruntime::contrib::DynamicQuantizeMatMul::ComputeMatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)onnxruntime::contrib::MatMulIntegerToFloat::ComputeInput must have shape {W_zero_pointR_zero_pointW_scaleR_scaleWeight zero point must be zeroDynamicQuantizeLSTM : Weight point must be constantRecurrentonnxruntime::contrib::DynamicQuantizeLSTM::PrePackC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\dynamic_quantize_lstm.cc} for per-channel quantization. Actual:, 4*} for per-tensor/layer quantization or shape {onnxruntime::contrib::QAttention::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\attention_quant.ccinput scale must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1.QLinearMulQLinearAddonnxruntime::contrib::`anonymous-namespace'::QLinearImplIsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_binary_op.ccMatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::`anonymous-namespace'::BuildLookupTableIfFixedtensor_x_scale == nullptr || IsScalarOr1ElementVector(tensor_x_scale)opset must be existed in attributes of QlinearSoftmaxC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_softmax.cconnxruntime::contrib::QLinearSoftmax::ComputeImplOpset13onnxruntime::contrib::QLinearSoftmax::QLinearSoftmaxQLinearSoftmaxQLinearLeakyReluQLinearSigmoidinput y_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale)QLinear Pooling unsupported pooling size!tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)Input x_scale must be a scalar or 1D tensor of size 1onnxruntime::contrib::QLinearAveragePool::ComputeImplIsScalarOr1ElementVector(tensor_x_scale)input x_zero_point must be a scalar or 1D tensor of size 1 if givenC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_pool.ccinput y_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_where.cconnxruntime::contrib::QLinearWhere::QLinearWheretensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() && tensor_x_zero_point->GetElementType() == tensor_z_zero_point->GetElementType() && tensor_y_zero_point->GetElementType() == tensor_z_zero_point->GetElementType()Wrong input type encountered for zero point input def of x, y, ztensor_x_scale->IsDataType()Input scale is not float for quantized input x @ 2Input scale is not float for quantized input y @ 5onnxruntime::contrib::QLinearWhere::ComputeInput scale is not float for quantized output z @ 7tensor_y_scale->IsDataType()Wrong input type encountered for zero point of quantized input @tensor_z_scale->IsDataType()QLinearWhere inputs! (condition, x, x_scale, x_zero_point, y, y_scale, y_zero_point, z_scale, z_zero_point)input_def_count == kExpected_input_countThere must be Each input must be (tensor, scale, zero_point) tuple!onnxruntime::contrib::QLinearConcat::QLinearConcatinput_def_count >= 5 && (input_def_count - 2) % 3 == 0Input scale is not float for input def @C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_concat.cctensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @onnxruntime::contrib::QLinearConcat::Computeinput_count_x3 >= 3 && input_count_x3 % 3 == 0QLinearConcatInput scale is not float for quantized input @onnxruntime::contrib::QLinearGlobalAveragePool::ComputeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_global_average_pool.ccIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)MatMulInteger16C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\matmul_integer16.ccA != nullptr && B != nullptronnxruntime::contrib::MatMulInteger16::ComputeX_rank == 4onnxruntime::contrib::ReorderInput::Computetransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'onnxruntime::contrib::ReorderOutput::Computechannels_ <= X_shape[1]C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.cc(channels % 4) == 0(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.onnxruntime::contrib::NchwcConv::ComputeX_shape.NumDimensions() == 4X_shape.size() == 4onnxruntime::contrib::NchwcUpsample::Computeonnxruntime::contrib::NchwcPoolBase::NchwcPool(X_shape[1] % MlasNchwcGetBlockSize()) == 0info.GetAttr("channels", &channels_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutputonnxruntime::contrib::ReorderInput::ReorderInputC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\nchwc_ops.honnxruntime::contrib::NchwcConv::NchwcConvpool_attrs_.kernel_shape.size() == 2invalid channel countchannels_ > 0onnxruntime::contrib::NchwcUpsample::NchwcUpsamplescales_.size() == 4onnxruntime::contrib::NchwcPoolBase::NchwcPoolBaseinfo.GetAttrs("scales", scales_).IsOK()' for NCHWc UpsampleUnsupported transformation mode 'scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::ml::GetVectorAttrsOrDefaultC:\a\_work\1\s\onnxruntime\core\providers\cpu\ml\tree_ensemble_helper.cc'.n_dims == 1onnxruntime::ml::GetNumberOfElementsAttrsOrDefaultproto.data_type() == proto_type' has one dimension but is empty.(for attribute 'Unexpected type (n_elements > 0' must be a vector.Attribute '' is specified but is empty.Attribute:'Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0Input 0 is expected to have 1 or more dimensions, got bad tNumbad pNumbad protect modeT_SHORT is not supported in AutoGrowlabel is already set by L()label is not set by L()can't convertbad vsib addressingrounding is already setopmask is already seterr munmapbad label stringer(embedded rounding) is invalidsae(suppress all exceptions) is invalidevex is invalidk0 is invalidlabel is too farlabel is redefinedbad alignimm is too bigcan't protectbad parametercode is not copyablelabel is not foundbad mem sizeMEM size is not specifiedoffset is too bigcan't use 64bit disp(use (void*))can't allocunder local labelover local labelbad st combinationcode is too bigbad addressingbad size of registerbad combinationesp can't be indexbad scaleinvalid rip in AutoGrowinvalid zeroinvalid opmask with memoryinvalid broadcastsame regs are invalidnot supportedx2APIC is not supportedinvalid mib addressunknown err!!!Hybrid:%d %x %x %x %x!!! %d,Pcore:%d,%d %d %d .maskflag smt: Ecore:.maskend.zeroflag@b@@@fERR Pack bad n=%d(%d) ERR Pack::init bad n=%d .kloop.read.nloop.kend.unkbloop.kbloopThread Block:(%d,%d) .rowloop1.proc48.colloopThread in use:%d of %d, Nx%d .rowloop3.rowloop2.proc32.colend.maskproc.NOZPCache Size:%zu used:%zu GEMM MStep:%d NStep:%d KStep:%d %s bestla::parallel::GemmRun @f@DfDf   xئP@(p`॑`H0 0 裑x`H𤑀Ȥ0覑p`H@ rows[.. row[ [seqno=onnxruntime::contrib::BahdanauAttention::PrepareMemorymem_steps <= max_memory_steps_ && mem_steps > 0!normalize_not support normalize yet.Real memory steps is not in (0, C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\attnlstm\bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttentionC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\logits_processor.cconnxruntime::contrib::transformers::NoRepeatNGramLogitsProcessor::Processprefix.size() == narrow(prefix_length)decoder_start_token_idpad_token_ideos_token_idmodel_typefilter_valuetop_ptemperatureno_repeat_ngram_sizevocab_sizecustompresence_penaltymin_tokens_to_keepC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\sampling_parameters.cconnxruntime::contrib::transformers::SamplingParameters::ParseFromInputsseed >= 0Seed must be >= 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\greedy_search_parameters.ccinput_ids shall have 2 dimensions. Got dims.size() == 2) shall be greater than input sequence length (context != nullptronnxruntime::contrib::transformers::GreedySearchParameters::ParseFromInputsrepetition_penalty shall be greater than 0, got repetition_penalty > 0.0fmax_length (max_length > sequence_length) shall be no more than max_length <= kMaxSequenceLength@start_of_lm_token_idno_speech_token_idno_timestamps_token_idbeginning_timestamp_token_idnum_beams >= 1 && num_beams <= kMaxNumBeamsnum_return_sequences shall be a positive integer, got num_return_sequences >= 1) shall be be no more than num_beams (, expecting extra_decoder_tensor first dim not same as batch_size. Got extra_decoder_tensor_dims[0] == batch_sizenum_beams shall be a positive integer no more than model_type == IGenerationParameters::kModelTypeWhisperonnxruntime::contrib::transformers::WhisperBeamSearchParameters::ParseFromAttributestranslate_token_idtranscribe_token_idnum_return_sequences (num_beams >= num_return_sequenceslogits_processor shall be a non-negative integer, got logits_processor >= 0C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_parameters.ccdecoder_input_ids shall have 2 dimensions. Got decoder_dims.size() == 2extra_decoder_tensor shall have 2 dimensions. Got extra_decoder_tensor_dims.size() == 2early_stoppingonnxruntime::contrib::transformers::BeamSearchParameters::ParseFromInputsinput_features shall have 3 dimensions. Got dims.size() == 3subgraph input 2 (attention_mask) shall have int32 typesubgraph output 0 (logits) shall be float or float16 data typesubgraph input 3 (past_0) shall shall have same data type of logits outputsubgraph output 1 (present_0) shall shall have same data type of logits outputsubgraph logits output is expected to have 3 dimension, got subgraph past state dimension 2 shall have a positive value for vocabulary sizesubgraph input 0 (input_ids) shall have int32 typesubgraph input 1 (position_ids) shall have int32 typeattention_masksubgraph input 2 shall be named as attention_mask, got: past_0subgraph input 3 shall be named as past_0, got: input_idssubgraph input 0 shall be named as input_ids, got: position_idssubgraph input 1 shall be named as position_ids, got: logitssubgraph output 0 shall be named as logits, got: present_0subgraph input 1 shall be named as present_0, got: subgraph past state is expected to have 5 dimension, got subgraph past state dimension 0 shall have length of 2subgraph past state dimension 2 shall have a positive value for number of headssubgraph past state dimension 4 shall have a positive value for hidden size per headinput_ids_shape.NumDimensions() == 2Invalid GPT-2 subgraph: number of outputs shall be larger than 1 (Need past state in outputs).onnxruntime::contrib::transformers::GptSubgraph::ValidateInvalid 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)Setup must be called before CreateInitialFeedssession_state_ != nullptronnxruntime::contrib::transformers::GptSubgraph::CreateInitialFeedsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_gpt.ccBeamSearch op: An implementation for the input type onnxruntime::contrib::GenerationCpuDeviceHelper::CreateGptInputsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\generation_device_helper.cconnxruntime::contrib::GenerationCpuDeviceHelper::CreateEncoderInputsCPU beam search current not support output cross QK.input_features_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::CreateWhisperEncoderInputsstart_token_id >= 0original_decoder_input_ids_shape.NumDimensions() == 2last_outputs.size() >= static_cast(1) + num_present_tensorsonnxruntime::contrib::GenerationCpuDeviceHelper::UpdateDecoderFeedsonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandBufferis_kv_cacheC:\a\_work\1\s\onnxruntime\contrib_ops/cpu/transformers/sampling_cpu_helper.honnxruntime::contrib::SamplingCpuHelper::Sampleonnxruntime::contrib::GenerationCpuDeviceHelper::ExpandInputslogits_shape.NumDimensions() == 3onnxruntime::contrib::GenerationCpuDeviceHelper::ProcessLogitsonnxruntime::contrib::GenerationCpuDeviceHelper::GreedySearchProcessLogitselement_type == DataTypeImpl::GetType()Inputs 'mask_index' with 4D data shall have is_unidirectional set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got Expect to have present state output when past state input is givenInputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size or 3 * batch_size + 2Inputs 'mask_index' with 2D data shall have shape batch_size x total_sequence_lengthInputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x total_sequence_lengthInputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)onnxruntime::contrib::AttentionBase::GetPresentC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\bert\attention_base.cc k_hidden_size= q_hidden_size=Input 'bias' dimension 0 should have same length as sum of Q/K/V hidden sizes:Input 'past' expect k_hidden_size == v_hidden_sizeqkv_hidden_sizes first element should be same as the secondHidden size of Q, K and V shall be samebias_dims[0]= v_hidden_size=Inputs 'past' dimension 2 shall have length of when past_present_share_buffer, past tensor sequence must not smaller than total_sequqnce_length Input 'relative_position_bias' dimension 0 should be same as batch_size or 1, got max_sequence_length not matching from mask and past when past_present_share_buffer_ is setInput 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInput 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'qkv_hidden_sizes attribute should have 3 elementshidden_size should be divisible by num_heads:Attention cannot have both past and relative_position_biasInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got onnxruntime::contrib::transformers::Subgraph::GetParameterssubgraph output present_key_self_0 is expected to have 4 dimension, got past_sequence_lengthbeam_widthonnxruntime::contrib::transformers::Subgraph::SetupC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_base.ccDecoderMaskedSelfAttentionDecoderMaskedMultiHeadAttentionactivation_params count mismatchunimplemented activation: input_ids and position_ids shall have same sequence_lengthInput 0 and 1 shall have same shapeposition_ids's first dimension shall be 1 or batch_sizeword_embedding and segment_embedding shall have same dimension 1segment_embedding is expected to have 2 dimensions, got gamma is expected to have 1 dimensions, got word_embedding and position_embedding shall have same dimension 1input_ids is expected to have 2 dimensions, got Input 0 and 7 (mask) shall have same shapeposition_embedding is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got beta is expected to have 1 dimensions, got gamma is expected to have size of beta is expected to have size of C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\beam_search_scorer.cconnxruntime::contrib::transformers::BeamHypotheses::Outputonnxruntime::contrib::transformers::BeamSearchScorer::Processnext_scores.size() == next_tokens.size()top_k <= beams_used_output_sequences != nullptrstatic_cast(hypothesis_buffer_used_) <= hypothesis_buffer_.size()onnxruntime::contrib::transformers::BeamSearchScorer::FinalizeBatch can only be done if all beams have been generatednext_scores.size() == next_indices.size()beam_idx == num_beams_beam_hyp.beams_used_ == gsl::narrow_cast(num_beams_)onnxruntime::contrib::transformers::T5DecoderSubgraph::CreateInitialFeedsdecoder subgraph output shall have same data type as that of encoder_hidden_statesInvalid hidden_states expension: has_hidden_state_ == falsedecoder subgraph input 1 (encoder_attention_mask) shall have int32 typedecoder subgraph input 0 (input_ids) shall have int32 typedecoder subgraph past inputs shall have same data type as that of encoder_hidden_statesdecoder subgraph input 2 (encoder_hidden_states) shall have float or float16 typeC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_decoder.cconnxruntime::contrib::transformers::T5DecoderSubgraph::Validatenumber of inputs expected to be kFirstPastInputIndex + 4 * layers, got:decoder_masked_attention shall use with past_present_share_bufferencoder_hidden_statesdecoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder_attention_maskdecoder subgraph output 0 shall be named as logits, got: decoder subgraph input 2 shall be named as encoder_hidden_states, got: number of inputs expected to be kFirstPastInputIndex + 4 * layers + 1, got:number of inputs expected to be kFirstPastInputIndex + 4 * layers + 3, got:decoder subgraph input 0 shall be named as input_ids, got: number of outputs expected to be 1 + 2 * layers, got:onnxruntime::contrib::transformers::WhisperDecoderSubgraph::CreateInitialFeedsdecoder subgraph input 1 shall be named as encoder_hidden_states, got: When outputing cross qk, number of outputs expected to be first_present_output_index_decoder subgraph past inputs shall have same data type as that of encoder_hidden_states.decoder subgraph input 1 (encoder_hidden_states) shall have float or float16 type + 2 * layers, got:C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_decoder.cc + 3 * layers, got:number of outputs expected to be first_present_output_index_onnxruntime::contrib::transformers::WhisperDecoderSubgraph::Validateexpect 2 inputs, got:encoder subgraph outputs 1, 2, ... shall have same data typeencoder subgraph output 0 (logits) shall be float or float16 data typecpu_allocator shouldn't be nullptronnxruntime::contrib::transformers::WhisperEncoderSubgraph::CreateInitialFeedsencoder subgraph input 0 shall be named as encoder_input_ids, got: encoder_input_idsencoder subgraph input 2 shall be named as decoder_input_ids, got: decoder_input_idsC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_whisper_encoder.cconnxruntime::contrib::transformers::WhisperEncoderSubgraph::Validatenumber of outputs expected to be 2 + 4 * layers, got:expect >=6 outputs, got:encoder subgraph output 3 shall be named as present_value_self_0, got: present_value_self_0encoder subgraph input 1 (decoder_input_ids) shall have int32 typeencoder subgraph input 0 (encoder_input_features) shall have float32 or float16 typeencoder subgraph output 1 shall be named encoder_hidden_states, got: encoder subgraph output 0 shall be named as logits, got: encoder subgraph output 2 shall be named as present_key_self_0, got: present_key_self_0encoder subgraph input 1 shall be named as encoder_attention_mask, got: encoder subgraph input 0 (encoder_input_ids) shall have int32 typeonnxruntime::contrib::transformers::T5EncoderSubgraph::ValidateC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\transformers\subgraph_t5_encoder.ccexpect 3 inputs, got:onnxruntime::contrib::transformers::T5EncoderSubgraph::CreateInitialFeedsencoder subgraph input 1 (encoder_attention_mask) shall have int32 typeencoder subgraph input 2 (decoder_input_ids) shall have int32 typeonnxruntime::contrib::QlinearBuildLookupTableC:\a\_work\1\s\onnxruntime\contrib_ops\cpu\quantization\qlinear_lookup_table.ccQlinearBuildLookupTable : 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 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1This OrtValue is neither Tensor, SparseTensor, Map or Sequence typeThis TypeProto does not have ValueCase setC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_typeinfo.ccTensor types should have been handled alreadytensor_data_type != nullptrOrtTypeInfo::FromOrtValueOrtValue is TensorSequence type but has no element Tensor DataType.OrtTypeInfo::FromTypeProtoThe type is not tensor, sparse tensor, sequence, map or optional typeCudaOpenVINO_GPUWebGPU_BufferHipPinnedHipCudaPinnedSpecified device is not supported.onnxruntime::IAllocator::CalcMemSizeForArrayWithAlignment::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\allocator.cc size= nmemb= alignment=C:\a\_work\1\s\onnxruntime\core\framework\tensor_type_and_shape.ccUnsupported indices_format passedArgument is not a tensorOrtApis::GetTensorTypeAndShapeTensor type is undefinedthe ort_value must contain a constructed tensor or sparse tensor`anonymous-namespace'::GetIndicesTensorC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_sequence_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kSequenceTypeOrtSequenceTypeInfo::FromTypePrototype_proto is not of type sequence!C:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_optional_type_info.ccvalue_case == ONNX_NAMESPACE::TypeProto::kOptionalTypeOrtOptionalTypeInfo::FromTypePrototype_proto is not of optional typeOrtMapTypeInfo::FromTypeProtoC:\a\_work\1\s\onnxruntime\core\framework\onnxruntime_map_type_info.cctype_proto is not of type map!value_case == ONNX_NAMESPACE::TypeProto::kMapTypedtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Initonnxruntime::Tensor::SizeInBytestensor size overflowonnxruntime::Tensor::Tensorelt_type != nullptrtensor failed memory size calculationC:\a\_work\1\s\onnxruntime\core\framework\tensor.cconnxruntime::Tensor::CalculateTensorStorageSizeshape.Size() must >=0onnxruntime::CPUDataTransfer::CopyTensorsrc.SizeInBytes() == dst.SizeInBytes()onnxruntime::IDataTransfer::CopySparseTensorsC:\a\_work\1\s\onnxruntime\core\framework\data_transfer.cconnxruntime::IDataTransfer::CopyTensorsonnxruntime::IDataTransfer::CopyTensoronnxruntime::data_types_internal::SequenceTypeHelper::Setelem_proto != nullptrC:\a\_work\1\s\include\onnxruntime\core/framework/data_types.honnxruntime::data_types_internal::MapTypeHelper::Setvalue_proto != nullptrexpected a registered ONNX typeonnxruntime::SequenceTensorTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetElementTypeonnxruntime::OptionalTypeBase::GetDeleteFunconnxruntime::SparseTensorTypeBase::GetElementTypeonnxruntime::TensorTypeBase::GetElementTypeonnxruntime::data_types_internal::OptionalTypeHelper::Setbfloat16float16uint64int64uint32int32uint16int16tensor type is not supported(unknown type)Float8E5M2FNUZFloat8E5M2Float8E4M3FNUZFloat8E4M3FNNot implementedonnxruntime::NonTensorTypeBase::IsSequenceCompatibleutils::HasKeyType(thisProto->map_type())onnxruntime::NonTensorTypeBase::IsMapCompatiblethisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasElemType(thisProto->optional_type())uint8int8doubleboolfloat(null)onnxruntime::NonTensorTypeBase::ToDataContaineronnxruntime::NonTensorTypeBase::FromDataContainerutils::HasElemType(thisProto->tensor_type())onnxruntime::TensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kTensorTypeWe do not expect duplicate registration of types for: onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataTypeproto != nullptrOnly ONNX MLDataType can be registeredC:\a\_work\1\s\onnxruntime\core\framework\data_types.cconnxruntime::OptionalTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kOptionalTypeutils::HasElemType(thisProto->sequence_type())onnxruntime::SequenceTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sparse_tensor_type())onnxruntime::SparseTensorTypeBase::IsCompatiblethisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeonnxruntime::data_types_internal::IsCompatibleonnxruntime::utils::ContainerChecker::ContainerCheckerInvalid DataTypeImpl TypeProto definitionMLDataType for: is not currently registered or supportedsparse tensor type rows: Expecting inner index size: the same as values size: Inner and Outer indices must either be both zero or non-zeroonnxruntime::SparseTensor::ValidateCsrIndicesdense shape must 2-D. Got: format_data_.size() == 2UExpecting two indices. Got: Format() == SparseFormat::kBlockSparseMust contain BlockSparse format. Got: onnxruntime::SparseTensor::MakeCsrStringsonnxruntime::SparseTensor::MakeCsrDataUse MakeCsrStringsonnxruntime::SparseTensor::UseCsrIndicesThis method does not expect allocator to be setOuter index count must be rows + 1 or zero. Got: onnxruntime::SparseTensor::MakeCooDataUse MakeCooStringsNot expecting an allocator setonnxruntime::SparseTensor::UseCooIndicesSparse format must not be set. Already contains format: onnxruntime::SparseTensor::GetCooIndexDimsvalues_count == index_sizeIndex size: onnxruntime::SparseTensor::AsCsrFormat() == SparseFormat::kCsrcMust contain Csr format. Contains: allocator_ != nullptrThis method should follow a call to constructor that supplies the allocatorFormat() == SparseFormat::kUndefinedonnxruntime::SparseTensor::MakeCooStringsExpecting data type to be set as string must be less than total buffer size: this tensor already has populated sparse_indicesonnxruntime::SparseTensor::GetSparseTensorFromOrtValuethe ort_value must contain a constructed sparse tensorC:\a\_work\1\s\onnxruntime\core\framework\sparse_tensor.cconnxruntime::`anonymous-namespace'::CopyDataMust have the same size. Got src_size: dst_size: must be equal to or twice the values size: format_data_.size() == 1UExpecting to contain one index, got: onnxruntime::SparseTensor::AsCooFormat() == SparseFormat::kCooMust contain Coo format. Got: onnxruntime::SparseTensor::AllocateBufferValues size Must have the same shapeSrc and Dst must be of the same typeX-device copy of strings not supportedDestination must have a CPU allocator setDestination should be emptyThis instance should not be emptyonnxruntime::SparseTensor::CopyExpecting index blocks: to be equal to values blocks: Indices shape must have dim[0] == 2Expecting indices to have 2-D shape . Got: onnxruntime::SparseTensor::ValidateBlockSparseShapesExpecting to have at lest 3-D shape. Got:Expecting one index. Got: onnxruntime::SparseTensor::AsBlockSparseUnable to find a data transfer for copying from device type: to device type: onnxruntime::SparseTensor::MakeBlockSparseStringsonnxruntime::SparseTensor::MakeBlockSparseDataUse MakeBlockSparseStringsonnxruntime::SparseTensor::UseBlockSparseIndicesExpecting fully sparse tensors to have indices shape {0}Expecting fully sparse tensors to have value shape {0}C:\a\_work\1\s\onnxruntime\core\framework\tensor_shape.cconnxruntime::TensorShape::SizeToDimensiondimension <= num_dimsInvalid dimension of for SizeToDimension. Tensor has dimensions.}{onnxruntime::TensorShape::Slicedimstart <= dimend && dimend <= values_.size()Invalid tensor shape slice argument.onnxruntime::TensorShape::SizeFromDimension for SizeFromDimension. Tensor has C:\a\_work\1\s\onnxruntime\core\framework\config_options.cconnxruntime::ConfigOptions::AddConfigEntryConfig with key [] already exists with value []. It will be overwrittenConfig value is longer than maximum length: Config key is empty or longer than maximum length 128init_num == values.size()Expecting same size spansAn OrtValue for this name has already been added: C:\a\_work\1\s\onnxruntime\core\framework\session_options.cconnxruntime::SessionOptions::AddInitializerBuffer containing the initializer must be owned by the user.Received OrtValue is not a tensor. Only tensors are supported.Received nullptr for OrtValueonnxruntime::SessionOptions::AddExternalInitializersReceived nullptr for nameRequested attribute: is expected to have type: No attribute with this name is defined.Attribute name and type don't matchAttribute: expected to be of type: but is of type: No attribute with name: is defined.node_offsets_index < node_offsets_size_onnxruntime::NodeIndexInfo::GetNodeOffsetC:\a\_work\1\s\onnxruntime\core/framework/node_index_info.harg_num < arg_counts.size()onnxruntime::OpKernelContext::NumVariadicInputsTempSpace allocator not foundCPU allocator not foundonnxruntime::OpKernelContext::GetOrCreateOutputMLValueframe != nullptronnxruntime::OpKernelContext::OpKernelContextC:\a\_work\1\s\onnxruntime\core\framework\op_kernel.ccOpKernel was nullkernel != nullptronnxruntime::OpKernelContext::OutputMLValue. Num args is Invalid arg_num of Execution frame was null type: */_ORT_MEM_ADDR_/*utils::HasExternalData(tensor_proto)onnxruntime::utils::GetExtDataFromTensorProtoGetFileLength for are out of bounds or can not be read in full. given file_length: size to read: offset: External initializer: onnxruntime::utils::UnpackTensorWithExternalDataImplExternal data type cannot be STRING.) in proto) does not match the data size(corrupted protobuf data: tensor shape size(UnpackTensor: the pre-allocate size does not match the size in protodata overflowonnxruntime::utils::GetFileContentTensorProto: onnxruntime::`anonymous-namespace'::ReadExternalDataForTensorstring tensor requires allocator to be provided., Got onnxruntime::`anonymous-namespace'::TensorProtoToOrtValueImplnullptr == p_dataUnpackTensor: the pre-allocated size does not match the raw data size, expected Tensor does not have external data to read from.onnxruntime::`anonymous-namespace'::GetExternalDataInfoC:\a\_work\1\s\onnxruntime\core\framework\tensorprotoutils.ccExternal data type cannot be UNDEFINED or STRING., external_data.length: external data size mismatch. Computed size: Invalid TensorProtoElement_size of: Unsupported sparse tensor data type of Must have a valid data typeHasDataType(dense_proto)onnxruntime::utils::DenseTensorToSparseTensorProto data_type: onnxruntime::utils::UnpackInitializerDataUnsupported type: Invalid SparseTensor indices. INT16 indices must be in the raw data of indices tensorInvalid SparseTensor indices. INT8 indices must be in the raw data of indices tensorInvalid SparseTensor indices. Should one of the following types: int8, int16, int32 or int64indices_shape[1] > 0 && static_cast(indices_shape[1]) == dims.size()cur_index == indices_data.end()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::utils::SparseTensorProtoToDenseTensorProto is not supported.Constant node: onnxruntime::utils::ConstantNodeProtoToTensorProto in 'Constant' node 'Unsupported attribute value type of Sparse Indices raw data size does not match expected.onnxruntime::utils::CopySparseDataSparse indices int64 data size does not match expectedSparse indices int32 data size does not match expectedTensorProtoToTensor() tensor shape mismatch! can not be written into Tensor type TensorProto type onnxruntime::utils::TensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: has no data attributesinput_offset >= 0 && output_offset >= 0onnxruntime::KernelDefBuilder::VariadicAliasC:\a\_work\1\s\onnxruntime\core\framework\kernel_def_builder.ccFailed to add kernel for and type ( domain (Op with name (Kernel not found However the types are incompatible.Op type (kernel def can't be NULL: Conflicting with a registered kernel with op versions. node_version: Version mismatch. However the types are incompatible. (node_version: in the supported version rangeKernel found kernel Encountered following errors: ( kernel is not supported in ), but the node in the model has the following type (onnxruntime::`anonymous-namespace'::MatchKernelDefTypesC:\a\_work\1\s\onnxruntime\core\framework\kernel_registry.ccstatic_cast(first_arg_idx) < actual_inputs.size()type_proto != nullptr kernel_end_version: kernel start version: This op has been implemented only for the following types (float8e4m3fnuzfloat8e5m2float8e5m2fnuz capable of executing this node because the CPU execution path is deemed faster than overhead involved with execution on other EPs ORT optimization- Force fallback to CPU execution for node: Candidate for fallback CPU execution: onnxruntime::GetCpuPreferredNodes::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\fallback_cpu_capability.cckernel_info != nullptronnxruntime::GetCpuPreferredNodesfloat8e4m3fnIExecutionProvider constructor must be called with true for use_metadef_id_creatorIExecutionProvider::Compile with FusedNodeAndGraph is not implemented by onnxruntime::IExecutionProvider::GenerateMetaDefIdmetadef_id_generator_C:\a\_work\1\s\onnxruntime\core\framework\execution_provider.ccC:\a\_work\1\s\onnxruntime\core\framework\allocator_utils.cconnxruntime::CreateAllocatorReceived invalid value of arena_extend_strategy onnxruntime::sparse_utils::DenseTensorToSparseCooInvalid index: onnxruntime::sparse_utils::SparseCsrToDenseTensorinner_num == src.Values().Shape().Size()outer_num == (rows + 1)Outer indices must be M + 1. Got: Expecting indices to be equal the number of values or be twice as manyInput must be of COO format > dense_size: onnxruntime::sparse_utils::SparseCooToDenseTensoronnxruntime::sparse_utils::DenseTensorToSparseCsrUnable to convert strings tensor to a sparse tensor that not on CPUUnsupported element size: C:\a\_work\1\s\onnxruntime\core\framework\sparse_utils.ccInput must be of CSR formatSupport 2-D matrices onlyExpecting inner indices to be same as nnz. Got: Unable to convert strings tensor to a sparse tensor that is not on CPUCurrently do not support dims higher than 2 dimensions: Tensor size mismatchdata_transfer registered is nullptr.There's no data transfer registered for copying tensors from to C:\a\_work\1\s\onnxruntime\core\framework\data_transfer_manager.cconnxruntime::DataTransferManager::CopyTensorsonnxruntime::DataTransferManager::CopySparseTensorsSNPEExecutionProvideronnxruntime::utils::CopyOneInputAcrossDevicesonnxruntime::utils::UpdateWithParentStreamonnxruntime::utils::ExecuteGraphImplonnxruntime::utils::CopyOutputsAcrossDevicesonnxruntime::utils::ExecuteGraphonnxruntime::utils::FinalizeCopyInfoForFetchesfetch_alloc_info.size() == copy_info.size()onnxruntime::utils::CopyInputsAcrossDevicescopy_info.size() == num_feeds, this is not supported yet.feed_streams.size() == num_feedsSubgraph has nodes running on device: while parent graph node running on device: onnxruntime::utils::FindDeviceForValueUnsupported OrtValue type to copy between device.onnxruntime::utils::CalculateStaticCopyInfoForFeedexec_plan_ptronnxruntime::utils::InitializeFeedFetchCopyInfoonnxruntime::utils::CalculateStaticCopyInfoForFeedsonnxruntime::utils::FinalizeCopyInfoForFeedsfeed_locations.size() == copy_info.size()invalid allocator.Failed to find allocator for device Unsupported OrtValue type.C:\a\_work\1\s\onnxruntime\core\framework\utils.cconnxruntime::utils::BatchOrCopyMLValueInternalTestingExecutionProviderkernel_type_str is null.kernel_type_str_args entry is null.args entry is null.args is null.Duplicate entry for op id: Duplicate entry for kernel type str: onnxruntime::OpSchemaKernelTypeStrResolver::ResolveKernelTypeStronnxruntime::KernelTypeStrResolver::RegisterNodeOpSchemaOp schema must be available.onnxruntime::KernelTypeStrResolver::SaveToOrtFormatonnxruntime::KernelTypeStrResolver::RegisterGraphNodeOpSchemasop_kernel_type_str_args entry is null.op_kernel_type_str_args is null.kernel_type_str_args is null.op_id is null.'. If type constraint names are available, Failed to find args for kernel type string '' for node type 'Kernel type string already exists for formal parameter name '', but the existing argument with that formal parameter name has a different formal parameter type string.onnxruntime::KernelTypeStrResolver::RegisterOpSchemaonnxruntime::KernelTypeStrResolver::RegisterOpSchema::::operator ()Invalid ORT format model.onnxruntime::KernelTypeStrResolver::ResolveKernelTypeStrFailed to find op_id: 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.C:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver.cc*2Failed to unload DSO: onnxruntime::ExLibLoader::~ExLibLoader has already been loaded.Caught exception while loading custom ops with message: onnxruntime::ExLibLoader::LoadExternalLibA dso with name onnxruntime::ExLibLoader::{dtor}::::operator ()Caught exception while destructing CustomOpsLoader with message: Unloading DSO C:\a\_work\1\s\onnxruntime\core\framework\ex_lib_loader.cc').onnxruntime::SessionState::CreateKernelsfor node '' ('onnxruntime::SessionState::PruneRemovableAttributesfailed at retrieving the removable attributes'), among attributes: SaveMLValueNameIndexMappingC:\a\_work\1\s\onnxruntime\core\framework\session_state.cconnxruntime::SessionState::CreateGraphInfoonnxruntime::SessionState::PopulateKernelCreateInfoDone saving OrtValue mappings.onnxruntime::SessionState::GetNodeKernelCreateInfoentry != kernel_create_info_map_.cend()session.disable_prepackingsession.node_partition_config_file' OpType: Index:entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'p_op_kernelonnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()onnxruntime::OuterScopeNodeArgLocationAccumulator::::operator ()subgraphs_kernel_create_info_maps.find(local_subgraph_kernel_create_info_map_key) == subgraphs_kernel_create_info_maps.end()onnxruntime::OuterScopeNodeArgLocationAccumulatoronnxruntime::SessionState::FinalizeSessionStateImpl::::operator ()onnxruntime::AccumulateAllNestedSubgraphsInfo Attribute:onnxruntime::SessionState::FinalizeSessionStateImplNode placementsonnxruntime::VerifyEachNodeIsAssignedToAnEp All nodes placed on []. Number of nodes: 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. Node(s) placed on [onnxruntime::SessionState::FinalizeSessionStateRerunning with verbose output on a non-minimal build will show node assignments.onnxruntime::SessionState::GetNodeIndexInfonode_index_info_.has_value()) node with name 'onnxruntime::SessionState::CreateSubgraphSessionStateonnxruntime::VerifyEachNodeIsAssignedToAnEpImplCould not find an implementation for onnxruntime::SessionState::AddOutputNameToNodeInfoMappingoutput_names_to_nodeinfo.empty() for attribute existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node SetGraphAndCreateKernels must be called prior to GetExecutionInfo.onnxruntime::SessionState::AddSubgraphSessionStateUnable to write the provided PrePackedWeights instance into the containerUsing cached version of pre-packed weight for constant initializer: ) and node (Using an input in multiple nodes on different devices is not supported currently. Input: is used by node Only one node should produce an output. Existing entry for Failed to find input name in the mapping: onnxruntime::SessionState::PrepackConstantInitializedTensors::::operator ()allocator_for_caching.get() != nullptrweights_to_be_filled_in.buffers_.size() > 0 doesn't have an implementation that can cache computed pre-packed weights!op_type.empty()The op type of a node cannot be empty used in the node: which is of op type: removed removable attributes duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function? doesn't have an implementation that can consume provided pre-packed weightsonnxruntime::KernelUseSharedPrePackedBuffers+The kernel corresponding to the node onnxruntime::DeviceStreamCollectionImpl::AddDeviceStreamonnxruntime::DeviceStreamCollectionImpl::SetDeviceStreamC:\a\_work\1\s\onnxruntime\core\framework\device_stream_collection.ccidx < num_streams_stream_idx < num_streams_onnxruntime::DeviceStreamCollectionImpl::GetStreamonnxruntime::DeviceStreamCollectionImpl::CleanUpbad variant accessfound duplicated provider '). in KernelRegistryManagerThe node is not placed on any Execution Provider. Failed to find kernel for ' ep:' (node:'C:\a\_work\1\s\onnxruntime\core\framework\mldata_type_utils.ccnullptr != type_protoonnxruntime::utils::GetMLDataTypeonnxruntime::kernel_type_str_resolver_utils::LoadKernelTypeStrResolverFromBufferonnxruntime::kernel_type_str_resolver_utils::AddLayoutTransformationRequiredOpsToKernelTypeStrResolverC:\a\_work\1\s\onnxruntime\core\framework\kernel_type_str_resolver_utils.ccFailed to verify KernelTypeStrResolver flatbuffers data.ktsrC:\a\_work\1\s\onnxruntime\core\framework\feeds_fetches_manager.ccError mapping feeds: onnxruntime::FeedsFetchesInfo::MapNamesToMLValueIdxsonnxruntime::FeedsFetchesManager::SetDeviceCopyChecksError mapping output names: input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown赎onnxruntime::FunctionKernel::Createonnxruntime::GraphPartitioner::InlineFunctionsAOT) pruned.onnxruntime::PartitionOrtFormatModelThis model does not have any local functions defined. AOT Inlining is not performedNo provider specified.onnxruntime::GraphPartitioner::Partition) functions of (AOT inlining completed. (_ctx.onnxFailed to generate EP context model since the file exist already.onnxruntime::InlineNodesonnxruntime::InlineFunctionsAOTImplonnxruntime::PartitionOrtFormatModelImplsingle_node_compute_func should have 1 element.onnxruntime::CreateEpContextModelonnxruntime::PartitionOnnxFormatModel was inserted using the NHWC format as requested by with domain: 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., but was not selectedThe Function Style fusion is deprecated. did not return correct number of compiled functionsNode 'onnxruntime::PartitionOnnxFormatModelImplep.context_file_pathep.context_enableonnxruntime::GetCapabilityForEPC:\a\_work\1\s\onnxruntime\core\framework\graph_partitioner.cc cannot be used with this model due to its ONNX opset not being supported by the layout transformer.API VERSION provider_type must be specified.C:\a\_work\1\s\onnxruntime\core/framework/func_kernel.hCreate state function failed. Return value:C:\a\_work\1\s\onnxruntime\core/framework/kernel_lookup.h!provider_type_.empty()onnxruntime::KernelLookup::KernelLookupC:\a\_work\1\s\onnxruntime\core\framework\bfc_arena.cconnxruntime::BFCArena::BFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size) == BinFromIndex(b)Creating bins of max chunk size arena_extend_strategy: onnxruntime::BFCArena::RegionManager::RegionForCould not find Region for onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: max_power_of_two_extend_bytes: memory limit: p_int >= base_intC:\a\_work\1\s\onnxruntime\core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion0 == memory_size % kMinAllocationSizeentry != regions_.end()Could not find Region for: p_int < base_int + memory_size_onnxruntime::BFCArena::AllocationRegion::IndexForBinFromIndex(c->bin_num)->free_chunks.erase(h) > 0Could not find chunk in binonnxruntime::BFCArena::RemoveFreeChunkFromBinonnxruntime::BFCArena::RemoveFreeChunkIterFromBinonnxruntime::BFCArena::Coalesce!c->in_use()onnxruntime::BFCArena::FreeAndMaybeCoalescec->in_use() && (c->bin_num == kInvalidBinNum)!c1->in_use() && !c2->in_use() && c1->stream == c2->streamonnxruntime::BFCArena::DeallocateRawInternalonnxruntime::BFCArena::Shrink BFC Arena shrunk by !c->in_use() && (c->bin_num != kInvalidBinNum)onnxruntime::BFCArena::InsertFreeChunkIntoBinc2->prev == h1onnxruntime::BFCArena::Mergeonnxruntime::BFCArena::FindChunkPtr!chunk->in_use() bytes. The total allocated bytes is now onnxruntime::BFCArena::SplitChunk!c->in_use() && (c->bin_num == kInvalidBinNum) (actual) rounded_bytes:onnxruntime::BFCArena::AllocateRawInternaltried to allocate 0 bytesFailed to find a free memory block despite calling Extend. rounded_bytes=Extending BFCArena for . bin_num: (requested) num_bytes: Reserving memory in BFCArena for size: h != kInvalidChunkHandlereserved_chunks_.find(ptr) == reserved_chunks_.end()onnxruntime::BFCArena::Reserve bytes.Failed to allocate memory for requested buffer of size Available memory of is smaller than requested bytes of Allocated memory at Total allocated bytes: onnxruntime::BFCArena::ExtendExtended allocation by onnxruntime::BFCArena::ChunkFromHandleh < chunks_.size()BinForSize(bin_size * 2) != BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)onnxruntime::BFCArena::Extend::::operator ()Incorrect arena extend strategy.hipMalloccudaMalloc. onnxruntime::NodeIndexInfo::GetMLValueIndexoffset >= 0 && static_cast(offset) < node_values_size_C:\a\_work\1\s\onnxruntime\core/framework/execution_frame.honnxruntime::IExecutionFrame::GetMLValueort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_onnxruntime::ExecutionFrame::TraceFreeort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()onnxruntime::ExecutionFrame::TraceAllocateTraceAllocation for ort_value_idx=Memory pattern planner is not enabled on this execution framework.TraceFree for ort_value_idx= for output Invalid allocation kind: Allocation of tensor types requires a shape.We don't expect custom allocators for non-tensor types, so a shape is mandatory here.onnxruntime::ExecutionFrame::ReleaseMLValueImplonnxruntime::ExecutionFrame::VerifyOutputSizesExpected shape from model of does not match actual shape of . 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.onnxruntime::ExecutionFrame::AllocateMLValueTensorPreAllocateBufferalloc && alloc.get() != nullptronnxruntime::ExecutionFrame::AllocateAsPerAllocationPlanort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()onnxruntime::ExecutionFrame::AllocateReusedOrtValueIfNotAllocatedHelperShape mismatch attempting to re-use buffer. but the actual size is: , fall back to default allocation behaviorTensor shape cannot contain any negative valueFailed to get allocator for onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: onnxruntime::ExecutionFrame::{ctor}::::operator ()Allocation of memory pattern buffer for failed. Error:Fetches vector passed to GetOutputs contains Trying to allocate memory for unused optional inputs/outputs returned nullptronnxruntime::ExecutionFrame::ExecutionFramebuffers_.find(location) == buffers_.end()Get feed size: but expected feed size: invalid index shape && sp_tensor.DenseShape() == *shape entries which doesn't match the number of fetches the frame was initialized with of fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()onnxruntime::IExecutionFrame::Initfeeds.size() == feed_mlvalue_idxs.size()onnxruntime::IExecutionFrame::IExecutionFramenode_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedC:\a\_work\1\s\onnxruntime\core\framework\execution_frame.cconnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValueshape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:onnxruntime::AsStreamBasedAllocatorallocator.get() != nullptrallocator is nullptrCould not find OrtValue with idx 'onnxruntime::NodeIndexInfo::Init::::operator ()onnxruntime::NodeIndexInfo::Init::::operator ()C:\a\_work\1\s\onnxruntime\core\framework\node_index_info.ccC:\a\_work\1\s\onnxruntime\core\framework\transpose_helper.cconnxruntime::TransposeSingleAxisOutwardsdim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensiononnxruntime::OrtValueTensorSlicer::Iterator::Iteratoronnxruntime::OrtValueTensorSlicer::Createonnxruntime::OrtValueTensorSlicer::Iterator::IteratorC:\a\_work\1\s\onnxruntime\core\framework\ort_value_tensor_slicer.cconnxruntime::OrtValueTensorSlicer::Createort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was Insufficient dimensions to slice on . Shape:ort_value.IsAllocated()OrtValue has not been allocated so can't be sliced.C:\a\_work\1\s\onnxruntime\core\framework\library_handles.cconnxruntime::LibraryHandles::UnloadLibrariesonnxruntime::utils::detail::CopyLittleEndiansource and destination buffer size mismatchC:\a\_work\1\s\onnxruntime\core\framework\endian_utils.ccparsing failedlocationmodel format error! Need a value for the external data infomodel format error! Missing 'location'model format error!checksumlengthmodel format error! Need a key for the external data info node. Name:'Non-zero status code returned while running onnxruntime::ExecuteThePlanAsync Kernel Support is not implemented yet.onnxruntime::ExecuteKernelC:\a\_work\1\s\onnxruntime\core\framework\sequential_executor.cc' Status Message: output_type_shapethread_scheduling_stats_kernel_time_fence_afteractivation_sizeparameter_sizeoutput_sizeinput_type_shapeop_name_fence_beforenode_index]}":[{"SequentialExecutor::ExecuteAll implicit inputs should have OrtValue instances by now. entry != nullptronnxruntime::OpKernelContextInternal::OpKernelContextInternalC:\a\_work\1\s\onnxruntime\core/framework/op_kernel_context_internal.h does not. : onnxruntime::DeviceBasedPartitioner::PartitionGraphPartitioner type is not DeviceBasedPartitioneronnxruntime::DeviceBasedPartitioner::InitializestreamsDeviceBasedPartitioner" in node-stream mapFailed to find node "iter != node_stream_map.end()onnxruntime::PlannerImpl::BuildExecutionPlanexecution_plan[node_stream_map_[node_index]]->device_.Type() == node_device_mem_location.Type()trigger_point_it != node_to_trigger_points.end()onnxruntime::PlannerImpl::CreatePlanonnxruntime::PlannerImpl::GenerateDeallocationPlan::::operator ()onnxruntime::PlannerImpl::GenerateDeallocationPlanonnxruntime::PlannerImpl::PartitionIntoStreamsonnxruntime::PlannerImpl::ComputeSingleStreamReusePlanonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStreamonnxruntime::PlannerImpl::ComputeReusePlanOnly tensors are supported for external outputs for now.!IsNonTensor(*node_output)onnxruntime::PlannerImpl::GeneratePlanForWeightsHelperonnxruntime::PlannerImpl::OptimizeReusePlanForMultiStream::::operator () as input reused by KernelDef is null for node: kernel_create_info.kernel_def != nullptronnxruntime::PlannerImpl::GetAliasMapspecific_subgraph_kernel_create_info_map != subgraphs_kernel_create_info_maps_.end()p_kernel_defonnxruntime::PlannerImpl::ComputeValueLocationCan not find the execution provider onnxruntime::PlannerImpl::GetLocationForNodeInputThere is no location for this node arg in the outer scope location mapfound_in_outer_scope_location_maponnxruntime::PlannerImpl::ComputeValueLocation::::operator ()Should not have entry in kernel create info with nullptr for kernel_defnullptr != tensor_type_baseonnxruntime::PlannerImpl::GetElementSizeCan not find the node onnxruntime::PlannerImpl::ComputeReuseCountid >= 0 && static_cast(id) < ort_value_info_.size()onnxruntime::PlannerImpl::ProcessDefreused != reused_foronnxruntime::PlannerImpl::Reuseonnxruntime::PlannerImpl::UseCountonnxruntime::PlannerImpl::Buffern >= 0 && static_cast(n) < plan_.allocation_plan.size()onnxruntime::PlannerImpl::AllocPlanonnxruntime::PlannerImpl::Index against size invalid value index: n >= 0 && static_cast(n) < ort_value_info_.size()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:entry != kernel_create_info_map.cend()onnxruntime::GetKernelCreateInfoC:\a\_work\1\s\onnxruntime\core\framework\allocation_planner.cccannot use operator[] with a string argument with cannot use operator[] with a numeric argument with invalid UTF-8 byte at index incomplete UTF-8 string; last byte: 0x�\u%04x: 0x{"bytes":[],"subtype":null}[ "bytes": [], "subtype": ": , ":[]{}{ cannot use insert() with onnxruntime::DeviceBasedPartitioner::SaveConfigCaught exception when reading partition config file: onnxruntime::IGraphPartitioner::CreateGraphPartitionerUse DeviceBasedPartition as defaultdevicesNumber of streams does not equal to number of device types!node_names_by_stream_.size() == device_types_.size()Caught exception during saving DeviceBasedPartitioner config: 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ƚpOܼw kAV<Ѝ,U1(\QFɦqaˋ#w"{mSx@I̮ Wζ]y<7VM6OH8oꖐ$:%˅t,φ4* 45<5*g8PD;?ȄkL'DŅT%Οk\b}$lۺd_ Xfl&ޓt |J|l_b%S04`?U&NZ~)p$wt帟ߦ}t_ϛpDk11eU%{?;+*\.Ӓsi$$Icd~̈Po ̼,eXѳ@ bx x9?{Η8$p\{2~S,h髤8m4E"&'O<'1cDȌ8eްLeǃTqB]\X,iM dpd'lJwmBtk}{x \|wyTwś[[=]S5Ȭ\*_F%94›\rξTSܷ1A"Lx\ӛ fS!{Z:0ܵ\S٨::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMapping::::operator ()onnxruntime::session_state_utils::SaveInputOutputNamesToNodeMappingSkipping entry for missing optional value at idx Using user supplied initializer with name ( failed.Deserialize tensor Can not trace string tensorentry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING bytes for [Memory] SessionStateInitializer statically allocates ort_value_name_idx_map.MaxIdx() > -1 from initializer_allocation_order not found among initialized tensorsOrtValue index: entry != initialized_tensors_to_allocate.end()onnxruntime::session_state_utils::SaveInitializedTensors::::operator ()Saving initialized tensors.onnxruntime::session_state_utils::SaveInitializedTensorsOrtValue indexes should have been populated.Failed to copy tensor to ) is different from what is supplied () because the ORT planned memory location device Cannot use user supplied initializer with name: (DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires onnxruntime::session_state_utils::DeserializeTensorProtostring tensor is not supported for copying between allocatorsFailed memory size calculationonnxruntime::session_state_utils::ExtDataTensorProtoToTensorC:\a\_work\1\s\onnxruntime\core\framework\session_state_utils.ccsession.use_device_allocator_for_initializersUnsupported device allocator in the context of pre-packed weights caching: onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocatorC:\a\_work\1\s\onnxruntime\core\framework\prepacked_weights_container.cconnxruntime::TensorAllocatorWithMemPattern::Trace' is not foundWeight buffer for initializer '' failedGet preallocated buffer for initializer 'C:\a\_work\1\s\onnxruntime\core\framework\tensor_allocator_with_mem_pattern.hInternal error. is not foundMem pattern for initializer Failed to get allocator for location: duplicated locationonnxruntime::TensorAllocatorWithMemPattern::FinalizePlanCan't use func with null ptr not found.onnxruntime::FuncManager::GetFuncsC:\a\_work\1\s\onnxruntime\core\framework\fuse_nodes_funcs.cc already exist.func info for node: !using_counters_C:\a\_work\1\s\onnxruntime\core/framework/mem_pattern_planner.honnxruntime::MemPatternPlanner::TraceAllocationcurrent <= buffer_size_Exiting due to terminate flag being set to true.onnxruntime::StreamExecutionContext::RecycleNodeInputsonnxruntime::RunSincesince == endonnxruntime::StreamExecutionContext::GetDeviceStreamidx < device_stream_map_->NumStreams()frame_.ReleaseMLValue(static_cast(execution_plan->release_actions[idx].value_index)).IsOK()C:\a\_work\1\s\onnxruntime\core\framework\stream_execution_context.ccC:\a\_work\1\s\onnxruntime\core/framework/stream_execution_context.honnxruntime::StreamExecutionContext::CountDownBarrier::Setv >= 0ActivateNotificationStep: activate notification with id: onnxruntime::ActivateNotificationStep::ExecuteTriggerDownstreamStep: trigger downstream of trigger point: WaitOnEPStep: wait on notification with id: stream activate notification with index Launch kernel with node id: onnxruntime::WaitOnEPStep::Executewait_handle_ wait on Notification with id: C:\a\_work\1\s\onnxruntime\core\framework\execution_steps.cc, count: WaitOnEPStep.wait_handle is nullSet a barrier with id: ConstantNoOpALLOW_RELEASED_ONNX_OPSET_ONLYC:\a\_work\1\s\onnxruntime\core/framework/tensorprotoutils.hTypeProto must have shape for this to runonnxruntime::utils::GetShapeshape != nullptrRemovable attributes were removed before the conversion is started.Optional Type mismatch. Expected: ] op_type [onnxruntime::Node::ToProtoType mismatch. Current= Input= . Got: onnxruntime::NodeArg::UpdateTypeAndShapeonnxruntime::MergeShapeInfoError merging shape info for output. 'Tensor element type mismatch. C:\a\_work\1\s\onnxruntime\core\graph\graph.cc. Falling back to lenient merge. , or sparse tensors' source: target:Output: , or optional typed entitiesSource and target must both be tensorsonnxruntime::Graph::BuildConnectionsArgument mismatch when removing edge.This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.onnxruntime::Graph::RemoveEdgeInvalid node indexes specified when removing edge.Invalid destination node arg slot specified when removing edge.Invalid source node arg slot specified when removing edge.Invalid source node arg slot specified when adding edge.onnxruntime::Graph::AddEdgeArgument type mismatch when adding edge.Invalid destination node arg slot specified when adding edge.This is an invalid model. Error: two nodes with same node name (This is an invalid model. Subgraph output (Invalid node indexes specified when adding edge.This is an invalid model. Error: Duplicate definition of name () does not exist in the graph.node_arg) is an outer scope value being returned directly. Please update the model to add an Identity node between the outer scope value and the subgraph output.This is an invalid model. Graph output (graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()Graph state to be loaded into must be empty.Graph ctor should have created NodeArg for initializer. Missing:onnxruntime::Graph::InitializeStateFromModelFileGraphProtoThis may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, Initializer appears in graph inputs and will not be treated as constant value/weight. This is an invalid model. Tensor does not have type information.Duplicate initializer (dense, sparse or ConstantNode): 'by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.Duplicate sparse_tensor_initializer: 'utils::HasName(sparse_tensor)' the model will use the latest encountered initializer. Please, fix your model.' Model is invalid.onnxruntime::Graph::GraphSparse initializer must have a name. This model is invalidDuplicate constant node sparse initializer name: 'This is an invalid model. The sum of input arg count is not equal to size of input defs in node (output edgesgraph_proto != nullptrgraph_proto cannot be nullinput index: is not the same as this node's index:input edgesonnxruntime::Node::LoadEdgesFromOrtFormatNode::LoadFromOrtFormat, input_arg_counts is missingSerialization error. Graph attribute was serialized without Graph instanceonnxruntime::Node::LoadEdgesFromOrtFormat::::operator ()Node::LoadEdgesFromOrtFormat, edge is missing for LoadNodeArgsFromOrtFormat: Node [], could not find NodeArg fbs_attr cannot be nullonnxruntime::Node::LoadFromOrtFormatfbs_node_arg_names cannot be nullonnxruntime::Node::SaveToOrtFormatnode_arg_name cannot be nullonnxruntime::Node::LoadFromOrtFormat::::operator ()Serialization of fused function body is not currently supported, Node [does not have the graph for key Removable attributes were removed before the node is saved.onnxruntime::Graph::Resolveonnxruntime::Graph::ForThisAndAllSubgraphsouter_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.Error: Duplicate definition-site for (onnxruntime::Graph::PerformTypeAndShapeInferencingonnxruntime::Graph::InitInputsInitializersOutputsFatal error: ) is not a registered function/op) attribute () is required but not specified., Error Type Error: Shape of initializer onnxruntime::Graph::VerifyNodeAndOpMatchThis is an invalid model. In Node, ' has element type but usage of initializer in graph expects does not match. Type Error: Data in initializer 'Type Error: Type () of output arg (This is an invalid model. Model input () does not have type information.) output arg () type inference failed, inferred types: ) of node () does not match expected type (Type Error: Type parameter () of Optype (index=onnxruntime::Graph::InferAndVerifyTypeMatchThis is an invalid model. Type Error: Type '' of input parameter () bound to different types ( in node () is invalid.This is an invalid model. Node () of operator () in node (Node () Op () input arg () does not have type information set by parent node.node.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs) onnxruntime::Graph::UpdateShapeInferenceNode:Size mismatch validating subgraph inputs. Got onnxruntime::Graph::InferAndVerifySubgraphTypesSubgraph input missing type. inputs. Either provide all subgraph inputs, or just the required inputs.No Graph instance was found for attribute inputs but subgraph has inputs and requires This is an invalid model. Error: the graph is not acyclic.onnxruntime::Graph::KahnsTopologicalSort in node Graph attribute inferencing failed: ' is not a graph input, initializer, or output of a previous node.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Some nodes are not included in the topological sort, graph have a cycle.Invalid model. Node input ' conflicts with graph initializer. Check name generation above._if_Initializer name: from graph: ' Unable to fetch: insert_result.secondonnxruntime::Graph::InlineIfSubgraphUnable to constant fold If node: 'onnxruntime::Graph::AddConstantProtoAsInitializerdst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Constant node name: conflicts with graph initializer. Check that the node names have been made unique.Schema was not found for fused node. Domain: OpType:onnxruntime::Graph::FinalizeFuseSubGraphSetOpSchemaFromRegistryForNode(fused_node)nullptr != func_meta_defonnxruntime::Graph::AllocateNode SinceVersion:onnxruntime::Graph::CreateFusedSubGraphNodeRemoving NodeArg 'outer_scope_node_arg != nullptrnodes_.size() < static_cast(std::numeric_limits::max()) must be either specified in graph inputs or graph initializers.'. It is not used by any node and should be removed from the model.onnxruntime::Graph::CleanUnusedInitializersAndNodeArgs'. It is no longer used by any node.Removing initializer 'onnxruntime::Graph::ToGraphProtoInternalOuter scope node arg name 'initializer_node_arg != nullptrCannot find NodeArgs for [external_stream.is_open()onnxruntime::Graph::ToGraphProto'was added but does not exist. onnxruntime::Graph::ToGraphProtoWithExternalInitializersnode->GetOutputEdgesCount() == 0Can't remove node Failed to convert dense initializer to sparseonnxruntime::Graph::RemoveNodeonnxruntime::Graph::SaveToOrtFormat as it still has output edges._token_Replaced external initializer: onnxruntime::Graph::InjectExternalInitializedTensorsReplacement tensor's data type does not match.Replacement tensor's dimensions do not match.existing_entry != mutable_initializers.pointer_end()graph_proto_ is not in sync with name_to_initial_tensor_Failed to find existing initializer with name !foundTrying to replace non-external initializer with external dataonnxruntime::Graph::ReplaceInitializedTensorImplsparse_tensor_names_.count(tensor_name) == 0sparse_tensor_names_ not in sync with name_to_initial_tensor_graph_proto_ is not in sync with name_to_initial_tensor_.onnxruntime::Graph::RemoveInitializedTensorAddInitializedTensor already has tensor with name but different TensorProto.onnxruntime::Graph::AddInitializedTensorexisting->second == &tensoronnxruntime::ConstPointerContainer > >::atindex < data_.size()C:\a\_work\1\s\include\onnxruntime\core/common/const_pointer_container.hNodeEdge is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Sparse Initializer tensor is missing. Invalid ORT format model.Node index is out of rangeNode is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat::::operator ()NodeArg Name is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Initializer tensor is missing. Invalid ORT format model.onnxruntime::Graph::LoadFromOrtFormat""(") -> (input->Exists()Input to set must exist.) : (onnxruntime::Graph::SetInputsonnxruntime::Graph::InlineFunctioncallnode.TryGetFunctionProto(inlined_fp) in inlined subgraph: conflicts with graph initializer. Check Specializing code.onnxruntime::Graph::InlineFunctionProtoConstant folded (inlined) Node has no function body and cannot be inlined._inlfunc_ input: is not If node's input or previous node output in this subgraph for If node: Node's: absl::container_internal::raw_hash_map<>::at, but it its version is higherknown by the checker.onnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSchemaInternalthan the operator set version , but it its domain is notDomain already set in registryC:\a\_work\1\s\onnxruntime\core\graph\schema_registry.cconnxruntime::OnnxRuntimeOpSchemaRegistry::RegisterOpSetonnxruntime::GraphViewerToProtoC:\a\_work\1\s\onnxruntime\core\graph\graph_proto_serializer.cconnxruntime::GraphViewer::GetNodesInTopologicalOrdernodeInvalid ExecutionOrderMismatch between Graph and IndexedSubGraph. Output not found:Mismatch between Graph and IndexedSubGraph. Node not found: Mismatch between Graph and IndexedSubGraph. Input not found:nodeargonnxruntime::GraphViewer::GraphViewerC:\a\_work\1\s\onnxruntime\core\graph\graph_viewer.ccIndexedSubGraph contains values not present in the Graphgraph_->GetNode(idx) != nullptr쏀( model may run depending upon legacy support of some older opset version operators.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 , max supported IR version: Unsupported model IR version: Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Missing model IR version.onnxruntime::Model::ModelC:\a\_work\1\s\onnxruntime\core\graph\model.ccModelProto does not have a graph.onnxruntime::model_load_utils::ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain 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 ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset C:\a\_work\1\s\onnxruntime\core/graph/model_load_utils.h is till opset are '0' and '1'. The environment variable contained the value: onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySetsystem error number failed. File doesn't existLoad model onnxruntime::SaveModelWithExternalInitializersonnxruntime::SaveModelonnxruntime::Model::LoadFromOrtFormatGraph is null. Invalid ORT format model.onnxruntime::Model::SaveToOrtFormatNull entry in metadata_props. Invalid ORT format model. is less than 0.onnxruntime::Model::SaveWithExternalInitializersonnxruntime::Model::SaveProtobuf serialization failed. less than 0. is less than 0.Protobuf parsing failed.onnxruntime::Model::LoadFailed to load model with error: No graph was found in the protobuf.Output tensor must have at least 3 dimensionsTensor must have at least 3 dimensions to convert between channels first and channels last. should specify a shapeNegative values are not allowed in a shape specificationAttribute Bias applied to each channel, same size as C.The scale to apply.Constrain input and output types to float tensors.C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\onnx_deprecated_operators.ccValue of alphaValue of beta) + scale[1] () needs to be greater than or equal to the left_border () + scale[0] () needs to be greater than or equal to the top_border () + right_border ('Scale' must contain exactly 2 values - (height, width)) + bottom_border (Input's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)Scaling valueIf 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.GRUUnithiddenseq_lengthsgateshidden_prevdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.GivenTensorFillvaluesextra_shapeConstrain indices to integer typesinput_as_shapeConstrain input and output types to all tensor types.dataThreshold valueA 1-D values of (height, width).A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).C should be of integer type and specify a type. does not specify a valid type. not specifiedValue of attribute is null expected to have tensor or sparse tensor type: Attribute expected to have tensor or sparse tensor type expected to have rank > expected to have rank Dimension mismatch in unification between but has rank Incompatible dimensionsError unexpected extra input in node: = Constant()Error parsing node:Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.TorchEmbeddingConstrain input and output types to all numeric tensors.scale_grad_by_freqpadding_idxweight != mat_h:The inner-most 2 dimensions must have the same size (mat_w:Alpha value.Input rank must be >= 2.Alpha CX = Mul (Alpha, X) SIGMOIDCX = Sigmoid (CX) Y = Mul (X, SIGMOIDCX) C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\contrib_defs.cc CX = Mul (C, X) ERFCX = Erf (CX) ERFCXPlus1 = Add (ERFCX, One) PhiX = Mul (ERFCXPlus1, Half) Y = Mul (X, PhiX) OneFailed to parse num_return_sequences or it is not positive integer scalarFailed to parse max_length or it is not positive integer scalarFailed to parse num_beams or it is not positive integer scalarInputs 0 shall be 3 dimensions in whisper graphInputs 0 shall be 2 dimensionsInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplication'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesAxFThe id of the token that indicates decoding starts.no repeat ngrams sizeThe id of the end-of-sequence tokenThe id of the padding tokenDynamicTimeWarpingConstrain to float tensors.Constrain to integer types.UnfoldTensorAllow inputs and outputs to be any kind of tensor.specify the step.specify the dimension to unfoldspecify the sizesize attribute in UnfoldTensor not positive and less than the dim size!size attribute in UnfoldTensor must greater than 0.size attribute in UnfoldTensor not specified!dimstepConstrain input types to all tensor types.Constrain output types to float tensors.GridSupport padding modes for outside grid values: `zeros`(default), `border`, `reflection`. zeros: use 0 for out-of-bound grid locations, border: use border values for out-of-bound grid locations, reflection: use values at locations reflected by the border for out-of-bound grid locations.If align_corners=1, the extrema (-1 and 1) are considered as referring to the center points of the input's corner pixels. If align_corners=0, they are instead considered as referring to the corner points of the input's corner pixels, making the sampling more resolution agnostic.Three interpolation modes: bilinear (default), nearest and bicubic.If true, check only for NaN.Constrain the output to a boolean tensor.IsAllFiniteIf true, check only for Inf, -Inf.isnan_onlyBoth attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions.Constrain output 'mask' types to uint32 tensors.isinf_onlyConstrain input 'training_mode' types to boolean tensors.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.maskratioresidual(Optional) Seed to the random generator, if not specified we will auto generate one.true if broadcast bias across input for dimensions broadcast_axis to axis-1, otherwise broadcast bias across input for dimensions 0 to broadcast_axis - 1apply softmax to elements for dimensions axis or higherConstrain input and output types to all numeric tensors and bool tensors.Presence penalty for custom samplingIf 1 custom sampling logicAll filtered values will be set to this float value.Minimumber of tokens we keep per batch example in the output.The value used to module the next token probabilities.If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation.The subgraph for initialization of encoder and decoder. It will be called once before `decoder` subgraph.model type: 0 for decoder only like GPT-2; 1 for encoder decoder like Bartnon_speech_probsConstrain cross_qk to float32 tensors.cross_qkextra_decoding_idscross_qk_layer_headIf nozero, decoder subgraph contains output Q*K from cross attentions. Default 0.The id of the first timestampMust be 2 for whisperThe token in whisper model that marks all sequence empty. With this model, whisper could output no_speech_prob after. Default -1.The id of the token that indicates no timestampsThe id of the transcribe taskThe id of the token that indicates LM startsThe id of the token that indicates decoding starts (i.e. the start of transcription token id)The id of the translate taskConstrain mask to integer typesConstrain input type to float or int tensors.Constrain to integer typesscoressequences_scoressequenceslogits_processorprefix_vocab_maskvocab_maskrepetition_penaltyDecoder subgraph to execute in a loop.Size of the vocabulary. If not provided, it will be inferred from the decoder subgraph's output shapeThe subgraph for initialization of encoder and decoder. It will be called once before decoder subgraph.The subgraph for the first decoding run. It will be called once before `decoder` subgraph. This is relevant only for the GPT2 model. If this attribute is missing, the `decoder` subgraph will be used for all decoding runsearly stop or notmodel type: 0 for GPT-2; 1 for encoder decoder like T5Scalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.Whether A should be transposedWhether B should be transposedConstrain input and output types to float/int tensors.First input does not have rank 2Second input does not have rank 2ZPadsConstrain input and output types to float tensorsComplexMulConjWComplexMulIrfftnumber of dimensions comprising the signalRfftConstrain input and output types to float or half tensors.normalizedmust be 1, only one sided FFTs supportedsignal_ndimmust be 0, normalization currently not supportednumber of dimensions comprising the signal, collected in reverse order (e.g. 1 = last dimension is the signal)Constrain input0 and output types to float tensorsMoEConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.fc2_experts_biasConstrain input and output types to float or float16 tensors.fc1_experts_biasfc2_experts_weightsfc1_experts_weightsrouter_probsactivation_typeNumber of top experts to select from expert poolActivation function to use. Choose from relu, gelu, silu and identity. Default is relufiltered_logitspresence_maskModel type: 0 for decoder only like GPT-2; 1 for encoder decoder like BartConstrain input and output types to any tensor type.Constrain indice type to int32 or int64last dimension of indices must not be larger and rank of data tensorIf value is 1, output type is uint32_t, else int32_t. Default value is 1.both data and indices tensor need to have rank larger than zero.Constrain output type to unsigned and signed 32-bit integer tensor.Seed for the hashing algorithm, unsigned 32-bit integer, default to 0.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.sparse_tensor(uint64)sparse_tensor(uint32)sparse_tensor(int64)sparse_tensor(int32)sparse_tensor(float)sparse_tensor(double)Whether B should be transposed on the 1st dimension and batch dimensions (dim-1 to dim-rank-2) before doing multiplicationactivation_axisWhether A 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 last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationScalar multiplier for the product of the input tensors.Constrain input B data types as data blobConstrain shape of B must be int64 tensor.B_shapeConstrain input matrix data types as single precision float tensorQuantization type4b quantization not yet supported on this hardware platform!Input q4 tensors of wrong size!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).B input for MatMul must be a 2-D matrix!Constrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredinputs are expected to have tensor type and output type should not be null.An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.Input/Output is a string tensoractivation_gammaInput axis is invalid: scaleYTRscaleBscaleATSActivation function, RELU or GELU or NONE (default).Scalar multiplier for the product of input bias C.Output Type. Same definition as attribute 'to' for operator Cast.Whether A should be transposed. Float 8 only supprted transA=0.Whether B should be transposed. Float 8 only supprted transB=1.Constrain types to float tensors.Constrain types to int tensors.crop_sizebatch_indicesroisThe pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. batch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionfirst input tensor has wrong dimensionrois input tensor has wrong dimensionConstrains input to only numeric types.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".countsInput can be of any tensor type.idx'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayConstrain to tensor(float).Constrain to tensor(int32).SequenceThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weightinv_std_varType of Mean and InvStdDev tensors.Constrain output Y, scale and bias type to float tensors.InvStdDevConstrain input X type to float tensors.The epsilon value to use to avoid division by zero.type used for stash mean/inv_std_varThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).Input 0 shall be 3 dimensionsAttribute 'pooled_size' must be >= 1.pooled_sizemax_output_boxesAttribute 'max_output_boxes' must be >= 1.InvStdDev = Reshape (InvStdDev2D, ReducedShape)InvStdDev2D = Reciprocal (StdDev)Mean = Reshape (Mean2D, ReducedShape)Biased = Identity (Scaled)Y = Reshape (Biased, XShape)B2D = Flatten (B)Biased = Add (Scaled, B2D)Scale2D = Flatten (Scale)Scaled = Mul (NormalizedV, Scale2D)Normalized = Div (Deviation, StdDev)NormalizedV = Cast (Normalized)StdDev = Sqrt (VarPlusEpsilon)Deviation = Sub (XU, Mean2D)Var = Sub (MeanOfSquare, SquareOfMean)VarPlusEpsilon = Add (Var, Epsilon)MeanOfSquare = ReduceMean (Square)SquareOfMean = Mul (Mean2D, Mean2D)Mean2D = ReduceMean (XU)Square = Mul (XU, XU)X2D = Flatten (X)XU = Cast (X2D)SuffixShape = ConstantOfShape (NumReducedAxes)ReducedShape = Concat (PrefixShape, SuffixShape)NumReducedAxes = Sub (Rank, Axis1D)NumReducedAxes = Neg (Axis1D)Axis1D = Constant()PrefixShape = Slice (XShape, Zero1D, Axis1D)Rank = Size (XShape)Zero1D = Constant()EpsilonXShape = Shape (X)Constrain type for all input scales (scaleA, scaleB, scaleY).GemmFloat8Constrain type to input C.Constrain type to result type.Constrain type to input A.Constrain type to input B.snpe_version(Optional) Target device like CPU, DSP, etc.DLC(Optional) SNPE version used to convert the model.payload of the SNPE DLC file.factorspanScaling 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.disentangled_attentionMaximum relative distance, k.p2c_attentionc2p_attentionc2c_attentionDisentangledAttention_TRTPooled size.PyramidROIAlign_TRTImage size.image_sizepatchesfeature_map_3feature_map_2feature_map_1feature_map_0score_thresholdMultilevelCropAndResize_TRTscore_activationScore threshold value.plugin_versionActivation function to apply to the scores input.Max detections to output.Version number of the TRT plugin.Box IOU threshold value.iou_thresholdEncoding type for the boxes or anchors inputs.box_codingBackground class ID.background_classdetection_classesdetection_scoresdetection_boxesnum_detectionsanchorsboxesEfficientNMS_TRTConstrain mean and inv_std_var to be float tensors.Constrain output Y and scale type to float tensors.absmaxConstrain input and output types to float/half_float/brain_float tensors.Indicate if the ops run in training_mode, by default, False.Whether B should be transposed on the last two dimensions before doing multiplication. Default to be 1.number of groupsize used for weight quantization. It needs to be a power of 2 and not smaller than 16.quantization data type. 0 for FP4, 1 for NF4.Constrain input and output types to float/half_float tensors.Constrain quantized weight types to uint8.zero_pointsThe 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.number of bits used for weight quantization (default 4)number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.size of each input featuresize of each output featureConstrain 'training_mode' to boolean tensor.Constrain output 'mask' types to bit-packed uint32 tensor.Constrain input and output types.(Optional) the source used to generate the engine/context cache file. Ort EP or native SDK tool chainsource(Optional) partitioned graph name.partition_name(Optional) Hardware architecture.hardware_architecture(Optional) SDK version used to convert the model.ep_sdk_version1: 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.embed_modepayload of the execution provider context if embed_mode=1, or path to the context file if embed_mode=0.ep_cache_contextUsually each single EPContext associate with a graph partition.But for some case like QNN, it has single EPContext contains all partitions.In that case, the node with ep_cache_context should set main_context=1. Other nodes set main_context=0 and skip ep_cache_context.The path is relative to this Onnx file. Default is 1.main_contextEPContextConstrain input and output types to uint8, uint16, float tensors.notestarget_device(Optional) Some notes for the modelrelative_position_biaspastCustom scale will be used if specified. Default value is 1/sqrt(head_size)Dimension of rotary embedding. Limited to 32, 64 or 128. Default value is head_sizeThe value to be filled in the attention mask. Default value is -10000.0fCorresponding past and present are same tensor, its size is (2, batch_size, num_heads, max_sequence_length, head_size)Whether to use rotary position embedding. Default value is 0.Whether every token can only attend to previous tokens. Default value is 0.Hidden dimension of Q, K, V: hidden_size, hidden_size and v_hidden_sizeNumber of attention headsInputs 0 (query) shall be 3 dimensionsInputs 2 (value) shall be 3 dimensionsInputs 2 (value) shall be 3 or 4 dimensionsThe past_key input shall be 4 dimensionstoken_offset shall be 2 dimensionsInputs 0 (query) shall be 3 or 5 dimensionsinput shall be 3 dimensionsinput shall be 2 dimensionskey and value cache shall be 4 dimensionskey_padding_maskqkConstrain qk output types to float32 tensors.present_valuepresent_keypast_valuepast_keyNeed output the cross attention MatMul(Q, K)output_qkCorresponding past and present are same tensor, its size is (batch_size, num_heads, max_sequence_length, head_size)cache_indirectionPackedMultiHeadAttentionConstrain input and output to float tensors.Constrain mask, offset and sequence length to integer typeskeyqueryInputs 2 (value) shall be 2 dimensionsInputs 0 (query) shall be 2 or 4 dimensionsPackedAttentioncumulative_sequence_lengthtoken_offsetInvalid bias shapeqkv_hidden_sizes should have 3 elementsC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\bert_defs.ccpresentConstrain mask index to integer typeshas_key_padding_maskhas_layer_stateuse_paststatic_kvkey_cachevalue_cachekv_weightq_weightLongformerAttentionglobalglobal_biasGglobal_weightwindowOne sided attention windows length W, or half of total window lengthConstrain mask to int tensor.GroupQueryAttentionsin_cachecos_cachetotal_sequence_lengthseqlens_krotary_interleavedlocal_window_sizeRotate using interleaved pattern. Default value is 0 (False).kv_num_headsleft_window_size for local attention (like Mistral). Default value is -1 meaning unused.Number of attention heads for qNumber of attention heads for k and vskipRelativePositionBiasConstrain sequence_length to int tensors.key_lengthquery_lengthbias_tableDefault value is 0.is_bidirectionalMax distancemax_distanceX_bias = Identity (X) T1 = Mul (X_bias, X_bias) T2 = Mul (c, T1) T3 = Add (b, T2) T4 = Mul (X_bias, T3) T5 = Tanh (T4) T6 = Add (one, T5) T7 = Mul (X_bias, T6) Y = Mul (a, T7) oneX_bias = Add (X, bias)baConstrain input and output integer tensors typesConstrain input and output float tensors types.embedding_sumsegment_embeddingposition_embeddingword_embeddingsegment_idsmask_index_typeThe mask index tensor type for shape inference (0: None, 1: 1D mask_index)Constrain input and output types to integer tensorsNumber of attention heads. Default value is 0. Must use with rotary_embedding_dimCustom scale will be used if specified. Default value is 1.0Rotary embedding dimension. Default value is 0.DecoderAttentionConstrain input and output types to float and float16 tensors.Constrain key_padding_mask to bool tensors.new_key_cachenew_value_cacheGatedRelativePositionBiaseco_arel_posquery_biasquery_layerRestorePaddingConstrain token_offset to integer typesRemovePaddingmax_seq_lenConstrain sequence_token_count and token_offset to integer typescumulated_seq_lensequence_token_countGemmFastGelusuffix_match_idxtokenspred_tokensprev_suffix_match_idxcur_tokenssrc_tokensThe maximum NGram size for suffix matching.The minimum NGram size for suffix matching.scores_outConstrain scores input and output types to float tensors.The NGram size.Constrain mean and inv_std_var to float tensors.input_skip_bias_sum̼+̼+The epsilon value to use to avoid division by zeroBiasAddBiasSplitGelubias shall be 1 dimensionConstrain input X, skip, bias and output Y, S types to float tensors.SkipGroupNormGroupNormC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\diffusion_defs.ccConstrain input X and output Y types to float tensors.Constrain gamma and beta to float tensors.Activation after group normalization: 0 for None, 1 for SiLU1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.The number of groups of channels. It should be a divisor of the number of channels Cgroups Dimension=Can't merge shape info. Both inferred and declared dimension have values but they differ. Inferred= expected to have: or UNDEFINED. Got: QuantizeBFPConstrain y to uint8.Constrain shape and strides to uint64.Constrain the input to float and bfloat.block_dimbfp_typeEach 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.The type of BFP - must match with the BFPType enumConstrain 'x' and 'x_zero_point' to 8-bit integer tensors, 16-bit integer tensors, or 32-bit signed integer tensors.Constrain 'y', 'x_scale' to float tensors.x_zero_pointx_scaleC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\quantization_defs.ccConstrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit and 16-bit integer tensors.y_zero_pointy_scaleThe 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 input and output types to 8 bit signed and unsigned tensors.C_zero_pointC_scaleB_zero_pointB_scaleA_zero_pointA_scale{name}{additionalDocumentation}inputs are expected to have tensor type. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} Scale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarInput data type does not match the expected data type , 'axis' must be in []. Its actual value is: Y_zero_pointConstrain input and output types to 8 bit tensors.Y_scaleX_zero_pointX_scaleCoefficient of leakage.QLinearReduceMeanreduced_scalereduced_zero_pointdata_zero_pointdata_scaleaxis must be in [-rank, rank-1]. input rank was C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointmultiplicationC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointadditionConstrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.a_zero_pointa_scaleConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.b_zero_pointb_scaleMulIntegerConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.Keep the reduced dimension or not, default 1 mean keep reduced dimension.ReduceSumIntegerConstrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.reducedConstrain input type to 8-bit integer tensor.DequantizeBFPConstrain the input to uint8.Constrain y to float and bfloat16.Shape of quantized tensor must be 1D.The datatype to dequantize to.z_zero_pointz_scaleconditionConstrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8).TVT8Which axis to concat onaxis must be in [-rank, rank)All inputs to Concat must have same rankConstrain weights types to 8 bit tensors.Required attribute axis is missingY_cConstrain seq_lens to integer tensor.Y_hPinitial_cinitial_hsequence_lensRCouple the input and forget gates if 1.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.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.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerConstrain input and output types to signed/unsigned int8 tensors.opset version of corresponding SoftMax.apply softmax to elements for dimensions axis,or all dims along with axis according to op-versioncublasLt order of output matrix.order_outputcublasLt order of input matrix. ORDER_COL = 0, ORDER_ROW = 1, ORDER_COL32 = 2, ORDER_COL4_4R2_8C = 3, ORDER_COL32_2R_4R4 = 4. Please refer https://docs.nvidia.com/cuda/cublas/index.html#cublasLtOrder_t for their meaning.order_inputConstrain input and output types to float32 tensors.mask_index_outlayernorm_outbeta_zero_pointgamma_zero_pointsegment_embedding_zero_pointposition_embedding_zero_pointword_embedding_zero_pointbeta_scalegamma_scalesegment_embedding_scaleposition_embedding_scaleword_embedding_scalebeta_quantposition_embedding_quantgamma_quantword_embedding_quantConstrain input and output types to int8 tensors.weight_zero_pointinput_zero_pointweight_scaleinput_scaleCorresponding past and present are same tensor, its shape is (2, batch_size, num_heads, max_sequence_length, head_size)Constrain output zero point types to 8 bit tensors.Constrain output type to float32 or 8 bit tensors.Constrain input B and its zero point types to 8 bit tensors.Constrain input C to 32 bit integer tensors.Constrain scale types to float tensors.Constrain input A and its zero point types to 8 bit tensors.QOrderedLayerNormalizationcublasLt order of input X. Default is ROW MAJOR. See the schema of QuantizeWithOrder for order definition.Constrain scales to float32 tensors.QOrderedAttentionscale_values_gemmscale_QKT_softmaxV_biasscale_QKT_gemmQ_biasK_biasscale_K_weightscale_V_weightscale_Q_weightK_weightV_weightQ_weightscale_K_gemmscale_V_gemmscale_Q_gemmcublasLt order of global biascublasLt order of weight matrixorder_weightHidden layer sizes of Q, K, V paths in AttentionConstrain bias and scales to float32QOrderedMatMulscale_Cscale_Yscale_Bscale_Aorder_Yorder_BcublasLt order of matrix Y and optional matrix Corder_AcublasLt order of matrix BcublasLt order of matrix A. See the schema of QuantizeWithOrder for order definition.The output data type, only support TensorProto_DataType_FLOAT (1) and TensorProto_DataType_FLOAT16 (10)DequantizeWithOrdercublasLt order of input matrix. See the schema of QuantizeWithOrder for order definition.cublasLt order of output matrixQuantizeWithOrderConstrain to float typesConstrain Scale to float32 typesscale_inputQnumber of groups input channels and output channels are divided into.QLinearConvTransposeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSETPadding for the beginning and ending along each spatial axisdilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input W.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.w_zero_pointw_scalewinput and zero_point pair is expected to have be same type.weight and zero_point pair is expected to have same type.Be compatible with float version.QOrderedLongformerAttentionscale_outputscale_global_gemmscale_global_weightscale_qkv_gemmscale_biasscale_weightorder_global_weightQOrderedGeluConstrain scales to float32cublasLt order of matrix Y, must be same as order_X if specified together. Optional.cublasLt order of input X. Optional. See the schema of QuantizeWithOrder for order definition.quantization scale must be float tensors.quantization tensor must be int8 tensors.Constrain input gamma and bias could be float16/float tensors. float may get better precision, float16 runs faster.scale_Xorder_XcublasLt order of matrix Y, must be same as order_X. Default is ROW MAJOR.̼+Stride along each spatial axis. If not present, the stride defaults is 1 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.Whether to use ceil or floor (default) to compute the output shape.Works on NHWC layout or not? Default not.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.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.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.C:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nhwc_schema_defs.cconnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutputstd::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })Node must only have one used outputonnxruntime::graph_utils::CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraphstd::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0C:\a\_work\1\s\onnxruntime\core\graph\graph_utils.cconnxruntime::graph_utils::GetIndexFromNameitr != node_args.end()Attempting to get index by a name which does not exist:for node: onnxruntime::graph_utils::FindPathFailed since multiple edges matched:onnxruntime::graph_utils::AddNodeInputnum_explicit_inputs == static_cast(target_input_idx)Can only add a new input at the end of the current ones.onnxruntime::graph_utils::ReplaceNodeInputInvalid input index for node . Index: ExplicitInputs: ImplicitInputs:onnxruntime::graph_utils::RemoveNodeShould be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::graph_utils::GetNodeOutputNameindex >= 0 && static_cast(index) < outputs.size()Attempting to get an output that does not exist.onnxruntime::graph_utils::GetNodeInputNameindex >= 0 && static_cast(index) < inputs.size()Attempting to get an input that does not exist.onnxruntime::graph_utils::AddInitializer!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:C:\a\_work\1\s\onnxruntime\core\graph\node_attr_utils.cconnxruntime::utils::SetNodeAttributeutils::HasName(attribute)AttributeProto must have a name.Attempting to load runtime optimization records for a previously loaded optimizer: onnxruntime::RuntimeOptimizationRecordContainer::LoadFromOrtFormatonnxruntime::LoadRuntimeOptimizationRecordFromOrtFormatruntime optimization record produced op id is null. onnxruntime::RuntimeOptimizationRecordContainer::SaveToOrtFormatC:\a\_work\1\s\onnxruntime\core\graph\runtime_optimization_record_container.cconnxruntime::SaveRuntimeOptimizationRecordToOrtFormat*C:\a\_work\1\s\onnxruntime\core/common/string_utils.honnxruntime::utils::SplitString!delimiter.empty()delimiter must not be emptyFailed to parse since_version from C:\a\_work\1\s\onnxruntime\core/graph/op_identifier.honnxruntime::BasicOpIdentifier,class std::allocator > >::LoadFromStringInvalid OpIdentifier string: C:\a\_work\1\s\onnxruntime\core\graph\op_identifier_utils.cconnxruntime::fbs::utils::LoadOpIdentifierOrtFormat0f$f ffefffeeeeeTENSORSPARSE_TENSORSSPARSE_TENSORGRAPHSTENSORSSTRINGSINTSFLOATSGRAPHSTRINGINTFLOATUNDEFINEDNull tensor in tensors attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null floats attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull graph attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.onnxruntime::fbs::utils::LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Missing dims for sparse initializer: Missing indicies for sparse initializer: Missing name for SparseTensor initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.onnxruntime::fbs::utils::LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: Graph attribute value was null. Invalid ORT format model.onnxruntime::fbs::utils::SaveAttributeOrtFormatonnxruntime::fbs::utils::SaveSparseInitializerOrtFormatC:\a\_work\1\s\onnxruntime\core\graph\graph_flatbuffers_utils.cconnxruntime::fbs::utils::SaveInitializerOrtFormatThe given function name: , domain: is not found in model local functionsToutoutTininstatic_cast(i) < node_op_schema->inputs().size()Too many inputs for op domain_version != -1onnxruntime::function_utils::IOTypeConstraintHelper::::operator ()it != opset_imports.end()No opset registered for domain in function opset imports.onnxruntime::function_utils::CreateSchemainput_arg->Type() != nullptrTAggregatedTypesall_tensor_types_ir4C:\a\_work\1\s\onnxruntime\core\graph\function_utils.cconnxruntime::function_utils::Inliner::bindactuals.size() <= formals.size()Number of actual parameters cannot exceed number of formal parametersonnx_func_protoC:\a\_work\1\s\onnxruntime\core\graph\function.cconnxruntime::FunctionImpl::FunctionImplinitializer != nullptr is not found or is not constant initializer.deltalimitUnsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!tensor rank too smallinvalid scales dimensioninvalid scales valueC:\a\_work\1\s\onnxruntime\core\graph\contrib_ops\nchwc_schema_defs.ccCouple the input and forget gates if 1, default 0.Number of neurons in the hidden layer.Constrain seq_lens to integral tensors.QWMWmemory_seq_lensAWThe past input shall be 5 dimensionsinput_ids shall be 2 dimensionssegment_ids input shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.segment_embedding should have 2 dimensions, dimension size known, and same hidden size as word_embedding.gamma should have 2 dimension, dimension size known, and same hidden size as word_embedding.beta should have 1 dimension, dimension size known, and same hidden size as word_embedding.Inputs 0 shall be 3 dimensionsOrtThreadPoolParams { thread_pool_size: auto_set_affinity: allow_spinning: dynamic_block_base_: stack_size: affinity_str: set_denormal_as_zero: ;Processor id must consist of only digits: std::all_of(processor_interval[0].begin(), processor_interval[0].end(), ::isdigit) && std::all_of(processor_interval[1].begin(), processor_interval[1].end(), ::isdigit)onnxruntime::concurrency::ReadThreadAffinityConfigC:\a\_work\1\s\onnxruntime\core\util\thread_utils.ccProcessor id must start from 1: processor_from > 0 && processor_to > 0Invalid processor interval: processor_from <= processor_tostd::all_of(processor_str.begin(), processor_str.end(), ::isdigit)processor_id > 0, skip affinity settingFound invalid processor id in affinity string: Found out-of-range processor id in affinity string: Failed to read affinities from affinity string, thread_pool_size: Number of affinities does not equal to thread_pool_size minus one, affinities: actual_num_affinities == static_cast(options.thread_pool_size) - 1onnxruntime::concurrency::CreateThreadPoolHelpercustom join thread function not setReceived null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1Received null affinity_stringSize of affinity string must be between 1 and C:\a\_work\1\s\onnxruntime\core\util\math_cpu.ccdims[d_i] < d_maxonnxruntime::math::NextPosition??rB¥B@K;?r1:n/ <*=(*>>??33{@l?A ;Eۼ# =pu>7fȹ~;ƼX=P"?>?B;?r1mL:e9 <*=*>>??@Kbad dimensions?@@@AQ5@.N3;A8X} <2?~>++{16?:>=4?~??A%~3a*7A\3JCy7 ':Y;Ӡ5֕8;Y;bad mlas activation kind) is not supported on this deviceQuant GEMM format: AIsSigned(), BIsSigned(ElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool parameter out of computation range!QLinearGlobalAveragePool ImageSize too large!8`    onnxruntime::ToUTF8StringC:\a\_work\1\s\onnxruntime\core\common\helper.cclength overflowonnxruntime::ToWideStringISink must be provided.onnxruntimedefault_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.onnxruntime::logging::LoggingManager::LoggingManagerC:\a\_work\1\s\onnxruntime\core\common\logging\logging.ccDefault logger already set. onnxruntime::logging::LoggingManager::CreateDefaultLogger[ONNXRuntimeError]C:\a\_work\1\s\onnxruntime\core\common\status.ccSystemErrorFAILNO_SUCHFILESUCCESSRUNTIME_EXCEPTIONINVALID_PROTOBUFNO_MODELENGINE_ERRORINVALID_GRAPHEP_FAILMODEL_LOADEDNOT_IMPLEMENTEDGENERAL ERRORcode != static_cast(common::OK)onnxruntime::common::Status::Statusgenericsystemonnxruntime::UnmapFileC:\a\_work\1\s\onnxruntime\core\platform\windows\env.cc error msg: unmap view of file failed. error code: index >= 0onnxruntime::WindowsThread::WindowsThread/\Negative thread index is not allowedWindowThread:_beginthreadex failed with message: kernel32.dllcustom_create_thread_fn returned invalid handle. doserrno: does not exist, processor SetThreadDescriptiononnxruntime, then on , first on group Cannot set affinity for thread onnxruntime::WindowsThread::ThreadMain, group_id: SetThreadAffinityMask done for thread: Cannot set cross-group affinity for thread , mask: , error code: , index: . Specify the number of threads explicitly so the affinity is not set., error msg: fail, errcode = open file SetThreadAffinityMask failed for thread: - Invalid fd was supplied: Received negative size from stat callGetFileSizeEx onnxruntime::WindowsEnv::ReadFileIntoBufferoffset < 0file_path == nullptrReadFile fail: unexpected endlength > buffer.size()SetFilePointerEx , errcode = , allocation_granularity = onnxruntime::WindowsEnv::MapFileIntoMemoryopen file mapping \/DeleteFile() failed - path: , mapped_offset = mapped offset must be a multiple of the allocation granularityGetFinalPathNameByHandle() failed: onnxruntime::WindowsEnv::GetCanonicalPathonnxruntime::WindowsEnv::DeleteFolderRemoveDirectory() failed - path: " when trying to load " "UNC\"Kernel32.dllK32EnumProcessModulesLoadLibrary failed with error FreeLibrary failed with error ReturnNtReturnHrExceptionFailFast%hs(%u)\%hs!%p: LogNtLogHr%hs(%d) tid(%x) %08X %ws %hs!%p: (caller: %p) [%hs(%hs)] [%hs] Msg:[%ws] CallContext:[%hs] kernelbase.dll %wsRaiseFailFastExceptiononnxruntime::WindowsEnv::FormatLibraryFileNameFailed to calculate byte size for saving cpu info on windows in library, error code: Failed to find symbol consist of logical processors: core onnxruntime::WindowsEnv::InitializeCpuInfoFailed to fetch cpu info on windows core(s) from windows system:Found total \*onnxruntime::LoopDirC:\a\_work\1\s\onnxruntime\core/platform/path_lib.hunknown errorenabled_onnxruntime::concurrency::ThreadPoolProfiler::Stopunnamed_thread_poolProfiler not started yet}, "sub_threads": {", C:\a\_work\1\s\onnxruntime\core\common\threadpool.cc}}LogStart must pair with LogEnd!points_.empty()"thread_pool_name": "{"main_thread": {points_.empty()onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Resetonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndonnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart], "core": Distribution", "block_size": ["thread_id": "WaitWaitRevokeDistributionEnqueueRun"num_run": ": {UnknownEvent"core": onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection!ps_Nested parallelism not supported!current_parallel_section.has_value()More work items than threadsn <= num_threads_ + 1n >= 0onnxruntime::concurrency::ThreadPool::ParallelForonnxruntime::concurrency::ThreadPoolTempl::RunInParallelSectiononnxruntime::concurrency::ThreadPoolTempl::RunInParallelC:\a\_work\1\s\include\onnxruntime\core/platform/EigenNonBlockingThreadPool.hFailed to parse path root: onnxruntime::`anonymous-namespace'::ParsePathRootC:\a\_work\1\s\onnxruntime\core\common\path.cconnxruntime::Path::Parse/\ȑБKernelApiSessionNodeC:\a\_work\1\s\onnxruntime\core\common\profiler.ccsession_logger != nullptronnxruntime::profiling::Profiler::StartMaximum number of events reached, could not record profile event.onnxruntime::profiling::Profiler::Initializeonnxruntime::profiling::Profiler::EndProfiling",onnxruntime::profiling::Profiler::EndTimeAndRecordEventWriting profiler data to file "tid" :"dur" :{"cat" : ""pid" :"name" :""args" : {"ts" :"ph" : "X",} }, " : " : "] illegal input path:unexpected failureC:\a\_work\1\s\onnxruntime\core\platform\windows\logging\etw_sink.cconnxruntime::logging::EtwRegistrationManager::LazyInitializeETW registration failed. Logging will be broken: onnxruntime::logging::EtwSink::SendImplUnexpected Severity of Format specifier requires numeric argument.Number is too bigInvalid presentation type for integerInvalid presentation type for boolInvalid presentation type specifierModifier requires an integer presentation type for boolInvalid presentation type for pointerInvalid presentation type for stringInvalid presentation type for floating-point%I:%M:%S %p%H:%M:{:02}{:04}Invalid presentation type for wchar_tMissing precision specifier.Precision not allowed for this argument type.0e+00Dynamic width or precision index too large.{}{:02}{}{:02}Precision is not an integer.Negative width.Width is not an integer.Number is too big.falsetrueNegative precision.0bintegral cannot be stored in wchar_t{:0{}}\t0X0x0B\r\n\n\t\u{\\\rArgument not found.\x{String pointer is null. [:, ] Invalid format string.Unmatched '}' in format string.nan(snan)nan(ind)UTCCan not switch from manual to automatic indexingMissing '}' in format string.Unknown format specifier.Invalid format string - missing type after %Invalid format string - chrono-specs must begin with conversion-specInvalid type.Precision specification invalid for non-chrono::duration type, see N4950 [time.format]/1.Invalid type specification.Invalid modifier specification.Invalid format string - missing type after modifier.invalid fill character '{'Invalid fill (too long).{:L%F %T}Incompatible modifier for typeCan not switch from automatic to manual indexingffffffGzGK7A!uq m7c^6 02 p.9 ^333p= I h1*  '.6>GPZdoz -?Rey,E^x<Zy<^cCdeHImMSuUVwWxXyYz Kp0%)Y : ; < > A I M N Q b   < > A G K Q p u   < > @ A G K M U b   < > A F J U b  ; > ? 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P `    9 X x       0 `0pR6P*25;GKPW]fp]P`0  7;PP 8Pp :<?P`gj>$p$$/0@4Dh@j`jnjjjjkPk[kck}k@noOoooop2PUdpp0P){`V ;@FJR%#&00@NP^q!$')49;BGIKMQTWY[]_adglty~0@P`{P``p@ P_ &270;e;1 J             3  : H$'$ y)!9C VY ^  Y+F *, >A M/t<<+ +&5 / !!  ]-8~Y ?Vg+^T/mr7\; 8F tN !7 g<~ +1nj}6*3 {-X .1$+% $$(4  7* ,H !82 @' 6I33. *+*N$> 5'`/ ;  \H 6&E : <S . HSI - , % )2oc0G9 Q F [K9 #k .t'JpFWUG AT$>- :*L D=  ,d%,  w_  8 (T . 7 :1K` (4BP_p $@^}(NuM|DyYMV(q S A 9 = 00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899ߓߓߓtensor_typemap_typesequence_typeonnxruntime::fbs::utils::SaveTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::SaveMapTypeOrtFormatC:\a\_work\1\s\onnxruntime\core\flatbuffers\flatbuffers_utils.cconnxruntime::fbs::utils::SaveSequenceTypeOrtFormatonnxruntime::fbs::utils::SaveValueInfoOrtFormatWe do not support type [] for nowonnxruntime::fbs::utils::SaveTypeInfoOrtFormatonnxruntime::fbs::utils::LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.SaveValueInfoOrtFormat: value_info_proto for is missing type info.Null value type info in fbs::SequenceType. Invalid ORT format model.onnxruntime::fbs::utils::LoadTensorTypeAndShapeOrtFormatonnxruntime::fbs::utils::LoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.Null tensor type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.onnxruntime::fbs::utils::LoadSequenceTypeOrtFormat is not supported currentlyNull map type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.onnxruntime::fbs::utils::LoadTypeInfoOrtFormatonnxruntime::fbs::utils::LoadValueInfoOrtFormatNull type info for . Invalid ORT format model.Type:opset import domain is null. Invalid ORT format model.opset id is null. Invalid ORT format model.onnxruntime::fbs::utils::LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.tensor(Invalid data type Unsuported type proto value case.sparse_tensor(map(optional(opaquemapoptionalInvalid tensor data type complex128complex64DataTypeUtils::FromDataTypeString - Received invalid data type string ' typestr: , has unsupported type: Operator '' has been deprecated since version has unsupported type has inconsistent type not in allowed input sizes.) has input size not in range [min=, max=) in op definition.has output size not in allowed output sizes.) has output size )'s input is marked single but has an empty string in the graph) has more inputs () than declared ( for operator ' appeared multiple times.)'s output ) has more outputs (' is expected to have field 'sparse_tensor'' is expected to have field 't'Mismatched attribute type in 'Unrecognized attribute: ' is missing. has unknown expected type' is expected to have field 'type_proto'' is expected to have field 'g', opset_version = Duplicate type constraint nameRequired attribute '(inputs_.size() - 1) == iExtra unparsed input unexpected.Error parsing function body:Cannot find a function builder that satisfies the requested opset version: op_type = !(it.GetName().empty())(outputs_.size() - 1) == iONNX Schema : failed validating the check: The input must be a tensor of a numeric type or string. The output will be of the same tensor type.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\defs.ccValues greater than this are mapped to 1, others to 0.The input must be a tensor of a numeric type. The output will be of the same tensor type.map(int64, float)map(int64, string)The input must be an integer map to either string or float.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]Indicates whether to do OvR or multinomial (0=OvR is the default).A collection of intercepts.A collection of weights of the model(s).The output will be a tensor of strings or integers.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'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.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'The input must be a tensor of a numeric type.The total number of regression targets, 1 if not defined.Weights of the intercepts, if used.Weights of the model(s).One of 'MAX,' 'L1,' 'L2'List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.Exactly one of 'cats_*' attributes must be provided.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.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'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 kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. Its size will match the bactch size of the input.The input must be a tensor of a numeric type, either [C] or [N,C].Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'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.First set of probability coefficients.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.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.Support vector coefficients.Flag indicating whether the regression is a one-class SVM or not.Chosen support vectorsThe input type must be a tensor of a numeric type, either [C] or [N,C].Only one of the attributes 'nodes_values', 'nodes_values_as_tensor' should be specified.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The number of support vectors.Non of classlabels_int64s or classlabels_strings is set.Only one of the attributes 'base_values', 'base_values_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_hitrates', 'nodes_hitrates_as_tensor' should be specified.The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.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'.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.The output is a 1-D tensor of string, float, or integer.The input must be a tensor of strings or integers, either [N,C] or [C].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.The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe output is a tensor of strings or integers. Its shape will be the same as the input shape.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.map(string, float)map(int64, double)map(string, int64)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.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.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.map(string, double)The input type must be a tensor of a numeric type.The size of each input in the input listValue(s) to change to.A value that needs replacing.Value(s) to change toThe 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. and the key type are different, which is not permitted for LabelEncoders.At least one of keys_tensor, keys_strings, keys_int64s, keys_floats must be set. and the number of values must be the same in the LabelEncoder.At least one of values_tensor, values_strings, values_int64s, values_floats must be set.The input type was The default tensor must be a singleton 1D tensor.The default tensor type and the value type The number of keys Output type is determined by the specified 'values_*' attribute.The input type is a tensor of any shape.A list of floats.A list of ints.A list of strings.Keys encoded as a 1D tensor. One and only one of 'keys_*'s should be set.A float.An integer.A string.Values encoded as a 1D tensor. One and only one of 'values_*'s should be set.Input's shape should be 1D or 2DA default tensor.{"_Unused"} if values_* has string type, {-1} if values_* has integral type, and {-0.f} if values_* has float type.Thresholds to do the splitting on for each node.Feature id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Tree id for each node.Child node if expression is false.Child node if expression is true.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'Popularity of each node, used for performance and may be omitted.The index of the class list that each weight is for.node id that this weight is for.The id of the tree that this node is in.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.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)Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'The weight for the class in class_id.Child node if expression is trueNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Only one of the attributes 'target_weights', 'target_weights_as_tensor' should be specified.The node id of each weightThe id of the tree that each node is in.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.Child node if expression is falseDefines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The total number of targets.The weight for each targetThe index of the target that each weight is forZipMap input shape should be 1D or 2D.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.seq(map(int64, float))seq(map(string, float))The output will be a sequence of string or integer maps to float.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.A list of labels.The output type will be a tensor of strings or integers, and will have the same shape as the input.The input type must be a tensor of integers or strings, of any shape.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\traditionalml\old.ccInput type is not string tensor but key_strings is setOnly one of keys_*'s can be set in label encoder.Label encoder has only one output.Label encoder has only one input.Only one of values_*'s can be set in label encoder.Input type is not float tensor but keys_floats is setInput type is not int64 tensor but keys_int64s is setA list of strings. One and only one of 'value_*'s should be set.A list of strings. One and only one of 'keys_*'s should be set.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\text\defs.ccInputs and outputs must be UTF-8 stringsInputs must be UTF-8 stringsRegex pattern to match on. This must be valid RE2 syntax.Delimiter to split on. If left unset or set to the empty string (""), the input is split on consecutive whitespace.Outputs are bools and are True where there is a full regex match and False otherwise.The input must be a UTF-8 string tensorMaximum 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.Input shape must have either [C] or [1,C] dimensions where C > 0The number of substrings generated.Tensor of substrings.Boolean. Whether the identification of stop words in X is case-sensitive. Default is falsestring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"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.List of stop words. If not set, no word would be removed from X.minus infinity (if supported by the datatype) or undefined otherwiseplus 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 otherwisesum square { data_square = Mul(data, data) reduced = ReduceSum(data_square, axes) } sumC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\defs.cc { data_double = Cast(data) data_exp = Exp (data_double) reduced_sum = ReduceSum(data_exp, axes) reduced_double = Log (reduced_sum) reduced = CastLike(reduced_double, data) } log sum { reduced_sum = ReduceSum(data, axes) reduced = Log (reduced_sum) } product { 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) } L1 norm { data_abs = Abs(data) reduced = ReduceSum(data_abs, axes) } log sum exponentKeep the reduced dimension or not, default 1 means keep reduced dimension.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).'axis' must be in [-rank(indices), rank(indices)-1]L2 normWhether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).undefinedtype: and Output expected to have tensor or sparse tensor typeInput: does not match type of output: axis must be in [-rank, rank-1].indices must be in [-rank, rank-1].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.The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoConstrain output types. Casting to complex is not supported.Constrain input types. Casting from complex is not supported.output = Cast (input)C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\defs.cctarget_typeThe 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.Invalid dimension value: Invalid position of 0.Target shape may not have multiple -1 dimensions.(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.Dimension could not be inferred: incompatible shapesInvalid Target shape product of 0. Product cannot be 0 in combination with -1reshapedConstrain output to int64 tensor.Input tensor can be of arbitrary type.(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.(Optional) Starting axis for slicing the shape. Default value is 0.Negative value means counting dimensions from the back. has rank Constrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..All inputs to Concat must have same rank. Input Value=Constrain output types to any tensor type.concat_resultMismatch between number of splits () and outputs (Both 'split' input and 'num_outputs' attribute were givenInvalid value of attribute 'axis'. Rank=Neither 'split' input nor 'num_outputs' attribute has been givenAttribute `num_outputs` value cannot be lower than 1Mismatch between the sum of 'split' () and the split dimension of the 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).Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps'step' cannot be 0 for SliceNumber of outputs to split parts of the tensor into. If the tensor is not evenly splittable the last chunk will be smaller.optional(seq(tensor(uint64)))optional(seq(tensor(uint32)))optional(seq(tensor(uint16)))optional(seq(tensor(uint8)))optional(seq(tensor(int64)))optional(seq(tensor(int32)))optional(seq(tensor(int16)))optional(seq(tensor(int8)))optional(seq(tensor(string)))optional(seq(tensor(double)))optional(seq(tensor(float)))optional(seq(tensor(float16)))optional(tensor(uint8))optional(seq(tensor(complex128)))optional(seq(tensor(complex64)))optional(seq(tensor(bool)))optional(tensor(int8))optional(tensor(uint64))optional(tensor(uint32))optional(tensor(uint16))optional(tensor(float16))optional(tensor(int64))optional(tensor(int32))optional(tensor(int16))optional(tensor(bool))optional(tensor(string))optional(tensor(double))optional(tensor(float))optional(tensor(complex128))optional(tensor(complex64))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'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) 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) ``` .Constrain input 'X' and output 'Y' to all tensor types.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.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".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). 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.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 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.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.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".roiThree 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.Constrain roi type to float or double.sizesIf 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.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.Constrain grid types to float tensors.Constrain input `X` and output `Y` types to all tensor types.gridif 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.Length of input 'size' is . It must be 4 for 2D or 5 for 5D.theta { # 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) } > } Constrain size's type to int64 tensors.(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).Indices tensor must have rank >= 1Constrain input and output types to all tensor, sequence, and optional types.Constrain to boolean tensors.Input 'values' must be rank 1 tensor.Input 'depth' must have exactly one element.Input 'depth' must be a scalar or rank 1 tensor.OneHot node must have three inputs.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input 'values' must have exactly two elements.Constrain input to only numeric types.depthInput steps has incorrect lengthInput axes has incorrect lengthIncorrect or missing input value for starts and endsSlice op must have either three, four or five inputs.Input rank for starts and ends should be the same: () vs (Invalid attribute perm {stepsA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.Attribute perm for Transpose has repeated value: }, input shape = {Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).transposedInput and output types can be of any tensor type.updatesType 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.axis must be in [-r, r-1]data tensor must have rank >= 1Type of reduction to apply: none (default), add, mul, max, min. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'max': reduction using the maximum operation.'min': reduction using the minimum operation.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).squeezedList 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).expandedList 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).Blocks of [blocksize, blocksize] are moved.Input tensor must be 4-dimensionalBlocksize must be positiveDCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'Repeats' input must be 1D tensor of type int64Constrain repeat's type to int64 tensors.repeatsinput_dataoutput_data = Slice (padded_input, start_dims, end_dims)output_data = Slice (padded_input, start_dims, end_dims, axes_input)CenterCropPadIf 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.output_dataAxis is referred to more than once.Unexpected axis value: . Expected range [Constrain output types to boolean tensors.Constrain input types to float tensors.Constrain to any tensor type. Return elements, either from X or Y, depending on condition. Where behaves like [numpy.where](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) with three parameters. (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.(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.'input' must have rank >= 2Constrain to all tensor types.Constrain input and output types to all tensor types (including bfloat).(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.'sequence_lens' must have rank of 1Invalid value for attribute axis(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).(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).inverse_indicesThe number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Supported modes: `constant`(default), `reflect`, `edge`, `wrap` 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], ] ``` `shape` only supports `int32_t` or `int64_t` inputsCenterCropPad op must have 2 inputs.axes_input = Constant ()Number of elements of input 'shape' () does not match the number of axes (padded_sh = Max(x_shape, shape)x_shape = Shape (input_data)x_shape = Gather (x_shape_alldims, axes_input)x_shape_alldims = Shape (input_data)pads = Concat (pad_amount_left, pad_amount_right)pad_amount_right = Sub(pad_amount, pad_amount_left)pad_amount_left = Div(pad_amount, k2)pad_amount = Sub(padded_sh, x_shape)x_shape2 = Gather (x_shape_alldims2, axes_input)x_shape_alldims2 = Shape (padded_input)padded_input = Pad (input_data, pads)padded_input = Pad (input_data, pads, , axes_input)end_dims = Add (start_dims, shape)start_dims = Div (sh_diff, k2)sh_diff = Sub (x_shape2, shape)x_shape2 = Shape (padded_input)Input tensor must be 1-dimensionalpixel_formatPixel format. Can be one of "RGB", "BGR", or "Grayscale".RGBConstrain input types to 8-bit unsigned integer tensor.imageencoded_streamC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\image\defs.ccImageDecoderConstrain output types to 8-bit unsigned integer tensor. { Alpha = Constant () AlphaCast = CastLike (Alpha, X) Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less(X, ZeroCast) AlphaMulX = Mul (AlphaCast, X) Y = Where (XLessThanZero, AlphaMulX, X) } { Zero = Constant () ZeroCast = CastLike (Zero, X) Y = Max (X, ZeroCast) } Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). { 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) 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) } Coefficient of ELU. { 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) } Mish { Softplus_X = Softplus (X) TanHSoftplusX = Tanh (Softplus_X) Y = Mul (X, TanHSoftplusX) } Constrain input X and output types to float tensors.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. X_alpha = Div (X, alpha) Elu_Result = Elu (X_alpha) Y = Mul (alpha, Elu_Result) Gelu approximation algorithm: `"tanh"`, `"none"`(default).`"none"`: do not use approximation.`"tanh"`: use tanh approximation. 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) 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) approximate 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. Constrain input Y types to float/int tensors.Constrain input X and output types to float/int tensors. { Zero = Constant () ZeroCast = CastLike(Zero, X) XLessThanZero = Less (X, ZeroCast) SlopeMulX = Mul (slope, X) Y = Where(XLessThanZero, SlopeMulX, X) } slopeValue of beta.Value of alpha.List of tensors for { HS_X = HardSigmoid(X) Y = Mul (X, HS_X) } { 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) } output = Identity (input)Constrain input and output types to numeric tensors.data_0output = Where (input_large_than_max, max, input)input_large_than_max = Less (max, input)output = Where (input_less_than_min, min, input)input_less_than_min = Less (input, min)output = Where (output_large_than_max, max, tmp)output_large_than_max = Less (max, tmp)tmp = Where (input_less_than_min, min, input)C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\defs.cc broadcasting: (Wrong op_type name for running propagation: Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentConstrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.input and zero_point pair is expected to have same type.Constrain output y 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 output Y data type as 32-bit integer tensor.If set to 1 will perform the sums in reverse direction.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.axis tensor can be int32 or int64 onlyconst_oneconst_zeroConstrain input and output types to floating-point tensors.loss = Squeeze (loss_N1dd, axes) input_gather_element = GatherElements (input, expanded_target) loss_NCdd = Neg (input_gather_element) loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one) ignore_indexexpanded_target = Unsqueeze (target, axes)weight_gather = Gather (weight, target)loss = ReduceSum (loss_Ndd)loss = ReduceMean (loss_Ndd)loss_Ndd = Squeeze (loss_N1dd, axes) loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) loss_Ndd = Mul (loss_unweighted, weight_gather)loss = Mul (loss_unweighted, weight_gather)loss_unweighted = Squeeze (loss_N1dd, axes)const_zero_floatinput_gather_element = GatherElements (input, transform_targets) 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) const_ignore_indexloss_NCdd = Neg (input_gather_element_transform)input_gather_element_transform = Where (mask, const_zero_float, input_gather_element)input_gather_element_transform = Where (mask, const_zero_casted, input_gather_element)const_zero_casted = Cast (const_zero_float)const_one_casted = Cast (const_one_float)const_one_floatsqueeze_mask = Squeeze (mask, axes)loss_N1dd = Slice (loss_NCdd, const_zero, const_one, const_one)weight_gather_temp_1 = Where (mask, const_zero_float, weight_gather_temp)weight_gather_temp = Gather (weight, transform_targets)weight_gather = Where (squeeze_mask, const_zero_float, const_one_float)weight_gather = Where (squeeze_mask, const_zero_casted, const_one_casted)Target rank must be 1 less than the input rank. loss_sum = ReduceSum (loss_Ndd) weight_gather_sum = ReduceSum (weight_gather) loss = Div (loss_sum, weight_gather_sum) weight_gather = Squeeze (weight_gather_temp_1, axes)weight_gather_temp_1 = Where (mask, const_zero_casted, weight_gather_temp)X_ReduceMax = ReduceMax (input, axes) X_Sub = Sub (input, X_ReduceMax) X_Exp = Exp (X_Sub) X_ReduceSum = ReduceSum (X_Exp, axes) output = Div (X_Exp, X_ReduceSum) X_ReduceMax = ReduceMax (input) 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) { 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) } { One = Constant () OneCast = CastLike (One, input) AbsInput = Abs(input) OneAddAbsInput = Add (OneCast, AbsInput) output = Div(input, OneAddAbsInput) } K input must be a one-dimensional tensor of size 1.Axis has less than the requested k elements.K input must be of type int64.IndicesValuesWhether to return the elements in sorted order.Whether to return the top-K largest or smallest elements.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).Constrain index tensor to int64Constrain input and output types to all tensors. { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Constrain output types to numeric tensors.Constrain the input size to int64_t.Hann { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } Blackman { A0 = Constant () A1 = Constant () A2 = Constant () Zero = Constant () One = Constant () Two = Constant () Tau = Constant () Periodic_Size_FP = Cast (size) Symmetric_Size_FP = Sub(Periodic_Size_FP, One) IsPeriodic = Constant () IsPeriodic_FP = Cast (IsPeriodic) IsSymmetric_FP = Sub(One, IsPeriodic_FP) Periodic_Component = Mul(Periodic_Size_FP, IsPeriodic_FP) Symmetric_Component = Mul(Symmetric_Size_FP, IsSymmetric_FP) Size_FP = Add(Periodic_Component, Symmetric_Component) AngularIncrement = Div (Tau, Size_FP) Range = Range (Zero, Periodic_Size_FP, One) RangeAngular = Mul (Range, AngularIncrement) TwoRangeAngular = Mul (RangeAngular, Two) CosTwoRangeAngular = Cos (TwoRangeAngular) A2_Component = Mul (A2, CosTwoRangeAngular) CosRangeAngular = Cos (RangeAngular) A1_Component = Mul (A1, CosRangeAngular) Temp0 = Sub (A0, A1_Component) Temp1 = Add (Temp0, A2_Component) output = Cast (Temp1) } HammingThe 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. dft_length input must be scalar.num_mel_bins input must be scalar.num_mel_binslower_edge_hertzsample_rateConstrain to float tensorsConstrain to integer tensors.upper_edge_hertzwindow input must have rank = 1.frame_length input must be scalar.Constrain to any numerical types.signalIf 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 STFT has both a window input and frame_length specified, the dimension of the window must match the frame_length specified!frame_stepConstrain scalar length types to int64_t.Constrain signal and output to float tensors.frame_lengthWeight rank must be 1.Input and target dimension value mismatch.losstargetConstrain target to integer typesConstrain input, weight, and output types to floating-point tensors.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.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. does not match the equation indices.Ellipsis represents incompatible dimensions.Number of input tensors does not match the operands in the equation.NegativeLogLikelihoodLossEinsum expression string.Rank of input Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.Constrain input and output types to all numerical tensor types.Inputsoutput = NegativeLogLikelihoodLoss (X_Log, labels, weights)log_prob = Identity (X_Log) 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) Shape3Dlabelsoutput = NegativeLogLikelihoodLoss (X_Log, labels)log_probinput tensor must have rank >= 2, including the complex dimension.is_onesided and inverse attributes cannot be enabled at the same timeSoftmaxCrossEntropyLossaxis_tensor != nullptr%s:%u: %s: Assertion `%s` failed: rank should be equal to new_shape_proto.dim_size()operator ()rank == static_cast(new_shape_proto.dim_size()) is invalid for a tensor of rank . Valid values are '-rank <= axis && axis != -1 && axis < rank - 1'axis input must be a scalar.%s:%u: %s: Assertion `%s` failed: axis should not be nullptr at this pointWhether to perform the inverse discrete Fourier Transform. Default is 0, which corresponds to `false`.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`.dft_length input must be a scalar.axis attribute value dft_lengthsize input must be greater than 0.size input must be a scalar.Constrain scalar length types to integers.A {name} window with length: size. The output has the shape: [size].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. 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. value_floatThe values for the elements for the 1D, float32, output tensor.value_floatsThe value for the sole element for the scalar, UTF-8 string, output tensor.value_intThe values for the elements for the 1D, int64, output tensor.value_intsThe value for the sole element for the scalar, float32, output tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32value_stringThe values for the elements for the 1D, UTF-8 string, output tensor.value_stringsInput tensor must be 2-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.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.Constrain input types.Constrain output types to be numerics.Lower boundary of the output values.Upper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.The mean of the normal distribution.The standard deviation of the normal distribution.Input tensor must have rank 2Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Output type must be int32 or int64Constrain output types to integral tensors.All inputs to 'Range' op must be of the same typeX_greater = Greater (X_random, input)output = Cast (X_greater)The data type for the elements of the output tensor. if not specified, we will use the data type of the input tensor.Constrain input types to common numeric type tensors. { sub_result = Sub (limit, start) sub_result_casted = Cast (sub_result) delta_casted = Cast (delta) div_result = Div (sub_result_casted, delta_casted) ceil_result = Ceil (div_result) ceil_result_relu = Relu (ceil_result) ceil_result_relu_int = Cast (ceil_result_relu) ceil_result_relu_bool = Cast (ceil_result_relu) variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start) (cond_out, current, range) { cond_out = Identity (cond) current = Add (prev, delta) range = Identity (prev) }> } X_random = RandomUniformLike (input)Attribute expected to have a one-dim tensorAttribute expected to have a one-dim sparse tensorThe value for the elements of the output tensor.The value for the elements of the output tensor in sparse format.sparse_valueThe value for the sole element for the scalar, int64, output tensor.Constrain output types to all numeric tensors and bool tensors.BernoulliInput to 'Range' op should be scalars (Tensor with only one element and shape empty)seq(tensor(float8e4m3fn))seq(tensor(float8e4m3fnuz))seq(tensor(float8e5m2))optional(tensor(float8e4m3fnuz))optional(tensor(float8e5m2))optional(tensor(float8e5m2fnuz))seq(tensor(float8e5m2fnuz))optional(seq(tensor(bfloat16)))optional(tensor(bfloat16))optional(tensor(float8e4m3fn))condC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\defs.ccGraph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.All Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv9.Only boolThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.v_initialv_final_and_scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.initial_state_and_scan_inputsfinal_state_and_scan_outputsAn optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].All Tensor types up to IRv9.Constrain 'x' to float, float16, bfloat16 or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer/float tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\defs.cc(Optional) The axis of the quantization dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 quantization (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.Constrain 'x' to float tensor.(Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Constrain 'x_zero_point' and 'x' to 8-bit integer or float, or /32-bit integer tensor.'x_scale' determines the output type.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. { Q_Min = Constant() Q_Max = Constant() X_Min = ReduceMin (x) X_Min_Adjusted = Min (X_Min, Q_Min) X_Max = ReduceMax (x) X_Max_Adjusted = Max (X_Max, Q_Min) X_Range = Sub (X_Max_Adjusted, X_Min_Adjusted) Scale = Div (X_Range, Q_Max) Min_Scaled = Div (X_Min_Adjusted, Scale) Initial_ZeroPoint_FP = Sub (Q_Min, Min_Scaled) Clipped_ZeroPoint_FP = Clip (Initial_ZeroPoint_FP, Q_Min, Q_Max) Rounded_ZeroPoint_FP = Round (Clipped_ZeroPoint_FP) Zeropoint = Cast (Rounded_ZeroPoint_FP) y_scale = Identity (Scale) y_zero_point = Identity (Zeropoint) y = QuantizeLinear (x, Scale, Zeropoint) } 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. Dilation value along each spatial axis of the kernel. Default is 1 along each axis.Number of groups the input and output channels, C and oC, are divided into. C and oC must both be divisible by group. Default is 1.Shape of the convolution kernel. If not present, it is inferred from the shape of input W.Number of groups of offset. C must be divisible by offset_group. Default is 1. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: * Output case #1: Y, running_mean, running_var (training_mode=True) * Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B ``` where: ``` current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) ``` Notice that `ReduceVar` refers to the population variance, and it equals to `sum(sqrd(x_i - x_avg)) / N` where `N` is the population size (this formula does not use sample size `N - 1`). The computation of ReduceMean and ReduceVar uses float to avoid overflow for float16 inputs. When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. This number of op outputs should be 3 when Training_mode = True, but it is not.offset_groupPadding for the beginning and end along each spatial axis. The values represent the number of pixels added to the beginning and end of the corresponding axis and can take any nonnegative value. The format should be as follows: [x1_begin, x2_begin, ..., x1_end, x2_end, ...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. Default is 0 along each axis.Stride along each spatial axis. Default is 1 along each axis.DeformConvinput_meanThis number of op outputs should be 1 when Training_mode = False, but it is not.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.running_varConstrain scale and bias types to float tensors.Constrain mean and variance types to float tensors.input_varrunning_meanThe axis on which to apply normalization, -1 mean last axis.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.The order of the normalization, only 1 or 2 are supported.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Constrain input and output to all tensor types.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). { 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) } ) for attribute 'axis'Invalid value(Constrain input and output types to float tensors.ngram_indexes must be non-empty with no negative valuesThe number of channels to sum overScaling parameter.The exponent.Input tensor must have at least 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeauto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\defs.ccAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionConstrain input and output types to float and 8 bit tensors.Input tensor X must have at least 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.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. MaxUnpool op must have either two or three inputs.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Attribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.p value of the Lp norm used to pool over the input data.RoIs tensor must have 2 dimensionsThe shape of the convolution kernel. If not present, should be inferred from input 'w'.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.number of groups input channels and output channels are divided into. default is 1.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.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeBiasReshaped = Reshape (BiasT, ScaleShape)Scaled = Mul (ScaleReshaped, Normalized)Biased = Add (Scaled, BiasReshaped)The number of groups of channels. It should be a divisor of the number of channels `C`.ScaleShape = Constant ()ScaleT = Cast (scale)BiasT = Cast (bias)ScaleReshaped = Reshape (ScaleT, ScaleShape)GroupNormalization1-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1Input tensor must have rank 1 or 2Input is ether string UTF-8 or int32/int64list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)A list of integers, along which to reduce. The default is to calculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.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.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.1-dimensional tensor with dilation value along each spatial axis of the image. If not present, the dilation defaults to 1 along each spatial axis of the image.1-dimensional tensor with padding value for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. If not present, the padding defaults to 0 along start and end of each spatial axis.1-dimensional tensor with stride value along each spatial axis. If not present, the stride defaults to 1 along each spatial axis. { Exponent = Constant () Epsilon = Constant () X_RM = ReduceMean (X) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } { Exponent = Constant () Epsilon = Constant () axes = Constant () X_RM = ReduceMean (X, axes) EX_squared = Pow (X_RM, Exponent) X_squared = Pow (X, Exponent) E_Xsquared = ReduceMean (X_squared, axes) Variance = Sub (E_Xsquared, EX_squared) STD = Sqrt (Variance) X_variance = Sub (X, X_RM) Processed_STD = Add (STD, Epsilon) Y = Div (X_variance, Processed_STD) } Attribute pads must have an even sizeinput must have rank 3.block_shapeConstrain input and output types to all numeric tensor types.image_shapeEpsilon = Cast (FloatEpsilon)Axes_1 = Constant()Mean2D = ReduceMean (XU, Axes_1)MeanOfSquare = ReduceMean (Square, Axes_1)sinceVersion == 17 || sinceVersion == 18BuildContextDependentFunctionBodyLayerNormalization%s:%u: %s: Assertion `%s` failed.FloatEpsilonConstrain input types and output Y type to float tensors.NormalizedT = Cast (Normalized)Scaled = Mul (NormalizedT, Scale2D)The first normalization dimension. If rank(X) is r, axis' allowed range is [-r, r). Negative value means counting dimensions from the back.Type of Mean and InvStdDev. This also specifies stage one's computation precision.N = Shape (X)InstanceShape = Shape (X)NewShape = Concat (N, NumGroups, GroupSize, InstanceShape)XReshaped = Reshape (X, NewShape)num_groupsC = Shape (X)NumGroupsGroupSize = Div (C, NumGroups)Square = Mul (X3D, X3D)MeanOfSquare = ReduceMean (Square, Axes2)SquareOfMean = Mul (Mean, Mean)Deviation = Sub (X3D, Mean)Shape3D = Constant ()X3D = Reshape(XReshaped, Shape3D)Axes2Mean = ReduceMean (X3D, Axes2)Constrain input to boolean tensor.Constrain output to boolean tensor.Constrain input types to all numeric tensors.Constrain input types to all (non-complex) tensors.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\defs.ccConstrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrain input/output to boolean tensors.Constrain input/output to integer tensors.Constrain input to integer tensors. { O1 = Less (A, B) O2 = Equal (A, B) C = Or (O1, O2) } { O1 = Greater (A, B) O2 = Equal (A, B) C = Or (O1, O2) } {empty_value}The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equal 0, then the resulting tensor has the reduced dimension pruned. The input tensor must not be empty. The type of the output tensor is integer.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).Constrain input and output types to high-precision and 8 bit numeric tensors.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\reduction\old.ccAttribute 'type' should be a TypeProto and it should specify a type.Input type is null. Type information is expected for the input.Optional is expected to have either an input or the type attribute set.Optional is expected to have an output.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\defs.ccOptionalHasElement is expected to have 0 or 1 input.OptionalHasElement is expected to have 1 output.Type of the element in the optional outputConstrain input type to all tensor and sequence types.Constrain output type to all optional tensor or optional sequence types.OptionalGetElement must have an input element.Input type is null. Input must have Type information.Optional-type input must contain an element with type information.Constrain input type to optional tensor and optional sequence types.Constrain output to a boolean tensor.Constrain output type to all tensor or sequence types. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).constant_valueConstrain input and output to only numeric types.Constrain input types. Casting from strings and complex are not supported.'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Supported modes: `constant`(default), `reflect`, `edge`outputs...Which axis to split onConstrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.rank must be greater than axisThree modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapelength of each outputList 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`.paddingstileslegacy optimization attribute.consumed_inputsheight_scaleTwo interpolation modes: nearest(default), bilinearConstrain tiles and axis's type to int64 tensors.The scale along width dimension. It takes value greater than or equal to 1.width_scaleThe scale along height dimension. It takes value greater than or equal to 1.Attribute 'scales' must have floats type.Attribute 'scales' is required.Constrain output types to bool, int32, int64, float16, float, double tensors.) is not equal to the existing rank value (Ranks inferred (Number of elements of attribute 'scales' must be same as rank of input 'X'Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthAxes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Target shape may not have multiple -1 dimensionsInvalid position of 0C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\tensor\old.ccoutput = Cast (input)length of each output. Values should be >= 0.Input axes has invalid dataInvalid Target shape product of 0The input is not evenly splittableType of reduction to apply: none (default), add, mul. 'none': no reduction applied. 'add': reduction using the addition operation. 'mul': reduction using the multiplication operation.'step' cannot be 0'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeThree interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, `length_original` as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", `scale = length_resized / length_original`,
if coordinate_transformation_mode is `"half_pixel"`,
`x_original = (x_resized + 0.5) / scale - 0.5`
if coordinate_transformation_mode is `"pytorch_half_pixel"`,
`x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0`
if coordinate_transformation_mode is `"align_corners"`,
`x_original = x_resized * (length_original - 1) / (length_resized - 1)`
if coordinate_transformation_mode is `"asymmetric"`,
`x_original = x_resized / scale`
if coordinate_transformation_mode is `"tf_crop_and_resize"`,
`x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1)` . This attribute describes how to interpret the `sizes` input with regard to keeping the original aspect ratio of the input, and it is not applicable when the `scales` input is used.
Given a set of `sizes`, associated with a subset of `axes` (explicitly provided or default), and assuming `d = axes[i]`, with `i` being the index of the provided `sizes`.
If `keep_aspect_ratio_policy` is `"stretch"`, the original aspect ratio is disregarded, and the input is resized to the specified size:
`out_size[d] = sizes[i]`
If `keep_aspect_ratio_policy` is `"not_larger"`, the sizes are adjusted so that no extent of the output is larger than the specified size, while keeping the original aspect ratio:
`scale = Min(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
If `keep_aspect_ratio_policy` is `"not_smaller"`, the sizes are adjusted so that no extent of the output is smaller than the specified size, while keeping the original aspect ratio:
`scale = Max(sizes[i] / in_size[d])`
`out_size[d] = round_int(scale * in_size[i])`
For non-resizable axes (those not specified in `axes`), the output size will be equal to the input size. Note: `round_int` stands for computing the nearest integer value, rounding halfway cases up. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]List of non-negative integers, indicate the dimensions to squeeze.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1](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.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.List of non-negative integers, indicate the dimensions to be inserted'axis' must be in [-rank(indices)-1, rank(indices)]Attribute pads has incorrect lengthList of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Which axis to split on. Attribute value for pads is requiredConstrain input and output types to all tensor and sequence types. 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], ] ``` Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Value of alpha default to 0.2Value of beta default to 0.5Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Whether C should be broadcastedIf onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m, n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). When invoked with real or complex valued input, the default value is 0. Values can be 0 or 1.The axis on which to perform the DFT. By default this value is set to 1, which corresponds to the first dimension after the batch index. Negative value means counting dimensions from the back. Accepted range is $[-r, -2] \cup [0, r-2]$ where `r = rank(input)`. The last dimension is for representing complex numbers and thus is an invalid axis.Whether to perform the inverse discrete fourier transform. By default this value is set to 0, which corresponds to false.Invalid value for attribute kNumber of top elements to retrieveDimension on which to do the sort.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\math\old.ccDescribes 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).'shape' input must be 1D tensorexpanded_targetweight_gatherloss_unweightedloss_sumweight_gather_suminput_gather_elementloss_NCddloss_N1ddloss_Nddinput_gather_element_transformsqueeze_maskconst_one_castedweight_gather_tempconst_zero_target_typedexpanded_target_int64transform_targetsconst_zero_castedX_LogSMX_LogSM_NCDX_shapeX_Logweight_gather_temp_1X_NCDX_NDCIf set, defines the broadcast dimensions. See doc for details.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size 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. Pass 1 to enable broadcastingbroadcastSequenceConstruct is expected to have at least 1 input. is null. Type info is expected.Input type for input at index Attribute dtype should be of integer type and specify a type.(Optional) The data type of the tensors in the output sequence. The default type is 'float'.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\sequence\defs.ccConstrain input types to any tensor type.Input Sequence and Tensor are expected to have type info. Current type is null. Tensor=Element type of inputs are expected to be the same.output_sequencepositionConstrain position to integral tensor. It must be a scalar(tensor of empty shape).Input Sequence and Tensor are expected to have the same elem type. Sequence=input_sequenceInput type for input at index 0 is null. Type info is expected. sum of split values=Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size=Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.new_axis must be either 0 or 1], Value=Invalid value of attribute 'axis'. Accepted range=[Constrain split size to integral tensor.Constrain output types to all tensor types.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored. expected to have type info expected to be a sequence typeGraph attribute inferencer for "body" not available outputs. Expected Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis.Input 0 expected but not providedExpected a sequence type for input 0 expected but not providedSequenceMap_loop_bodyGraph attribute inferencing returned type information for Invalid ``body`` argument. Expected a graphExpected 1 or more inputs.Expected 1 or more outputs.SequenceMap__seqlen_cond_seqempty_itercount_cond_in_cond_out_inout_sequenceConstrain input types to any sequence type.Constrain to any tensor or sequence type.SequenceMapThe graph to be run for each sample in the sequence(s). It should have as many inputs and outputs as inputs and outputs to the SequenceMap function.additional_inputsAll Tensor, Sequence(Tensor), Optional(Tensor), and Optional(Sequence(Tensor)) types up to IRv4.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\controlflow\old.ccScan 'body' subgraph outputs should all be tensors but output axis value ) is not equal to number of scan inputs (Number of scan input axes specified (All Tensor types up to IRv4. was not a tensor.Scan input was notInt64 tensorAll Tensor types was Loop 'body' subgraph outputs should all be tensors but output ) is not equal to number of scan outputs (Number of scan output axes specified (An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. but subgraphs produce If node has 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.All Tensor and Sequence typesLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output else= then=Mismatched type for output Mismatched tensor element type for output selected_indicesInteger 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.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.Allowed values are 'half_pixel' and 'output_half_pixel'. Use the value 'half_pixel' to pixel shift the input coordinates by -0.5 (the recommended behavior). Use the value 'output_half_pixel' to omit the pixel shift for the input (use this for a backward-compatible behavior).Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.max_output_boxes_per_classC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\defs.ccC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\optional\old.ccInput must be an optional-type value containing an element with type information.OptionalHasElement is expected to have 1 input.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\nn\old.ccSTDX_varianceProcessed_STDEX_squaredX_squaredE_XsquaredDilation value along each spatial axis of filter.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each spatial axis.The storage order of the tensor. 0 is row major, and 1 is column major.dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.If set to nonzero, run spatial batch normalization in test mode, default is 0.is_testThe epsilon value to use to avoid division by zero, default is 1e-5f.saved_meanvar Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain mean and variance types to float tensors. It allows all float type for U.saved_var Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. The ratio of random dropoutConstrain output mask types to boolean tensors.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.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). 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.First input tensor must have rank 3The 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].C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\defs.ccA list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.The 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].Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\quantization\old.ccConstrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Constrain input to integral tensors.Constrain input to float tensors.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\logical\old.ccIf set, defines the broadcast dimensions.Enable broadcastingC:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\generator\old.ccOne of the attributes 'value' or 'sparse_value' must be specified for a Constant node.Attribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\object_detection\old.ccThe sequence output for the hidden is optional if 0. Default 0.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.foward 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 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 C:\a\_work\1\b\RelWithDebInfo\_deps\onnx-src\onnx\defs\rnn\old.cc 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) Input was expected to have tensor or sparse tensor type. Got Element type of tensor or sparse tensor input was unknownInput element type of does not match existing output type of Mismatched map tensor key type:target map type missing key type.target map type missing value type.source map type missing value type.Input was expected to have map type. Got Element type of optional input was unknownValue type of map input was unknownKey type of map input was unknownInput was expected to have sequence type. Got Output was expected to have tensor type. Got Input was expected to have optional type. Got Element type of sequence input was unknown expected to be a 1D tensor but was shape input must be 1D tensorInput was expected to have either tensor, sequence, optional or map type. Got DOne and only one attribute must be set out of expected to have sequence typeElement type of input expected to have optional typeElement type of sequence input Input type was null unknown expected to have tensor or sparse tensor type. Got: declared= expected to have type but instead is null inferred=Mismatch between number of inferred and declared dimensions. inferred= expected to have map typeElement type of optional input Value type of map input Key type of map input source optional type missing element type.target sequence type missing element type.source map type missing key type.target optional type missing element type.Mismatched tensor element type:Mismatched type:source sequence type missing element type.Mismatched sparse tensor element type:The type of tensor: is undefined so it cannot be parsed. does not match the actual sizeCannot parse data from external tensors. Please Data size mismatch. Tensor: expected size . Expected: Actual:load external data into raw data for tensor: ParseData type mismatch for tensor: content is required to be stored in repeated bytes string_data field. raw_data type cannot be string. data type is string. stringgraphdomain ==> Context: data_typeUnrecognized type value case (value_info name: setting data_type field (tensor name: ) to UNDEFINED is not allowedkey_typeelem_type): value_typedouble_dataraw_data) is stored externally and should not have data field.uint64_dataint32_datafloat_dataint64_datastring_data) should be file inside the , but the ') should be stored in , but it doesn't exist or is not accessible.) should be a relative path, but it is an absolute path: Data of TensorProto ( tensor name: ' points outside the directoryLocation of external TensorProto ( tensor name: ) should not be stored in raw_data field) should contain one and only one value field.' instead of 'STRING data (tensor name: TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!values of data_type '' should be stored in field 'Unrecognized data_type (tensor name: ) has values, but NNZ is ) first dimension size does not equal NNZ.] not in sorted order.] out of range.) second dimension size does not match rank of tensor.] out of range [0, Sparse tensor indices (Sparse tensor () index value at position [) dimensions are not positive.) must have a dense-rank > 0) must have rank 1 or 2.) must have INT64 type.sparse_tensor_proto] not in lexicographic sorted order.Sparse tensor values () must have rank 1.utf8str_to_wstring: string is too long for converting to wstring.name' of '' is required but missing.Field 'value_info' is required to be non-empty.) should refer to attribute in parent node.Attribute (name: op_typeWarning: Model contains experimental ops:attr) has no index values.) should not contain more than one value field.type field and data field mismatch in attribute No Op registered for with domain_version of Op registered for is deprecated in domain_version of , type: ) has zero input and zero output.No opset import for domain 'NodeProto (name: Tensor initializers must have a non-empty nameinit in initializer but not in graph input initializer name is not uniqueBad node spec for node. Name: OpType: Graph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.Nodes in a graph must be topologically sorted, however input '' of node: ' has been used as output names multiple times. sparse initializer name is not unique across initializers and sparse_initializersSparse tensor initializers must have a non-empty namename: is not output of any previous nodes.ConstantFillstol argument out of rangeinvalid stol argumentfloatstype_protostringsintopset_importir_versionproducer_versionproducer_namegraphstensorstype_protossparse_tensorsExpected character (line: Unexpected literal type.Integer value expected, but not found.doc_stringmodel_version column: metadata_propsValue expected but not found.Unexpected type in sparse-tensor element type.Expecting primitive type as map key type.Identifier expected but not found.String value expected, but not found.Incomplete string literal.Encountered invalid float literal! and specified value's typeUnhandled type: %dUnexpected attribute type.Mismatch between expected type Error parsing TensorProto (expected a tensor type).Unexpected type.Error parsing TensorProto shape (expected numeric dimension).Error parsing TensorProto (expected a tensor shape).Singleton attribute value cannot be specified as a list.Empty list attribute value requires type annotation.Error context: [ParseError at position . No opset import for domain optype Container for generated shape data cannot be nullptr when enable_data_propagation option is set.Cannot infer type and shape for node name key type mismatch from MapProto. existing=type case mismatch. existing=type case unsupported for symbolic shape inference. inferred=type case unsupported. existing=Cannot use the same name as both a subgraph initializer and subgraph input: Graph has were provided.The number of graph input cannot be smaller than the number of node inputInference error(s): inputs but were provided(op_type:in initializers. Graph initializer names must appear after the actual inputs: , node name: Cannot find missing input: Inferred shape and existing shape differ in rank: (Inferred elem type differs from existing elem type: (Inferred shape and existing shape differ in dimension : ( is out of bounds. does not contain a graph.GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance.unk__Data for input already exists.opaque_typeoptional_typeNOT_SETsparse_tensor_type 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}. axes as an input and attribute cannot be specified at the same time.Constrain input and output types to numeric and Boolean tensors. 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`.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.The input tensor and grid tensor must have the same rank for GridSample. Got input tensor rank: The input tensor and grid tensor ranks must be >= 3. Got input tensor and grid tensor ranks: Got grid tensor rank: Unknown value for `keep_aspect_ratio_policy`: Either `sizes` or `scales` must be provided, but not both of themNumber of elements of input 'sizes' (Providing `scales` is incompatible with a `keep_aspect_ratio_policy` other than "stretch".The last dimension of the grid tensor must be the rank of the grid tensor - 2. Got the last dimension of the grid tensor: Dimension value inferred () is not equal to the existing dim value ( values.'pads' input must be a 1D (shape: [2 * num_axes]) tensor of type int64Pads has incorrect number of values. Expected 2 * values. Got Number of elements of input 'scales' () must be same as rank of input 'X' (Input 'scales' must have float element type.Number of elements of input 'scales' must be same as rank of input 'X'One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_float' expect a float.Attribute 'value_int' expect an integer.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Attribute 'value_string' expect a string.Loop 'body' subgraph outputs should all be tensors or sequences or optionals, but output onnx.StringStringEntryProtoonnx.ModelProtoonnx.GraphProtoonnx.TensorAnnotationonnx.ValueInfoProtoonnx.AttributeProtoonnx.TrainingInfoProtoonnx.NodeProtoonnx.TypeProto.Tensoronnx.TensorShapeProtoonnx.TypeProto.Maponnx.TypeProto.Sequenceonnx.TensorProtoonnx.TensorProto.Segmentonnx.TensorShapeProto.Dimensiononnx.SparseTensorProtoonnx.FunctionProtoonnx.OperatorSetIdProtoonnx.TypeProto.SparseTensoronnx.TypeProto.Optionalonnx.TypeProtoonnx.TypeProto.Opaque   FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSTYPE_PROTOTYPE_PROTOSUNDEFINEDUnknown encoding Invalid RE2: text size: Error reverse compiling 'Error compiling 'pattern too large - compile failedC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\re2.ccError parsing 'program size pattern length bytemap range list count RE2: invalid startpos, endpos pair. [Unexpected re_anchor value: endpos: startpos: SearchNFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencyDFA out of memory: SearchDFA inconsistencyRegexp not destroyed.invalid character classinvalid character class rangeunexpected errorinvalid escape sequenceUnexpected op in Regexp::Equal: no errorC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\regexp.ccBad reference count bad repetition operatorinvalid perl operatorno argument for repetition operatorinvalid repetition sizeunexpected )trailing \missing ]missing )NumCapturesWalker::ShortVisit calledinvalid UTF-8invalid named capture groupWalk NULLStack not empty.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2/walker-inl.hPHX(8should never happenC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\compile.ccMissing case in Compiler: Compiler::Copy called!No ranges in char classunknown round: RE2: unexpected op: RepetitionWalker::ShortVisit calledConcat of AddFoldedRange recurses too much.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\parse.ccLmLoLlLtAnyLuBad hex digit PcBad call to ParseState::ParsePerlFlagsMcNdNlMn in AddToThreadq in stepUnhandled unhandled C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\nfa.ccUnexpected opcode in short circuit: Bad args: nsubmatch=context does not contain text|DeadState in RunStateOnByteunhandled opcode: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\dfa.ccStateSaver failed to restore state.Failed to analyze start state.NULL state in RunStateOnByteUnexpected special state in RunStateOnByteRunStateOnByteUnlocked failed after ResetRunStateOnByteUnlocked failed after ResetCacheC:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\bitstate.ccUnexpected opcode: njob_ = GrowStack() failed: job_.size() = Cannot use SearchOnePass for unanchored matches.C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\onepass.ccUnexpected opcode in IsMatch: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\prog.cc DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledCoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is Case not handled in ComputeSimple: C:\a\_work\1\b\RelWithDebInfo\_deps\re2-src\re2\simplify.ccSimplify case not handled: Malformed repeat <@48,0 xx  hPxPHX(80@ XXhh[:word:][:^word:][:upper:][:^upper:][:space:][:^space:][:punct:][:^punct:] 09AZ__az09AFafAZazAZ[:xdigit:][:^xdigit:]09AZaz !/:@[`{~!~ ~09AZ__az0909az[:alnum:][:^alnum:]\w\W\s\S\d\D[:cntrl:][:^cntrl:][:blank:][:^blank:][:ascii:][:^ascii:][:alpha:][:^alpha:][:print:][:^print:][:lower:][:^lower:][:graph:][:^graph:][:digit:][:^digit:]=P=(p== Ez#X=h= @=(L=@K=K=𬖀=P= =P==Ȇ= 4>(T˒eD @>zv> >Pv >>0= >X>>O|>h>Py[pSp>x>X>h>૖A@A0APAAAxA0Apu8B@BXB й(B  B{d B|A@Ai!`vBBpOpBb|B`  XBxh`BgHBPBO C0O CKvBdC🖀0[ BcBxEBBCd CX`CpC𩖀@CTC$C0C(uCE| C[ CP"MCCCXChCȬd$ iK( P9u "T( p} 0 k]|)(D0D\Xp_@nDv DDCbDІCuCeD؞x opBDxDMD0eXDHhDK8DPD `P|>D>{>u>O> 1CiC>h>p| %>0L?xvT?{ v0@?@`w*D?` ?@8?x?𹖀?ȫ?d?u?K?Ox?P?PX?8h?0(@8u0@K@09f8@H?@vH v?x?[Lh@u p@L_X@d@O P@`}7T@0cO8@xH@@[(h\pS@u@8@y@XO@h@H@X}Ahb ApxA@5w A@MrH@`O@e@O@`AXpAXHAPAh,AP_8AAz$Aeh@e@vhehpd h ghOgvg0ghhhgphXhdh@h@Lh(h@4h(h`_䒀ch`hX_hhЬBalineseBamumArmenianAvestanAnatolian_HieroglyphsArabicAdlamAhomBrailleBugineseBopomofoBrahmiBengaliBhaiksukiBassa_VahBatakChakmaChamCcCfCarianCaucasian_AlbanianBuhidCanadian_AboriginalCuneiformCypriotCopticCsCoCommonCherokeeChorasmianNandinagariNew_Tai_LueMyanmarNabataeanMroMultaniModiMongolianOl_ChikiOld_HungarianNyiakeng_Puachue_HmongOghamNoNushuNewaNkoOld_TurkicOld_UyghurOld_SogdianOld_South_ArabianOld_PermicOld_PersianOld_ItalicOld_North_ArabianPdPePalmyrenePau_Cin_HauOsmanyaPahawh_HmongOriyaOsagePsalter_PahlaviRejangPoPsPhoenicianPiPfPhags_PaSiddhamSignWritingSharadaShavianSaurashtraScRunicSamaritanSoyomboSundaneseSogdianSora_SompengSmSoSinhalaSkTai_VietTakriTai_LeTai_ThamTagalogTagbanwaSyloti_NagriSyriacDuployanEgyptian_HieroglyphsDives_AkuruDograDeseretDevanagariCypro_MinoanCyrillicGranthaGreekGlagoliticGothicEthiopicGeorgianElbasanElymaicHanunooHatranHangulHanifi_RohingyaGurmukhiHanGujaratiGunjala_GondiJavaneseKaithiInscriptional_PahlaviInscriptional_ParthianImperial_AramaicInheritedHebrewHiraganaKhojkiKhudawadiKhitan_Small_ScriptKhmerKayah_LiKharoshthiKannadaKatakanaLinear_BLisuLimbuLinear_ALatinLepchaLLaoManichaeanMarchenMalayalamMandaicMahajaniMakasarLycianLydianMeroitic_HieroglyphsMiaoMende_KikakuiMeroitic_CursiveMedefaidrinMeetei_MayekMasaram_GondiMeDPY0484sz    ?AJVoqPpP=@ptv@ U X _ AZazptvwz}/1VYY` Jnoq/M$$((@X`jp 9 = = P P X a q     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ] ] ` a     : = = N N T V _ a z  023@F@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo1@Q`lnp xPmpt T3EL#MOZ} EHMPWYY[[]]_}q q   !!!! 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