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5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļ[yaW.]O$Iu> /XObject <>>> /BBox [107 199 2375 3403] /Group <> /Length 65>> stream q 2268 0 0 -3204 107 3403 cm 0 0 0 RG 0 0 0 rg /G3 gs /X133 Do Q endstream endobj 135 0 obj <>>> endobj 144 0 obj <> stream xwPy?NdQxb.`C4ACEClzXX6NQT_ $tRȺ}~k߻+֭[=JOOى~~~4hvAA~~Ch>}fDEE޽gElݺgU{r?-3+R!?6n:RSت +LKN~&'aG^\kMl0N+;If.]ٰ{A3yE :mmˆSOԎFu :5MԶR"okK[B/24wq+űUTAsڱ~j6)-> xyz͝3{NБ z}pr9X ݐ *а*-ӲkRUc E[In8\5RJEG/?lBԠ 48UCs[t>8N[٨+ ,aFCBJUlPzwPTV튫Nz6riFv^ 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5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļk// ʠg (Ӫfr1ϲs 7* 5(Nř>KV2ܪ,p:g#'1/)ZrYŠ4Y~ļ[yaW.]O$Iu> /XObject <>>> /BBox [107 199 2375 3403] /Group <> /Length 65>> stream q 2268 0 0 -3204 107 3403 cm 0 0 0 RG 0 0 0 rg /G3 gs /X147 Do Q endstream endobj 149 0 obj <>>> endobj 156 0 obj <> stream xw\Wy"Mc%k {4jDc,T^P(R4**ȣR}A@6R~γ3;m;xwgsϜ{ܹG @xzzAUQK0Hrrr*%K}[ PO+ lj DA cO2mVU|zwŐC8Z=W1<9mm:iꋌgWnuֹ.LZ6=6Xk^$U}Ъ/ZO8ӧxuiNtc(n˿fZ(<7=?eAMx] kE,ut%V,.kY˝ӢXp3ܦt=V>Uzhr ]! @Dr oYv1<_U* 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Jr;BRdU!D8ƙ$FG1,!\ϑQ*Pÿ%Ø'.̠?G&'}¯;$P3uxFviP\zc;uK ޲^pAZISoFÀtd`$'[1'j裫 e2)w͗rS8ey&L-Yt_7 ߰~bSqүY/~NC,^QܦO,ܪd{LZ{+d*)!VvܳlSBR1+Ce"ԩ\9bΙNNXn9x_l_ M)mi+4PwVVc xM} endstream endobj 1435 0 obj <> stream x]Pj0 +tli&0\>tdF6s{$x{=q.|՘aaI;y7TLd\^猓!0!'~iݫ =Ov7%;NHֶp`\^M|3rHpFS_768 DӴ eHnS6s ܽT6-a.)!Z(=C1Ģ*bn endstream endobj 107 0 obj <> /CharProcs <>>> endobj 1436 0 obj <> stream xU1 1 {b?`XIw!FJ(D#FUtᐳvi{?fG.S im<´P> stream xuVK+7 )x&A0oJr?' EŢ@Ά&/DjM;,0&L?.ߟI'Ʋ&>~D%bg˃0ѸYp Q=>VqB[8+DI*Vl/UNm)4b2٨"_=! h3'3WT$7L=O1ӹ{X`b#53{,%T Q%hA1ʜDSbH$[f(uo/I}#p'`<*: ? KzXo5OX; H }uIv|:RJaozÐV<5Krn,c~7+tfdg 6+Qsa.X&q,#kM8Beg 1&O"*; KeʟQrZϬh̳`Ɩ x5^-о /J!.EV yJTzB:Ji5 ]n cX#gߐkܽH>9:;TMd$0s0Fg/cͤao|/g w ~$#w4_^prT-Fe6,m=y4IC^I?Gh-P1?4z^ڬ" endstream endobj 1438 0 obj <> stream x]Pj0 +tlICvc^?̰qa/tOz'.Ug)XFO.d< ۼl"Rz[2Ί8% . xd-9L&8ܥ>2qFа#jN!e]?nlpDc1@\.CrvbILܾ-aǮ)!Z(=C1Ģ*On endstream endobj 108 0 obj <> /CharProcs <>>> endobj 1439 0 obj <> stream xU1 1 {b?`XIw!FJ(D#FUtᐳvi{?fG.S im<´P> stream xm;0Sp"HRᣰ25+ƳLswQrVJƗƳĄ| 8G endstream endobj 1441 0 obj <> stream x]Pj0 +tlizFJc^?̰qa7tOz'ީ"`5f=KXEp-8ojwꪷ%h L_r€Goa4#zg wo&WI9vwEÍ h,&C24DKv0oHm>Jkj/n" endstream endobj 109 0 obj <> /CharProcs <>>> endobj 1442 0 obj <> stream xU1 1 {b?`XIw!FJ(D#FUtᐳvi{?fG.S im<´P> stream xmSK0 ?àovL.f,)&" `YI?<[ fcrPwtń&\PU 6oMLJB3:}jrktH^B ‰Y,ӥQ> stream xmUI8  {<!%e7!,. ф'ӼR}V,b(yy:py lO@}z $s5~eؼR%멃 X@C pTyIu&R >ubo]J-~);QDloGX&0By:Ԏ%ޛcNA L!€E8o<}J:zCkVyrLҩ iݓAV[];ԖD5 |3h;Zm+1! 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kj|qS54a5gClZj(ġ(-<"ikt#r[g?Z[] endstream endobj 861 0 obj << /Filter /FlateDecode /Length 225 >> stream x=QA0z{ZA{ 2a[x(jg=ԥ{\_bL,2,$|אpqYpZb Yi%&F~\!50L t &7Wi4H`V4'*GI;uCݻw LRӾJNmoivrScZ_3+O5S endstream endobj 862 0 obj << /Filter /FlateDecode /Length 353 >> stream x5q0 {O|'kr)|WM AZK-z%b/F{PBlD6S\6F-Q{V#;N/>tg_uzmaw%E+QEɦ:kR%Zh#s~BxQK`5F*> stream x5QKnD1@M8ϫF 1ذė<y߲bzۉog#TR+E )t' Påe_dP.7f0;00H.CEVjQ $ȶM&$?yS*A:S)އ3Q:lנM[ԪR}:l%Aqh8-j"?kYEm+ q&(7 ,F/4.*!eVl쇫 V^b8)m45j" endstream endobj 864 0 obj << /Filter /FlateDecode /Length 212 >> stream x=P0 5Fir)ۀ$M!ůDUxXP6CtH5sFOɉk4ͳHat!ˡ*tYL=,xV;A_zCL !T C.](db syp.Gy~ld{n+5L endstream endobj 865 0 obj << /Filter /FlateDecode /Length 427 >> stream x=Kv0D^KIZe\  sɐY**ٖ+-xH 3h~sW-ߒcʤtݫߦXJUǑ%>!L>s)܇2)^vꊿ2eb)H/%)E|*6I^Ve9aQ3ƒjQfiftMH9D 2 ڠ,Ri!(侾.)~>FD+yه,MZh2϶l~#&*;]neF8:yY?ՂeuPYσ5|p2uVd6=[R| endstream endobj 866 0 obj << /Filter /FlateDecode /Length 246 >> stream x5QInE! s!3(.ʆR)!_%s+ŀqm;95*#jd6?Z}r!O8T./UjP3 .8[j8h#HMnŒ> stream x=A0C9G0}Eyrwϗ1HvaZ˺c[Gۿf=׬ගr2,pYP{yK Zgn%, pnLN TDYHVB>~ؒс1`ˊp SK,|Ĥ&2FwL$d(c3Mݶe7}#zibUS@/R|j Ip&Q(àkgK-ŭ:!ox~زr-aaUbɅS6ܒh>25sD,S칾  endstream endobj 868 0 obj << /Filter /FlateDecode /Length 231 >> stream xUQAn1~B@LUakD=TZ-ǀa ɵQ3`+$x[F@xlG^<4c!0'U;ʜGibbRC抜JyI1(v;DqPJ2;[mދe𳧳o5<y- GhEx;.M2*=;yEHݒ39bߟOfT endstream endobj 869 0 obj << /Filter /FlateDecode /Length 259 >> stream x=Kn0 C9`ep].=:c=NkoeS{׫s\0OC$W` ԭVp-WOdq hI<R@-㫛> stream x5SKr0\ 3mNV"-1HRբ!ZmWo-+S?^qIG/I=Ru u$KLi+F{nz)iTo]Xn1 H3F~& -dKt؆d8`~qߓ9yt!}ϐ]VCHPFR?9]s(~pp6f8T!hݨ ՟ Zp"uϷ;'Zo@|]=Nb:%8DB61+:3l'QlT@"h &?334XZ 4 %XQ+׋̋kF QB~ɚmU4lq{2FqWд6%tM#N4r5sʝȨhǩo_~u endstream endobj 871 0 obj << /Filter /FlateDecode /Length 441 >> stream x=IPDy .gyUEzcpUٰ\>VVkřʾ_Xc~}^~|ʼ]Yx(U >Zm1͝1;Â9cl0$7myYԵ 1h5=_ :Nj@#hEgY-4Oԡc{6 P7q"ڀ9nNt6%:smFdGq)h^ _mt>^6U+SVrgֆ7 /lڞG8N2~2KN`O[pمafw;"*v $xCq["{H[њ8i=\$!Wğߖ ~ b_GD@^Jח}66nOFs6+XyWA-à7Vg: I endstream endobj 872 0 obj << /Filter /FlateDecode /Length 256 >> stream x=QIn1+J@3UךCb`ÈXOl,xnzk<p_X=:ܗj@'2CiDs4O-Jkm`V)dSzg5GRu)؁jj[d\P)80]ҷ{xC> [u"6HYHIl|ԡ}*6eKZg[Loub>ȖlmR>2@e 4\ endstream endobj 873 0 obj << /Filter /FlateDecode /Length 419 >> stream x5IADu .`)r:O[-/(As ~[U~7,3}L+m_ֱ"~{}ۊK'Ek&7ɑe-*nV[d+㵰"#oگաb@Vꕠhhՙ:(;-Va(k=W2A M3mZiY[#ӋG+ EܳT3JhlU峋6G+R`Z67<IZ:^s9ۂr@p5 5]=X{YLJ0\.@-dOd5-ǞA6T?Ѥ5='(ڙO;D L(45<В%%Etۼwet@79{dRwE}޾l_ endstream endobj 874 0 obj << /Filter /FlateDecode /Length 93 >> stream xM;0D# 0q,[a߰ˢ@N-{\a4 kt:,KΫE>?2&KlM53x(" endstream endobj 875 0 obj << /Filter /FlateDecode /Length 56 >> stream x366V0P01T0P04V023TH1X\00 * <*++ "E endstream endobj 876 0 obj << /Filter /FlateDecode /Length 262 >> stream x5PKnD1 H82Ecޣ+^`:=rK/(=,=fv!< сԀetjJ<-]>;y> Â`/ endstream endobj 877 0 obj << /Filter /FlateDecode /Length 183 >> stream xMPA0 ~B6M !m縮[@5رqтW=_jv|Y )W$ll$_ J20L~ɖ&40Y $S65@wl}dnlkJ&<Z}~9)rKYhP$'s<>MB endstream endobj 878 0 obj << /Filter /FlateDecode /Length 281 >> stream x=Ir@C>G0COI*sm?Yt-ڙiMN汚}3:,ɞpL\zŏ+,o:FY[U$ixg#3\}7lnqfGzU4dбAXApW[krs~]ڙl ~Nwi/Q0zB;wX28Ank/P~p@qƘPE{掤1,%LVĒ·ucVKDroPL+qX{ȥ[uW(sKBqxFj endstream endobj 879 0 obj << /Filter /FlateDecode /Length 353 >> stream x5R9n1 @-g mH9)$%G b&r |>4Qx?1q&/^*ȱz<> stream x=K]ACwl Rz^ʠiIF (mja*퇻r[{ONl~jLV%c+>O:ؼ6w҉\6k[xm~ՙt|+|]LaQcq#{v̦^A:n q;dajXRT) ˮ˪FKD,I|~O:ӓiڈ+ԁjX"§P?bt).wc'erK*>åIi&HP oiv*rIñc_NYDzk"g7̷7 =:p쁝 endstream endobj 881 0 obj << /Filter /FlateDecode /Length 17 >> stream x325P@C4M endstream endobj 882 0 obj << /Filter /FlateDecode /Length 378 >> stream x-R9n1 @ݶ߳Ab6͈c]R42Yҫ۞2h{HmVbq"Clz|Ǡ-Q~=BekJ q3b.TxR{\kFdm̑v(f xku-)bjߴ)-{ bz/- F=e&EcD3&/Bh 0 IuI EB\\6m9F XVxj*[fSp0G~1AEj]˼SpXxp }kM9P;Ď=dET83aTaSM簜'gтU^d Ɣρ 8 endstream endobj 883 0 obj << /Filter /FlateDecode /Length 258 >> stream x=Q;r1} y^&bs6;+D ~<_~2qAQ,|X} |Ɇ[_ȰnE ځ͈f#[?KJ.WD%FP44Yyȳ<: ӅX!.wZűL 0Δ#Jϋ[o9? 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C{N##>f2)6oclRX ڂ肇%gc6n5uVhR}ith6s+ fz:rCp_b9S6;ܬ~+UaiYK f@ S9ګ=? : endstream endobj 1139 0 obj << /Filter /FlateDecode /Length 83 >> stream xE 0D{`~&>Q޿ %n'=\ )n3C ,]q0 \GPaվRBf endstream endobj 1140 0 obj << /Filter /FlateDecode /Length 239 >> stream xMPm1 50<C hK[e#9oɺCNinͯ vPT%J\n4I/8k{Yx)=2f90=NƯ;$B %+ۘRN-{|=ލF-j80SѕqCgGW4 zB59p`˳Re]:4J(^7 endstream endobj 1141 0 obj << /Filter /FlateDecode /Length 150 >> stream x=O90 ~ uXޓ"xȲc1I4@,#zB0,p6IAVIW>[L!^H<MdLjR[uU$] cp=a5z0cGUKnv}`0] endstream endobj 1142 0 obj << /Filter /FlateDecode /Length 151 >> stream x5 0 C ϲ$#I9P7OhL 3Ke> stream xE91s'H\˵G4Mzh>Fk,JDPc;(HLHcH#X]T*Ri¿ZDpTO\M1T;I^EZ1D0 oE&پ` 8 L7 endstream endobj 1144 0 obj << /Filter /FlateDecode /Length 320 >> stream x5RKn1)@O󪪋okL0`)/YK%ۥ[%BޏR-G- Q=X/":pJWIj3 P@ƻb2R$siqr&'=YߓKMNI]IJD}B<>G1ɀ6m&%:fU`)W l[00Sbbe5ˆQ]pwhcG i8^8^9c, ch&zОaZT| endstream endobj 1145 0 obj << /Filter /FlateDecode /Length 18 >> stream x36P0C4R endstream endobj 1146 0 obj << /Filter /FlateDecode /Length 133 >> stream xEK!Dq2s퀝nc=T=.k!񷩖EY>TzRV!ܠ,I*!P-en6 S0l_["u?*/ȥֻޢ1 endstream endobj 1147 0 obj << /Filter /FlateDecode /Length 251 >> stream x-QIrA+fc+ @tZ⠌ ,W呫h31~+mX@T IcP5vzq ְ̓ f)1ge]=Aɬ\%La#g?2.s=7 FXanV34H!AF6r/tU1s-m_M8kX]q.'/6Qi@HnsxK{T`śn@6߼_`[ endstream endobj 1148 0 obj << /Filter /FlateDecode /Length 89 >> stream x5M0 =GHQ[4Nk/!/ *HtD g$ï,ms%YLAo?ݎ endstream endobj 1149 0 obj << /Filter /FlateDecode /Length 141 >> stream x=0C @)|Oj#K zcÆ`%Tk@%7ș`Gzb8\f}B%h{Siܦq5)ꜣg44s {Sw. endstream endobj 1150 0 obj << /Filter /FlateDecode /Length 215 >> stream x5Q9! @$/xO(͌V\!R!S>z.j4疴-s/fi<CѨQCԥS(>nc! b(,AͩEm'5^ޗSƖV8=O&t2ԫ5 E"<Z42j;0NqdJoWGT endstream endobj 1151 0 obj << /Filter /FlateDecode /Length 81 >> stream xMͻ О)<>Q"ٿ t'` nqX ;S}PsDX3%ڄ_0ØXȶ7Wr endstream endobj 1152 0 obj << /Filter /FlateDecode /Length 61 >> stream x355W0PF F )\@>ehifY@Has`zr2 endstream endobj 1153 0 obj << /Filter /FlateDecode /Length 90 >> stream x= 0CLSTU׆|z[ȸ  څo'u`]^Bd;Jf&$qD;MJ endstream endobj 1154 0 obj << /Filter /FlateDecode /Length 232 >> stream x=Kr! CBG> stream x=Kn1 C>.ٞ('%EjLYS4p;lffZb|F]Y'f:Q96M,.x&[?Р5. 7tWe)4c{2jL]lR{<^DUG"cA JhZwEA{ jȲ;)ftR.n!kB+%H؆3r'hލKh!n.`S:EbdNriUNPӹý lEC)s_ klxJ&KVۨN<Zt endstream endobj 1156 0 obj << /Filter /FlateDecode /Length 249 >> stream x=P;D! 9/$#pF-f߮)PL~3$G 1%Bn CR zt6:3?a7cE1t=&9 seVH'"3)*{x,6['= RRɥ?mʔ :f,dM8˻IR2v"}<ȣ:dMά#Oۙp_dNt΃96Mi*/RZ~ugdZ" endstream endobj 1157 0 obj << /Filter /FlateDecode /Length 395 >> stream x=RKn@\yRUݼokCR*10}ʐK]qɷ^[<|.[Zy%>ܗ!]t.8G 2*DͪѡBN }9/װ =2A$)BnQAaPYQ2jo c BmH@DTg$gb`ѲTD{LjΨD>OM(L-VnS_|t*4U Xy9Hl!:n32`K9`GYutpL~OtZur@MAF2>)z,F3ar4k"X"bDls=L9l֡33*!ںj@vp?3m endstream endobj 1158 0 obj << /Filter /FlateDecode /Length 94 >> stream xE TA O&2|` yF&^XJcP@r-p'TwiIU)ܤyg&yc c#< endstream endobj 1159 0 obj << /Filter /FlateDecode /Length 341 >> stream xERKnD1ۿSpHᗐ󴪺[L `lxSdʰ%#$הO}"C4r$"I vkK쑨FZѥ͢Q9{z\H!P,er %A[w+ak.e2\}b.6)hUI£znvlU30r%[}]E+@QY@v1Hpp<řpK8@z%/W ud/žBw^dí> stream x363T0P0T026Q064bC.@rs`r22 endstream endobj 1161 0 obj << /Filter /FlateDecode /Length 72 >> stream x32P0P4& f )\@B.H  %g m DDJ% endstream endobj 1162 0 obj << /Filter /FlateDecode /Length 83 >> stream x=90ОS#"qxV0z!8`ʺtSƜ xJM zԴԾhKՉ? endstream endobj 1163 0 obj << /Filter /FlateDecode /Length 163 >> stream xE;! C{N##>f2)6oclRX ڂ肇%gc6n5uVhR}ith6s+ fz:rCp_b9S6;ܬ~+UaiYK f@ S9ګ=? : endstream endobj 1164 0 obj << /Filter /FlateDecode /Length 83 >> stream xE 0D{`~&>Q޿ %n'=\ )n3C ,]q0 \GPaվRBf endstream endobj 1165 0 obj << /Filter /FlateDecode /Length 239 >> stream xMPm1 50<C hK[e#9oɺCNinͯ vPT%J\n4I/8k{Yx)=2f90=NƯ;$B %+ۘRN-{|=ލF-j80SѕqCgGW4 zB59p`˳Re]:4J(^7 endstream endobj 1166 0 obj << /Filter /FlateDecode /Length 150 >> stream x=O90 ~ uXޓ"xȲc1I4@,#zB0,p6IAVIW>[L!^H<MdLjR[uU$] cp=a5z0cGUKnv}`0] endstream endobj 1167 0 obj << /Filter /FlateDecode /Length 151 >> stream x5 0 C ϲ$#I9P7OhL 3Ke> stream xE91s'H\˵G4Mzh>Fk,JDPc;(HLHcH#X]T*Ri¿ZDpTO\M1T;I^EZ1D0 oE&پ` 8 L7 endstream endobj 1169 0 obj << /Filter /FlateDecode /Length 320 >> stream x5RKn1)@O󪪋okL0`)/YK%ۥ[%BޏR-G- Q=X/":pJWIj3 P@ƻb2R$siqr&'=YߓKMNI]IJD}B<>G1ɀ6m&%:fU`)W l[00Sbbe5ˆQ]pwhcG i8^8^9c, ch&zОaZT| endstream endobj 1170 0 obj << /Filter /FlateDecode /Length 18 >> stream x36P0C4R endstream endobj 1171 0 obj << /Filter /FlateDecode /Length 133 >> stream xEK!Dq2s퀝nc=T=.k!񷩖EY>TzRV!ܠ,I*!P-en6 S0l_["u?*/ȥֻޢ1 endstream endobj 1172 0 obj << /Filter /FlateDecode /Length 251 >> stream x-QIrA+fc+ @tZ⠌ ,W呫h31~+mX@T IcP5vzq ְ̓ f)1ge]=Aɬ\%La#g?2.s=7 FXanV34H!AF6r/tU1s-m_M8kX]q.'/6Qi@HnsxK{T`śn@6߼_`[ endstream endobj 1173 0 obj << /Filter /FlateDecode /Length 89 >> stream x5M0 =GHQ[4Nk/!/ *HtD g$ï,ms%YLAo?ݎ endstream endobj 1174 0 obj << /Filter /FlateDecode /Length 141 >> stream x=0C @)|Oj#K zcÆ`%Tk@%7ș`Gzb8\f}B%h{Siܦq5)ꜣg44s {Sw. endstream endobj 1175 0 obj << /Filter /FlateDecode /Length 215 >> stream x5Q9! @$/xO(͌V\!R!S>z.j4疴-s/fi<CѨQCԥS(>nc! b(,AͩEm'5^ޗSƖV8=O&t2ԫ5 E"<Z42j;0NqdJoWGT endstream endobj 1176 0 obj << /Filter /FlateDecode /Length 81 >> stream xMͻ О)<>Q"ٿ t'` nqX ;S}PsDX3%ڄ_0ØXȶ7Wr endstream endobj 1177 0 obj << /Filter /FlateDecode /Length 61 >> stream x355W0PF F )\@>ehifY@Has`zr2 endstream endobj 1178 0 obj << /Filter /FlateDecode /Length 90 >> stream x= 0CLSTU׆|z[ȸ  څo'u`]^Bd;Jf&$qD;MJ endstream endobj 1179 0 obj << /Filter /FlateDecode /Length 232 >> stream x=Kr! 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C{N##>f2)6oclRX ڂ肇%gc6n5uVhR}ith6s+ fz:rCp_b9S6;ܬ~+UaiYK f@ S9ګ=? : endstream endobj 1190 0 obj << /Filter /FlateDecode /Length 83 >> stream xE 0D{`~&>Q޿ %n'=\ )n3C ,]q0 \GPaվRBf endstream endobj 1191 0 obj << /Filter /FlateDecode /Length 239 >> stream xMPm1 50<C hK[e#9oɺCNinͯ vPT%J\n4I/8k{Yx)=2f90=NƯ;$B %+ۘRN-{|=ލF-j80SѕqCgGW4 zB59p`˳Re]:4J(^7 endstream endobj 1192 0 obj << /Filter /FlateDecode /Length 150 >> stream x=O90 ~ uXޓ"xȲc1I4@,#zB0,p6IAVIW>[L!^H<MdLjR[uU$] cp=a5z0cGUKnv}`0] endstream endobj 1193 0 obj << /Filter /FlateDecode /Length 151 >> stream x5 0 C ϲ$#I9P7OhL 3Ke> stream xE91s'H\˵G4Mzh>Fk,JDPc;(HLHcH#X]T*Ri¿ZDpTO\M1T;I^EZ1D0 oE&پ` 8 L7 endstream endobj 1195 0 obj << /Filter /FlateDecode /Length 320 >> stream x5RKn1)@O󪪋okL0`)/YK%ۥ[%BޏR-G- Q=X/":pJWIj3 P@ƻb2R$siqr&'=YߓKMNI]IJD}B<>G1ɀ6m&%:fU`)W l[00Sbbe5ˆQ]pwhcG i8^8^9c, ch&zОaZT| endstream endobj 1196 0 obj << /Filter /FlateDecode /Length 18 >> stream x36P0C4R endstream endobj 1197 0 obj << /Filter /FlateDecode /Length 133 >> stream xEK!Dq2s퀝nc=T=.k!񷩖EY>TzRV!ܠ,I*!P-en6 S0l_["u?*/ȥֻޢ1 endstream endobj 1198 0 obj << /Filter /FlateDecode /Length 340 >> stream x5R9n1 } n H qtR;ZTvˇTt7we ɫRHV!25gRqɂ?å X#D2f[~ix+-XR8y- V~AG}TX IpPvH 9{,vQ3(1EA%9źtб،7:*[ZYc>,2zXtǃ 4 4e9QVxC 1! 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C{N##>f2)6oclRX ڂ肇%gc6n5uVhR}ith6s+ fz:rCp_b9S6;ܬ~+UaiYK f@ S9ګ=? : endstream endobj 1217 0 obj << /Filter /FlateDecode /Length 83 >> stream xE 0D{`~&>Q޿ %n'=\ )n3C ,]q0 \GPaվRBf endstream endobj 1218 0 obj << /Filter /FlateDecode /Length 239 >> stream xMPm1 50<C hK[e#9oɺCNinͯ vPT%J\n4I/8k{Yx)=2f90=NƯ;$B %+ۘRN-{|=ލF-j80SѕqCgGW4 zB59p`˳Re]:4J(^7 endstream endobj 1219 0 obj << /Filter /FlateDecode /Length 150 >> stream x=O90 ~ uXޓ"xȲc1I4@,#zB0,p6IAVIW>[L!^H<MdLjR[uU$] cp=a5z0cGUKnv}`0] endstream endobj 1220 0 obj << /Filter /FlateDecode /Length 151 >> stream x5 0 C ϲ$#I9P7OhL 3Ke> stream xE91s'H\˵G4Mzh>Fk,JDPc;(HLHcH#X]T*Ri¿ZDpTO\M1T;I^EZ1D0 oE&پ` 8 L7 endstream endobj 1222 0 obj << /Filter /FlateDecode /Length 320 >> stream x5RKn1)@O󪪋okL0`)/YK%ۥ[%BޏR-G- Q=X/":pJWIj3 P@ƻb2R$siqr&'=YߓKMNI]IJD}B<>G1ɀ6m&%:fU`)W l[00Sbbe5ˆQ]pwhcG i8^8^9c, ch&zОaZT| endstream endobj 1223 0 obj << /Filter /FlateDecode /Length 18 >> stream x36P0C4R endstream endobj 1224 0 obj << /Filter /FlateDecode /Length 133 >> stream xEK!Dq2s퀝nc=T=.k!񷩖EY>TzRV!ܠ,I*!P-en6 S0l_["u?*/ȥֻޢ1 endstream endobj 1225 0 obj << /Filter /FlateDecode /Length 340 >> stream x5R9n1 } n H qtR;ZTvˇTt7we ɫRHV!25gRqɂ?å X#D2f[~ix+-XR8y- V~AG}TX IpPvH 9{,vQ3(1EA%9źtб،7:*[ZYc>,2zXtǃ 4 4e9QVxC 1! 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CBG> stream x=Kn1 C>.ٞ('%EjLYS4p;lffZb|F]Y'f:Q96M,.x&[?Р5. 7tWe)4c{2jL]lR{<^DUG"cA JhZwEA{ jȲ;)ftR.n!kB+%H؆3r'hލKh!n.`S:EbdNriUNPӹý lEC)s_ klxJ&KVۨN<Zt endstream endobj 1235 0 obj << /Filter /FlateDecode /Length 249 >> stream x=P;D! 9/$#pF-f߮)PL~3$G 1%Bn CR zt6:3?a7cE1t=&9 seVH'"3)*{x,6['= RRɥ?mʔ :f,dM8˻IR2v"}<ȣ:dMά#Oۙp_dNt΃96Mi*/RZ~ugdZ" endstream endobj 1236 0 obj << /Filter /FlateDecode /Length 395 >> stream x=RKn@\yRUݼokCR*10}ʐK]qɷ^[<|.[Zy%>ܗ!]t.8G 2*DͪѡBN }9/װ =2A$)BnQAaPYQ2jo c BmH@DTg$gb`ѲTD{LjΨD>OM(L-VnS_|t*4U Xy9Hl!:n32`K9`GYutpL~OtZur@MAF2>)z,F3ar4k"X"bDls=L9l֡33*!ںj@vp?3m endstream endobj 1237 0 obj << /Filter /FlateDecode /Length 249 >> stream xMQI0 @!^CC9 X 1,=!s7~ٻYz"SQR.bB]ϡ=kY9,s3c}I2!N uZ¸kbZvBzM;" 2;-+{n?R\ endstream endobj 1238 0 obj << /Filter /FlateDecode /Length 94 >> stream xE TA O&2|` yF&^XJcP@r-p'TwiIU)ܤyg&yc c#< endstream endobj 1239 0 obj << /Filter /FlateDecode /Length 341 >> stream xERKnD1ۿSpHᗐ󴪺[L `lxSdʰ%#$הO}"C4r$"I vkK쑨FZѥ͢Q9{z\H!P,er %A[w+ak.e2\}b.6)hUI£znvlU30r%[}]E+@QY@v1Hpp<řpK8@z%/W ud/žBw^dí> stream x363T0P0T026Q064bC.@rs`r22 endstream endobj 1241 0 obj << /Filter /FlateDecode /Length 72 >> stream x32P0P4& f )\@B.H  %g m DDJ% endstream endobj 1242 0 obj << /Filter /FlateDecode /Length 83 >> stream x=90ОS#"qxV0z!8`ʺtSƜ xJM zԴԾhKՉ? endstream endobj 1243 0 obj << /Filter /FlateDecode /Length 163 >> stream xE;! C{N##>f2)6oclRX ڂ肇%gc6n5uVhR}ith6s+ fz:rCp_b9S6;ܬ~+UaiYK f@ S9ګ=? : endstream endobj 1244 0 obj << /Filter /FlateDecode /Length 83 >> stream xE 0D{`~&>Q޿ %n'=\ )n3C ,]q0 \GPaվRBf endstream endobj 1245 0 obj << /Filter /FlateDecode /Length 239 >> stream xMPm1 50<C hK[e#9oɺCNinͯ vPT%J\n4I/8k{Yx)=2f90=NƯ;$B %+ۘRN-{|=ލF-j80SѕqCgGW4 zB59p`˳Re]:4J(^7 endstream endobj 1246 0 obj << /Filter /FlateDecode /Length 150 >> stream x=O90 ~ uXޓ"xȲc1I4@,#zB0,p6IAVIW>[L!^H<MdLjR[uU$] cp=a5z0cGUKnv}`0] endstream endobj 1247 0 obj << /Filter /FlateDecode /Length 151 >> stream x5 0 C ϲ$#I9P7OhL 3Ke> stream xE91s'H\˵G4Mzh>Fk,JDPc;(HLHcH#X]T*Ri¿ZDpTO\M1T;I^EZ1D0 oE&پ` 8 L7 endstream endobj 1249 0 obj << /Filter /FlateDecode /Length 320 >> stream x5RKn1)@O󪪋okL0`)/YK%ۥ[%BޏR-G- Q=X/":pJWIj3 P@ƻb2R$siqr&'=YߓKMNI]IJD}B<>G1ɀ6m&%:fU`)W l[00Sbbe5ˆQ]pwhcG i8^8^9c, ch&zОaZT| endstream endobj 1250 0 obj << /Filter /FlateDecode /Length 18 >> stream x36P0C4R endstream endobj 1251 0 obj << /Filter /FlateDecode /Length 133 >> stream xEK!Dq2s퀝nc=T=.k!񷩖EY>TzRV!ܠ,I*!P-en6 S0l_["u?*/ȥֻޢ1 endstream endobj 1252 0 obj << /Filter /FlateDecode /Length 340 >> stream x5R9n1 } n H qtR;ZTvˇTt7we ɫRHV!25gRqɂ?å X#D2f[~ix+-XR8y- V~AG}TX IpPvH 9{,vQ3(1EA%9źtб،7:*[ZYc>,2zXtǃ 4 4e9QVxC 1! 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CBG> stream x=Kn1 C>.ٞ('%EjLYS4p;lffZb|F]Y'f:Q96M,.x&[?Р5. 7tWe)4c{2jL]lR{<^DUG"cA JhZwEA{ jȲ;)ftR.n!kB+%H؆3r'hލKh!n.`S:EbdNriUNPӹý lEC)s_ klxJ&KVۨN<Zt endstream endobj 1262 0 obj << /Filter /FlateDecode /Length 249 >> stream x=P;D! 9/$#pF-f߮)PL~3$G 1%Bn CR zt6:3?a7cE1t=&9 seVH'"3)*{x,6['= RRɥ?mʔ :f,dM8˻IR2v"}<ȣ:dMά#Oۙp_dNt΃96Mi*/RZ~ugdZ" endstream endobj 1263 0 obj << /Filter /FlateDecode /Length 395 >> stream x=RKn@\yRUݼokCR*10}ʐK]qɷ^[<|.[Zy%>ܗ!]t.8G 2*DͪѡBN }9/װ =2A$)BnQAaPYQ2jo c BmH@DTg$gb`ѲTD{LjΨD>OM(L-VnS_|t*4U Xy9Hl!:n32`K9`GYutpL~OtZur@MAF2>)z,F3ar4k"X"bDls=L9l֡33*!ںj@vp?3m endstream endobj 1264 0 obj << /Filter /FlateDecode /Length 249 >> stream xMQI0 @!^CC9 X 1,=!s7~ٻYz"SQR.bB]ϡ=kY9,s3c}I2!N uZ¸kbZvBzM;" 2;-+{n?R\ endstream endobj 1265 0 obj << /Filter /FlateDecode /Length 94 >> stream xE TA O&2|` yF&^XJcP@r-p'TwiIU)ܤyg&yc c#< endstream endobj 1266 0 obj << /Filter /FlateDecode /Length 341 >> stream xERKnD1ۿSpHᗐ󴪺[L `lxSdʰ%#$הO}"C4r$"I vkK쑨FZѥ͢Q9{z\H!P,er %A[w+ak.e2\}b.6)hUI£znvlU30r%[}]E+@QY@v1Hpp<řpK8@z%/W ud/žBw^dí> stream x363T0P0T026Q064bC.@rs`r22 endstream endobj 1268 0 obj << /Filter /FlateDecode /Length 72 >> stream x32P0P4& f )\@B.H  %g m DDJ% endstream endobj 1269 0 obj << /Filter /FlateDecode /Length 83 >> stream x=90ОS#"qxV0z!8`ʺtSƜ xJM zԴԾhKՉ? endstream endobj 1270 0 obj << /Filter /FlateDecode /Length 163 >> stream xE;! C{N##>f2)6oclRX ڂ肇%gc6n5uVhR}ith6s+ fz:rCp_b9S6;ܬ~+UaiYK f@ S9ګ=? : endstream endobj 1271 0 obj << /Filter /FlateDecode /Length 83 >> stream xE 0D{`~&>Q޿ %n'=\ )n3C ,]q0 \GPaվRBf endstream endobj 1272 0 obj << /Filter /FlateDecode /Length 239 >> stream xMPm1 50<C hK[e#9oɺCNinͯ vPT%J\n4I/8k{Yx)=2f90=NƯ;$B %+ۘRN-{|=ލF-j80SѕqCgGW4 zB59p`˳Re]:4J(^7 endstream endobj 1273 0 obj << /Filter /FlateDecode /Length 150 >> stream x=O90 ~ uXޓ"xȲc1I4@,#zB0,p6IAVIW>[L!^H<MdLjR[uU$] cp=a5z0cGUKnv}`0] endstream endobj 1274 0 obj << /Filter /FlateDecode /Length 151 >> stream x5 0 C ϲ$#I9P7OhL 3Ke> stream xE91s'H\˵G4Mzh>Fk,JDPc;(HLHcH#X]T*Ri¿ZDpTO\M1T;I^EZ1D0 oE&پ` 8 L7 endstream endobj 1276 0 obj << /Filter /FlateDecode /Length 320 >> stream x5RKn1)@O󪪋okL0`)/YK%ۥ[%BޏR-G- Q=X/":pJWIj3 P@ƻb2R$siqr&'=YߓKMNI]IJD}B<>G1ɀ6m&%:fU`)W l[00Sbbe5ˆQ]pwhcG i8^8^9c, ch&zОaZT| endstream endobj 1277 0 obj << /Filter /FlateDecode /Length 18 >> stream x36P0C4R endstream endobj 1278 0 obj << /Filter /FlateDecode /Length 133 >> stream xEK!Dq2s퀝nc=T=.k!񷩖EY>TzRV!ܠ,I*!P-en6 S0l_["u?*/ȥֻޢ1 endstream endobj 1279 0 obj << /Filter /FlateDecode /Length 340 >> stream x5R9n1 } n H qtR;ZTvˇTt7we ɫRHV!25gRqɂ?å X#D2f[~ix+-XR8y- V~AG}TX IpPvH 9{,vQ3(1EA%9źtб،7:*[ZYc>,2zXtǃ 4 4e9QVxC 1! 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N!(iato;71L7NMUGFDFN0#r7s3 tVJ_!3Y,Y_ endstream endobj 1348 0 obj << /Filter /FlateDecode /Length 93 >> stream xM;0D# 0q,[a߰ˢ@N-{\a4 kt:,KΫE>?2&KlM53x(" endstream endobj 1349 0 obj << /Filter /FlateDecode /Length 320 >> stream x5KnC1E^d`{=:H?(R.1C)S֔/uY%wʷ~+ck@mg܂ߖI;*z⟹|ĮV%.4֖W>#:Z#>%eP o]qؕ]Ule+CiYNx23r.ň믳:ZLV37gժ/ l],XKq$jC)gqO1 }*>7-aNw endstream endobj 1350 0 obj << /Filter /FlateDecode /Length 262 >> stream x5PKnD1 H82Ecޣ+^`:=rK/(=,=fv!< сԀetjJ<-]>;y> Â`/ endstream endobj 1351 0 obj << /Filter /FlateDecode /Length 183 >> stream xMPA0 ~B6M !m縮[@5رqтW=_jv|Y )W$ll$_ J20L~ɖ&40Y $S65@wl}dnlkJ&<Z}~9)rKYhP$'s<>MB endstream endobj 1352 0 obj << /Filter /FlateDecode /Length 124 >> stream x%11{B s24#d>1F"o f ^ ¾{j'eM[up ^y*UFw4fHޫ3rvcAC' & endstream endobj 1353 0 obj << /Filter /FlateDecode /Length 281 >> stream x=Ir@C>G0COI*sm?Yt-ڙiMN汚}3:,ɞpL\zŏ+,o:FY[U$ixg#3\}7lnqfGzU4dбAXApW[krs~]ڙl ~Nwi/Q0zB;wX28Ank/P~p@qƘPE{掤1,%LVĒ·ucVKDroPL+qX{ȥ[uW(sKBqxFj endstream endobj 1354 0 obj << /Filter /FlateDecode /Length 353 >> stream x5R9n1 @-g mH9)$%G b&r |>4Qx?1q&/^*ȱz<> stream x5A1$٪!4ݕ8 ;`)yIsnq#8[y&F@ʉFjڃPM=r֑Wr endstream endobj 1356 0 obj << /Filter /FlateDecode /Length 344 >> stream x=K]ACwl Rz^ʠiIF (mja*퇻r[{ONl~jLV%c+>O:ؼ6w҉\6k[xm~ՙt|+|]LaQcq#{v̦^A:n q;dajXRT) ˮ˪FKD,I|~O:ӓiڈ+ԁjX"§P?bt).wc'erK*>åIi&HP oiv*rIñc_NYDzk"g7̷7 =:p쁝 endstream endobj 1357 0 obj << /Filter /FlateDecode /Length 17 >> stream x325P@C4M endstream endobj 1358 0 obj << /Filter /FlateDecode /Length 378 >> stream x-R9n1 @ݶ߳Ab6͈c]R42Yҫ۞2h{HmVbq"Clz|Ǡ-Q~=BekJ q3b.TxR{\kFdm̑v(f xku-)bjߴ)-{ bz/- F=e&EcD3&/Bh 0 IuI EB\\6m9F XVxj*[fSp0G~1AEj]˼SpXxp }kM9P;Ď=dET83aTaSM簜'gтU^d Ɣρ 8 endstream endobj 1359 0 obj << /Filter /FlateDecode /Length 258 >> stream x=Q;r1} y^&bs6;+D ~<_~2qAQ,|X} |Ɇ[_ȰnE ځ͈f#[?KJ.WD%FP44Yyȳ<: ӅX!.wZűL 0Δ#Jϋ[o9? kj|qS54a5gClZj(ġ(-<"ikt#r[g?Z[] endstream endobj 1360 0 obj << /Filter /FlateDecode /Length 353 >> stream x5q0 {O|'kr)|WM AZK-z%b/F{PBlD6S\6F-Q{V#;N/>tg_uzmaw%E+QEɦ:kR%Zh#s~BxQK`5F*> stream x5QKnD1@M8ϫF 1ذė<y߲bzۉog#TR+E )t' Påe_dP.7f0;00H.CEVjQ $ȶM&$?yS*A:S)އ3Q:lנM[ԪR}:l%Aqh8-j"?kYEm+ q&(7 ,F/4.*!eVl쇫 V^b8)m45j" endstream endobj 1362 0 obj << /Filter /FlateDecode /Length 212 >> stream x=P0 5Fir)ۀ$M!ůDUxXP6CtH5sFOɉk4ͳHat!ˡ*tYL=,xV;A_zCL !T C.](db syp.Gy~ld{n+5L endstream endobj 1363 0 obj << /Filter /FlateDecode /Length 427 >> stream x=Kv0D^KIZe\  sɐY**ٖ+-xH 3h~sW-ߒcʤtݫߦXJUǑ%>!L>s)܇2)^vꊿ2eb)H/%)E|*6I^Ve9aQ3ƒjQfiftMH9D 2 ڠ,Ri!(侾.)~>FD+yه,MZh2϶l~#&*;]neF8:yY?ՂeuPYσ5|p2uVd6=[R| endstream endobj 1364 0 obj << /Filter /FlateDecode /Length 330 >> stream x=A0C9G0}Eyrwϗ1HvaZ˺c[Gۿf=׬ගr2,pYP{yK Zgn%, pnLN TDYHVB>~ؒс1`ˊp SK,|Ĥ&2FwL$d(c3Mݶe7}#zibUS@/R|j Ip&Q(àkgK-ŭ:!ox~زr-aaUbɅS6ܒh>25sD,S칾  endstream endobj 1365 0 obj << /Filter /FlateDecode /Length 231 >> stream xUQAn1~B@LUakD=TZ-ǀa ɵQ3`+$x[F@xlG^<4c!0'U;ʜGibbRC抜JyI1(v;DqPJ2;[mދe𳧳o5<y- GhEx;.M2*=;yEHݒ39bߟOfT endstream endobj 1366 0 obj << /Filter /FlateDecode /Length 259 >> stream x=Kn0 C9`ep].=:c=NkoeS{׫s\0OC$W` ԭVp-WOdq hI<R@-㫛> stream x5SKr0\ 3mNV"-1HRբ!ZmWo-+S?^qIG/I=Ru u$KLi+F{nz)iTo]Xn1 H3F~& -dKt؆d8`~qߓ9yt!}ϐ]VCHPFR?9]s(~pp6f8T!hݨ ՟ Zp"uϷ;'Zo@|]=Nb:%8DB61+:3l'QlT@"h &?334XZ 4 %XQ+׋̋kF QB~ɚmU4lq{2FqWд6%tM#N4r5sʝȨhǩo_~u endstream endobj 1368 0 obj << /Filter /FlateDecode /Length 441 >> stream x=IPDy .gyUEzcpUٰ\>VVkřʾ_Xc~}^~|ʼ]Yx(U >Zm1͝1;Â9cl0$7myYԵ 1h5=_ :Nj@#hEgY-4Oԡc{6 P7q"ڀ9nNt6%:smFdGq)h^ _mt>^6U+SVrgֆ7 /lڞG8N2~2KN`O[pمafw;"*v $xCq["{H[њ8i=\$!Wğߖ ~ b_GD@^Jח}66nOFs6+XyWA-à7Vg: I endstream endobj 1369 0 obj << /Filter /FlateDecode /Length 256 >> stream x=QIn1+J@3UךCb`ÈXOl,xnzk<p_X=:ܗj@'2CiDs4O-Jkm`V)dSzg5GRu)؁jj[d\P)80]ҷ{xC> [u"6HYHIl|ԡ}*6eKZg[Loub>ȖlmR>2@e 4\ endstream endobj 1370 0 obj << /Filter /FlateDecode /Length 419 >> stream x5IADu .`)r:O[-/(As ~[U~7,3}L+m_ֱ"~{}ۊK'Ek&7ɑe-*nV[d+㵰"#oگաb@Vꕠhhՙ:(;-Va(k=W2A M3mZiY[#ӋG+ EܳT3JhlU峋6G+R`Z67<IZ:^s9ۂr@p5 5]=X{YLJ0\.@-dOd5-ǞA6T?Ѥ5='(ڙO;D L(45<В%%Etۼwet@79{dRwE}޾l_ endstream endobj 1371 0 obj << /Filter /FlateDecode /Length 270 >> stream x=QIn1l=E9LMѭԲ14 /U1|`g㽴mG02oD&\  ށk(_6u=T&"&iŠxB<uu$lL~ XzWZjOԎW 7Ns$ _D1s^Tz Uy. N!(iato;71L7NMUGFDFN0#r7s3 tVJ_!3Y,Y_ endstream endobj 1372 0 obj << /Filter /FlateDecode /Length 93 >> stream xM;0D# 0q,[a߰ˢ@N-{\a4 kt:,KΫE>?2&KlM53x(" endstream endobj 1373 0 obj << /Filter /FlateDecode /Length 320 >> stream x5KnC1E^d`{=:H?(R.1C)S֔/uY%wʷ~+ck@mg܂ߖI;*z⟹|ĮV%.4֖W>#:Z#>%eP o]qؕ]Ule+CiYNx23r.ň믳:ZLV37gժ/ l],XKq$jC)gqO1 }*>7-aNw endstream endobj 1374 0 obj << /Filter /FlateDecode /Length 262 >> stream x5PKnD1 H82Ecޣ+^`:=rK/(=,=fv!< сԀetjJ<-]>;y> Â`/ endstream endobj 1375 0 obj << /Filter /FlateDecode /Length 183 >> stream xMPA0 ~B6M !m縮[@5رqтW=_jv|Y )W$ll$_ J20L~ɖ&40Y $S65@wl}dnlkJ&<Z}~9)rKYhP$'s<>MB endstream endobj 1376 0 obj << /Filter /FlateDecode /Length 124 >> stream x%11{B s24#d>1F"o f ^ ¾{j'eM[up ^y*UFw4fHޫ3rvcAC' & endstream endobj 1377 0 obj << /Filter /FlateDecode /Length 281 >> stream x=Ir@C>G0COI*sm?Yt-ڙiMN汚}3:,ɞpL\zŏ+,o:FY[U$ixg#3\}7lnqfGzU4dбAXApW[krs~]ڙl ~Nwi/Q0zB;wX28Ank/P~p@qƘPE{掤1,%LVĒ·ucVKDroPL+qX{ȥ[uW(sKBqxFj endstream endobj 1378 0 obj << /Filter /FlateDecode /Length 353 >> stream x5R9n1 @-g mH9)$%G b&r |>4Qx?1q&/^*ȱz<> stream x5A1$٪!4ݕ8 ;`)yIsnq#8[y&F@ʉFjڃPM=r֑Wr endstream endobj 1380 0 obj << /Filter /FlateDecode /Length 344 >> stream x=K]ACwl Rz^ʠiIF (mja*퇻r[{ONl~jLV%c+>O:ؼ6w҉\6k[xm~ՙt|+|]LaQcq#{v̦^A:n q;dajXRT) ˮ˪FKD,I|~O:ӓiڈ+ԁjX"§P?bt).wc'erK*>åIi&HP oiv*rIñc_NYDzk"g7̷7 =:p쁝 endstream endobj 1381 0 obj << /Filter /FlateDecode /Length 17 >> stream x325P@C4M endstream endobj 1382 0 obj << /Filter /FlateDecode /Length 378 >> stream x-R9n1 @ݶ߳Ab6͈c]R42Yҫ۞2h{HmVbq"Clz|Ǡ-Q~=BekJ q3b.TxR{\kFdm̑v(f xku-)bjߴ)-{ bz/- F=e&EcD3&/Bh 0 IuI EB\\6m9F XVxj*[fSp0G~1AEj]˼SpXxp }kM9P;Ď=dET83aTaSM簜'gтU^d Ɣρ 8 endstream endobj 1383 0 obj << /Filter /FlateDecode /Length 258 >> stream x=Q;r1} y^&bs6;+D ~<_~2qAQ,|X} |Ɇ[_ȰnE ځ͈f#[?KJ.WD%FP44Yyȳ<: ӅX!.wZűL 0Δ#Jϋ[o9? kj|qS54a5gClZj(ġ(-<"ikt#r[g?Z[] endstream endobj 1384 0 obj << /Filter /FlateDecode /Length 353 >> stream x5q0 {O|'kr)|WM AZK-z%b/F{PBlD6S\6F-Q{V#;N/>tg_uzmaw%E+QEɦ:kR%Zh#s~BxQK`5F*> stream x5QKnD1@M8ϫF 1ذė<y߲bzۉog#TR+E )t' Påe_dP.7f0;00H.CEVjQ $ȶM&$?yS*A:S)އ3Q:lנM[ԪR}:l%Aqh8-j"?kYEm+ q&(7 ,F/4.*!eVl쇫 V^b8)m45j" endstream endobj 1386 0 obj << /Filter /FlateDecode /Length 212 >> stream x=P0 5Fir)ۀ$M!ůDUxXP6CtH5sFOɉk4ͳHat!ˡ*tYL=,xV;A_zCL !T C.](db syp.Gy~ld{n+5L endstream endobj 1387 0 obj << /Filter /FlateDecode /Length 427 >> stream x=Kv0D^KIZe\  sɐY**ٖ+-xH 3h~sW-ߒcʤtݫߦXJUǑ%>!L>s)܇2)^vꊿ2eb)H/%)E|*6I^Ve9aQ3ƒjQfiftMH9D 2 ڠ,Ri!(侾.)~>FD+yه,MZh2϶l~#&*;]neF8:yY?ՂeuPYσ5|p2uVd6=[R| endstream endobj 1388 0 obj << /Filter /FlateDecode /Length 330 >> stream x=A0C9G0}Eyrwϗ1HvaZ˺c[Gۿf=׬ගr2,pYP{yK Zgn%, pnLN TDYHVB>~ؒс1`ˊp SK,|Ĥ&2FwL$d(c3Mݶe7}#zibUS@/R|j Ip&Q(àkgK-ŭ:!ox~زr-aaUbɅS6ܒh>25sD,S칾  endstream endobj 1389 0 obj << /Filter /FlateDecode /Length 231 >> stream xUQAn1~B@LUakD=TZ-ǀa ɵQ3`+$x[F@xlG^<4c!0'U;ʜGibbRC抜JyI1(v;DqPJ2;[mދe𳧳o5<y- GhEx;.M2*=;yEHݒ39bߟOfT endstream endobj 1390 0 obj << /Filter /FlateDecode /Length 259 >> stream x=Kn0 C9`ep].=:c=NkoeS{׫s\0OC$W` ԭVp-WOdq hI<R@-㫛> stream x5SKr0\ 3mNV"-1HRբ!ZmWo-+S?^qIG/I=Ru u$KLi+F{nz)iTo]Xn1 H3F~& -dKt؆d8`~qߓ9yt!}ϐ]VCHPFR?9]s(~pp6f8T!hݨ ՟ Zp"uϷ;'Zo@|]=Nb:%8DB61+:3l'QlT@"h &?334XZ 4 %XQ+׋̋kF QB~ɚmU4lq{2FqWд6%tM#N4r5sʝȨhǩo_~u endstream endobj 1392 0 obj << /Filter /FlateDecode /Length 441 >> stream x=IPDy .gyUEzcpUٰ\>VVkřʾ_Xc~}^~|ʼ]Yx(U >Zm1͝1;Â9cl0$7myYԵ 1h5=_ :Nj@#hEgY-4Oԡc{6 P7q"ڀ9nNt6%:smFdGq)h^ _mt>^6U+SVrgֆ7 /lڞG8N2~2KN`O[pمafw;"*v $xCq["{H[њ8i=\$!Wğߖ ~ b_GD@^Jח}66nOFs6+XyWA-à7Vg: I endstream endobj 1393 0 obj << /Filter /FlateDecode /Length 256 >> stream x=QIn1+J@3UךCb`ÈXOl,xnzk<p_X=:ܗj@'2CiDs4O-Jkm`V)dSzg5GRu)؁jj[d\P)80]ҷ{xC> [u"6HYHIl|ԡ}*6eKZg[Loub>ȖlmR>2@e 4\ endstream endobj 1394 0 obj << /Filter /FlateDecode /Length 419 >> stream x5IADu .`)r:O[-/(As ~[U~7,3}L+m_ֱ"~{}ۊK'Ek&7ɑe-*nV[d+㵰"#oگաb@Vꕠhhՙ:(;-Va(k=W2A M3mZiY[#ӋG+ EܳT3JhlU峋6G+R`Z67<IZ:^s9ۂr@p5 5]=X{YLJ0\.@-dOd5-ǞA6T?Ѥ5='(ڙO;D L(45<В%%Etۼwet@79{dRwE}޾l_ endstream endobj 1395 0 obj << /Filter /FlateDecode /Length 270 >> stream x=QIn1l=E9LMѭԲ14 /U1|`g㽴mG02oD&\  ށk(_6u=T&"&iŠxB<uu$lL~ XzWZjOԎW 7Ns$ _D1s^Tz Uy. N!(iato;71L7NMUGFDFN0#r7s3 tVJ_!3Y,Y_ endstream endobj 1396 0 obj << /Filter /FlateDecode /Length 93 >> stream xM;0D# 0q,[a߰ˢ@N-{\a4 kt:,KΫE>?2&KlM53x(" endstream endobj 1397 0 obj << /Filter /FlateDecode /Length 262 >> stream x5PKnD1 H82Ecޣ+^`:=rK/(=,=fv!< сԀetjJ<-]>;y> Â`/ endstream endobj 1398 0 obj << /Filter /FlateDecode /Length 183 >> stream xMPA0 ~B6M !m縮[@5رqтW=_jv|Y )W$ll$_ J20L~ɖ&40Y $S65@wl}dnlkJ&<Z}~9)rKYhP$'s<>MB endstream endobj 1399 0 obj << /Filter /FlateDecode /Length 124 >> stream x%11{B s24#d>1F"o f ^ ¾{j'eM[up ^y*UFw4fHޫ3rvcAC' & endstream endobj 1400 0 obj << /Filter /FlateDecode /Length 281 >> stream x=Ir@C>G0COI*sm?Yt-ڙiMN汚}3:,ɞpL\zŏ+,o:FY[U$ixg#3\}7lnqfGzU4dбAXApW[krs~]ڙl ~Nwi/Q0zB;wX28Ank/P~p@qƘPE{掤1,%LVĒ·ucVKDroPL+qX{ȥ[uW(sKBqxFj endstream endobj 1401 0 obj << /Filter /FlateDecode /Length 353 >> stream x5R9n1 @-g mH9)$%G b&r |>4Qx?1q&/^*ȱz<> stream x5A1$٪!4ݕ8 ;`)yIsnq#8[y&F@ʉFjڃPM=r֑Wr endstream endobj 1403 0 obj << /Filter /FlateDecode /Length 17 >> stream x325P@C4M endstream endobj 1404 0 obj << /Filter /FlateDecode /Length 378 >> stream x-R9n1 @ݶ߳Ab6͈c]R42Yҫ۞2h{HmVbq"Clz|Ǡ-Q~=BekJ q3b.TxR{\kFdm̑v(f xku-)bjߴ)-{ bz/- F=e&EcD3&/Bh 0 IuI EB\\6m9F XVxj*[fSp0G~1AEj]˼SpXxp }kM9P;Ď=dET83aTaSM簜'gтU^d Ɣρ 8 endstream endobj 1405 0 obj << /Filter /FlateDecode /Length 258 >> stream x=Q;r1} y^&bs6;+D ~<_~2qAQ,|X} |Ɇ[_ȰnE ځ͈f#[?KJ.WD%FP44Yyȳ<: ӅX!.wZűL 0Δ#Jϋ[o9? kj|qS54a5gClZj(ġ(-<"ikt#r[g?Z[] endstream endobj 1406 0 obj << /Filter /FlateDecode /Length 353 >> stream x5q0 {O|'kr)|WM AZK-z%b/F{PBlD6S\6F-Q{V#;N/>tg_uzmaw%E+QEɦ:kR%Zh#s~BxQK`5F*> stream x5QKnD1@M8ϫF 1ذė<y߲bzۉog#TR+E )t' Påe_dP.7f0;00H.CEVjQ $ȶM&$?yS*A:S)އ3Q:lנM[ԪR}:l%Aqh8-j"?kYEm+ q&(7 ,F/4.*!eVl쇫 V^b8)m45j" endstream endobj 1408 0 obj << /Filter /FlateDecode /Length 212 >> stream x=P0 5Fir)ۀ$M!ůDUxXP6CtH5sFOɉk4ͳHat!ˡ*tYL=,xV;A_zCL !T C.](db syp.Gy~ld{n+5L endstream endobj 1409 0 obj << /Filter /FlateDecode /Length 427 >> stream x=Kv0D^KIZe\  sɐY**ٖ+-xH 3h~sW-ߒcʤtݫߦXJUǑ%>!L>s)܇2)^vꊿ2eb)H/%)E|*6I^Ve9aQ3ƒjQfiftMH9D 2 ڠ,Ri!(侾.)~>FD+yه,MZh2϶l~#&*;]neF8:yY?ՂeuPYσ5|p2uVd6=[R| endstream endobj 1410 0 obj << /Filter /FlateDecode /Length 330 >> stream x=A0C9G0}Eyrwϗ1HvaZ˺c[Gۿf=׬ගr2,pYP{yK Zgn%, pnLN TDYHVB>~ؒс1`ˊp SK,|Ĥ&2FwL$d(c3Mݶe7}#zibUS@/R|j Ip&Q(àkgK-ŭ:!ox~زr-aaUbɅS6ܒh>25sD,S칾  endstream endobj 1411 0 obj << /Filter /FlateDecode /Length 231 >> stream xUQAn1~B@LUakD=TZ-ǀa ɵQ3`+$x[F@xlG^<4c!0'U;ʜGibbRC抜JyI1(v;DqPJ2;[mދe𳧳o5<y- GhEx;.M2*=;yEHݒ39bߟOfT endstream endobj 1412 0 obj << /Filter /FlateDecode /Length 259 >> stream x=Kn0 C9`ep].=:c=NkoeS{׫s\0OC$W` ԭVp-WOdq hI<R@-㫛> stream x5SKr0\ 3mNV"-1HRբ!ZmWo-+S?^qIG/I=Ru u$KLi+F{nz)iTo]Xn1 H3F~& -dKt؆d8`~qߓ9yt!}ϐ]VCHPFR?9]s(~pp6f8T!hݨ ՟ Zp"uϷ;'Zo@|]=Nb:%8DB61+:3l'QlT@"h &?334XZ 4 %XQ+׋̋kF QB~ɚmU4lq{2FqWд6%tM#N4r5sʝȨhǩo_~u endstream endobj 1414 0 obj << /Filter /FlateDecode /Length 441 >> stream x=IPDy .gyUEzcpUٰ\>VVkřʾ_Xc~}^~|ʼ]Yx(U >Zm1͝1;Â9cl0$7myYԵ 1h5=_ :Nj@#hEgY-4Oԡc{6 P7q"ڀ9nNt6%:smFdGq)h^ _mt>^6U+SVrgֆ7 /lڞG8N2~2KN`O[pمafw;"*v $xCq["{H[њ8i=\$!Wğߖ ~ b_GD@^Jח}66nOFs6+XyWA-à7Vg: I endstream endobj 1415 0 obj << /Filter /FlateDecode /Length 256 >> stream x=QIn1+J@3UךCb`ÈXOl,xnzk<p_X=:ܗj@'2CiDs4O-Jkm`V)dSzg5GRu)؁jj[d\P)80]ҷ{xC> [u"6HYHIl|ԡ}*6eKZg[Loub>ȖlmR>2@e 4\ endstream endobj 1416 0 obj << /Filter /FlateDecode /Length 419 >> stream x5IADu .`)r:O[-/(As ~[U~7,3}L+m_ֱ"~{}ۊK'Ek&7ɑe-*nV[d+㵰"#oگաb@Vꕠhhՙ:(;-Va(k=W2A M3mZiY[#ӋG+ EܳT3JhlU峋6G+R`Z67<IZ:^s9ۂr@p5 5]=X{YLJ0\.@-dOd5-ǞA6T?Ѥ5='(ڙO;D L(45<В%%Etۼwet@79{dRwE}޾l_ endstream endobj 1417 0 obj << /Filter /FlateDecode /Length 270 >> stream x=QIn1l=E9LMѭԲ14 /U1|`g㽴mG02oD&\  ށk(_6u=T&"&iŠxB<uu$lL~ XzWZjOԎW 7Ns$ _D1s^Tz Uy. 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middle25projects/2024103840/src/scripts/000077500000000000000000000000001506651042300175645ustar00rootroot00000000000000middle25projects/2024103840/src/scripts/MoBE-main/000077500000000000000000000000001506651042300212705ustar00rootroot00000000000000middle25projects/2024103840/src/scripts/MoBE-main/LEGAL.md000066400000000000000000000010741506651042300224400ustar00rootroot00000000000000Legal Disclaimer Within this source code, the comments in Chinese shall be the original, governing version. Any comment in other languages are for reference only. In the event of any conflict between the Chinese language version comments and other language version comments, the Chinese language version shall prevail. 法律免责声明 关于代码注释部分，中文注释为官方版本，其它语言注释仅做参考。中文注释可能与其它语言注释存在不一致，当中文注释与其它语言注释存在不一致时，请以中文注释为准。middle25projects/2024103840/src/scripts/MoBE-main/README.md000066400000000000000000000247621506651042300225620ustar00rootroot00000000000000

MoBE: Mixture-of-Basis-Experts for Compressing
MoE-based LLMs

--- ## ✅ Feature List - [x] **Supported multiple MoE models (BF16)** - [x] Ling Family - [x] Qwen3MoE Family - [x] DeepSeek-V3 - [x] Kimi-K2-Instruct - [ ] **Supported SGLang inference (with fused-MoE kernel)** - [ ] **Supported MoBE mega-kernel (high-performance fused kernel for MoBE)** > 💡 *Coming soon: Optimized inference kernels for MoBE models to maximize throughput and memory efficiency.* --- ## 📘 Introduction **MoBE (Mixture-of-Basis-Experts)** is a novel model compression technique designed for MoE LLMs developed by the **AGI Center, Ant Group Research**. It achieves efficient parameter reduction by factorizing each expert's weight matrix as: $$ \mathbf{W} = \mathbf{A}\mathbf{B}, \quad \text{where} \quad \mathbf{B} = \sum_{i=1}^m \alpha_i B_i $$ - $\mathbf{A}$: Expert-specific matrix - $\mathbf{B}$: Linear combination of **basis matrices** across all experts, weighted by coefficients $\alpha_i$ The factorization is learned by minimizing the **reconstruction error** between the original and compressed weight matrices. ### 🔍 Key Results MoBE significantly outperforms prior compression methods with minimal accuracy degradation: - Reduces parameter count by **24%–30%** in leading open-source models - Incurs only **1%–2% absolute accuracy drop** (≈2% relative) - Demonstrated on **Qwen3-235B**, **DeepSeek-V3 (671B)**, and **Kimi-K2-Instruct (1T)** ## 📊 Evaluation Results ![results](results.jpg) --- ## 🚀 Quickstart ### 🔧 Installation ``` pip install -r requirement.txt ``` --- ### 🛠️ Step-by-Step Instructions Converting an MoE model to MoBE involves two stages: 1. **Train** the MoBE decomposition. 2. **Generate** either a native MoBE model or reconstruct a standard MoE for compatibility. --- #### 1. Train MoBE Matrices ``` python train.py --index_path /root/DeepSeek-V3-0324/model.safetensors.index.json \ --base_dir /root/DeepSeek-V3-0324 \ --save_path /root/MoBE/DeepSeek-V3-0324 \ --num_hidden_layers 61 \ --num_matrices 256 \ --rows_per_matrix 2048 \ --cols 7168 \ --num_epochs 10000 \ --batch_size 32 \ --num_batches 8 \ --learning_rate 0.07 \ --num_B 64 \ --truncation 2048 \ --start_layer 3 \ --end_layer 61 \ --matrix_type "gate_proj" \ --activation 'tanh' ``` | Argument | Description | |--------|-------------| | `index_path` | Path to `.safetensors.index.json` mapping tensor names to shards | | `base_dir` | Root directory containing model shards | | `save_path` | Output directory for trained MoBE matrices | | `num_hidden_layers` | Total number of transformer layers | | `num_matrices` | Number of experts in the original MoE model | | `rows_per_matrix` | Row dimension of the weight matrices (e.g., `up_proj`, `gate_proj`) | | `cols` | Column dimension of the weight matrices | | `num_epochs` | Number of optimization steps for reconstruction | | `batch_size` | Batch size (number of experts sampled per step) | | `num_batches` | Number of batches processed per epoch. Total experts in one layer = `batch_size × num_batches` | | `learning_rate` | Learning rate for the optimizer (e.g., Adam) | | `num_B` | Number of basis matrices used in the MoBE | | `truncation` | Maximum number of rows retained in each basis matrix | | `start_layer` | First transformer layer (inclusive) to apply MoBE compression | | `end_layer` | Last transformer layer (exclusive) to apply compression | | `matrix_type` | Type of weight matrix to compress (e.g., `"gate_proj"`, `"up_proj"`) | | `activation` | Activation function used in MoBE (e.g., `"silu"`, `"tanh"`) | > 💡 **Tip**: Run this step separately for each `matrix_type` (e.g., `gate_proj`, `up_proj`) within the same layer range. For Kimi-K2-Instruct, we recommend dividing the experts within each transformer layer into two groups and applying MoBE compression separately to each group. ``` python train_group.py --index_path /root/Kimi-K2-Instruct/model.safetensors.index.json \ --base_dir /root/Kimi-K2-Instruct \ --save_path /root/MoBE/Kimi-K2-Instruct \ --num_hidden_layers 61 \ --num_matrices 384 \ --rows_per_matrix 2048 \ --cols 7168 \ --num_epochs 15000 \ --batch_size 32 \ --num_batches 12 \ --learning_rate 0.07 \ --num_B 128 \ --truncation 2048 \ --start_layer 1 \ --end_layer 61 \ --matrix_type "gate_proj" \ --num_groups 2 \ --activation 'silu' ``` | Argument | Description | |--------|-------------| | `index_path` | Path to `.safetensors.index.json` mapping tensor names to shards | | `base_dir` | Root directory containing model shards | | `save_path` | Output directory for trained MoBE matrices | | `num_hidden_layers` | Total number of transformer layers | | `num_matrices` | Number of experts in the original MoE model | | `rows_per_matrix` | Row dimension of the weight matrices (e.g., `up_proj`, `gate_proj`) | | `cols` | Column dimension of the weight matrices | | `num_epochs` | Number of optimization steps for reconstruction | | `batch_size` | Batch size (number of experts sampled per step) | | `num_batches` | Number of batches processed per epoch. Total experts in one layer = `batch_size × num_batches` | | `learning_rate` | Learning rate for the optimizer (e.g., Adam) | | `num_B` | Number of basis matrices used in the MoBE | | `truncation` | Maximum number of rows retained in each basis matrix | | `start_layer` | First transformer layer (inclusive) to apply MoBE compression | | `end_layer` | Last transformer layer (exclusive) to apply compression | | `matrix_type` | Type of weight matrix to compress (e.g., `"gate_proj"`, `"up_proj"`) | | `activation` | Activation function used in MoBE (e.g., `"silu"`, `"tanh"`) | | `num_groups` | Number of expert groups to split the original MoE experts into before applying MoBE compression separately to each group | --- #### 2. Generate MoBE or Reconstructed MoE Model After training, you can: - Deploy the **native MoBE model** (high compression) - Reconstruct a **standard MoE model** for compatibility with `vLLM` or `SGLang` ##### 🔹 Option A: Save Native MoBE Model ``` python get_mobe.py --base_model /root/DeepSeek-V3-0324 \ --mobe_dir /root/MoBE/DeepSeek-V3-0324 \ --save_dir /root/DeepSeek-V3-0324-MoBE \ --num_B 64 \ --num_experts 256 \ --start_layer 3 \ --end_layer 61 \ --dtype bfloat16 \ --activation 'tanh' ``` ###### Arguments | Argument | Description | |--------|-------------| | `base_model` | Path to the original model directory | | `mobe_dir` | Directory containing trained MoBE matrices (`A`, `B^i`, `α_i`) | | `save_dir` | Where to save the final MoBE model | | `num_B` | Number of basis matrices (must match training) | | `num_experts` | Number of experts in the original model | | `start_layer` | First layer to replace with MoBE (inclusive) | | `end_layer` | Last layer to replace (exclusive) | | `dtype` | Target data type (`float32`, `bfloat16`, `float16`) | | `activation` | Activation function used in MoBE (e.g., `"silu"`, `"tanh"`) | | `grouped_experts` | Whether to group experts within the same layer | ##### 🔹 Option B: Reconstruct Standard MoE Weights For seamless integration with existing inference engines: ``` python get_hf_model.py --base_model /root/DeepSeek-V3-0324 \ --mobe_dir /root/MoBE/DeepSeek-V3-0324 \ --save_dir /root/DeepSeek-V3-0324-MoBE-hf \ --start_layer 3 \ --end_layer 61 \ --num_experts 256 \ --dtype bfloat16 ``` ###### Arguments | Argument | Description | |--------|-------------| | `base_model` | Path to the original model directory | | `mobe_dir` | Directory containing trained MoBE matrices (`A`, `B^i`, `α_i`) | | `save_dir` | Output path for reconstructed MoE model | | `start_layer` | First layer to replace with MoBE (inclusive) | | `end_layer` | Last layer to replace (exclusive) | | `num_experts` | Number of experts in the original model | | `dtype` | Target data type (`float32`, `bfloat16`, `float16`) | | `grouped_experts` | Whether to group experts within the same layer | > ✅ The reconstructed model is **fully compatible** with Hugging Face `AutoModelForCausalLM`, `vLLM`, and `SGLang`. --- ## 💡 MoBE Generate Example ``` from transformers import AutoTokenizer from models.modeling_deepseek_v3_mobe import DeepseekV3MoBEForCausalLM from models.modeling_qwen3_mobe import Qwen3MoBEForCausalLM from models.modeling_kimi_k2_mobe import KimiK2MoBEForCausalLM import torch model_name = "/root/DeepSeek-V3-0324-MoBE" offload_folder = "./offload_dir" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.pad_token = tokenizer.eos_token max_memory = {i: "120GiB" for i in range(8)} max_memory["cpu"] = "1200GiB" if 'Qwen' in model_name: model = Qwen3MoBEForCausalLM.from_pretrained( model_name, device_map="auto", offload_folder=offload_folder, offload_state_dict=True, torch_dtype=torch.bfloat16 max_memory=max_memory ) elif 'DeepSeek' in model_name: model = DeepseekV3MoBEForCausalLM.from_pretrained( model_name, device_map="auto", offload_folder=offload_folder, offload_state_dict=True, torch_dtype=torch.bfloat16, max_memory=max_memory ) else: model = KimiK2MoBEForCausalLM.from_pretrained( model_name, device_map="auto", offload_folder=offload_folder, offload_state_dict=True, torch_dtype=torch.bfloat16, max_memory=max_memory ) input_text = "Artificial intelligence is" inputs = tokenizer(input_text, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu") with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, pad_token_id=tokenizer.eos_token_id ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print("Generated text:") print(generated_text) ``` --- ## 📚 Citation If you find MoBE useful in your research or application, please consider citing our work: ```down-bibtex @misc{chen2025mobemixtureofbasisexpertscompressingmoebased, title={MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs}, author={Xiaodong Chen and Mingming Ha and Zhenzhong Lan and Jing Zhang and Jianguo Li}, year={2025}, eprint={2508.05257}, archivePrefix={arXiv}, } ``` middle25projects/2024103840/src/scripts/MoBE-main/get_hf_model.py000066400000000000000000000117031506651042300242600ustar00rootroot00000000000000import argparse import os import torch from safetensors.torch import load_file as torch_load_file from transformers import AutoModelForCausalLM, AutoTokenizer from tqdm import tqdm import re def parse_args(): parser = argparse.ArgumentParser(description="Merge MoBE experts into a MoE model.") parser.add_argument("--base_model", required=True, help="Path to the base model checkpoint (HuggingFace format).") parser.add_argument("--mobe_dir", required=True, help="Directory that contains MoBE weights.") parser.add_argument("--save_dir", required=True, help="Where to save the merged model.") parser.add_argument("--start_layer", type=int, default=0, help="Start layer index for MoBE conversion.") parser.add_argument("--end_layer", type=int, default=94, help="End layer index for MoBE conversion.") parser.add_argument("--num_experts", type=int, default=128, help="Total number of experts per layer") parser.add_argument("--dtype", default="bfloat16", choices=["float16", "bfloat16", "float32"], help="Data type for weights") parser.add_argument("--grouped_experts", action="store_true", help="If set, experts are stored in multiple grouped files like " "model_layers_0_mlp_gate_proj_group0.safetensors") return parser.parse_args() def load_safetensors(file_path: str) -> dict: if not os.path.exists(file_path): raise FileNotFoundError(f"File not found: {file_path}") return torch_load_file(file_path) def load_grouped_tensors(mobe_dir: str, layer_idx: int, proj_name: str, num_experts: int) -> dict: """ Load grouped .safetensors files for a given layer and projection (gate/up). Files are expected to be named like: model_layers_{i}_mlp_{proj_name}_group{k}.safetensors Returns a merged dict with keys: f'experts_{j}_{proj_name}_weight' """ pattern = re.compile(rf"model_layers_{layer_idx}_mlp_{proj_name}_group(\d+)\.safetensors$") group_files = [] for f in os.listdir(mobe_dir): match = pattern.match(f) if match: group_files.append((int(match.group(1)), os.path.join(mobe_dir, f))) group_files.sort() if not group_files: raise FileNotFoundError(f"No grouped files found for layer {layer_idx}, proj {proj_name}") merged_dict = {} expert_idx = 0 for _, file_path in group_files: tensors = load_safetensors(file_path) # Sort tensors by expert index to ensure order sorted_items = sorted(tensors.items(), key=lambda x: int(x[0].split('_')[1])) for k, v in sorted_items: new_key = f"experts_{expert_idx}_{proj_name}_weight" merged_dict[new_key] = v expert_idx += 1 if expert_idx != num_experts: raise ValueError(f"Expected {num_experts} experts for layer {layer_idx}, " f"but loaded {expert_idx} from grouped files.") return merged_dict def main(): args = parse_args() dtype_map = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32} torch_dtype = dtype_map[args.dtype] print(f"Loading base model from {args.base_model}") model = AutoModelForCausalLM.from_pretrained( args.base_model, torch_dtype=torch_dtype, device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True) state_dict = model.state_dict() for i in tqdm(range(args.start_layer, args.end_layer), desc="Merging experts"): if args.grouped_experts: # Load grouped files gate_dict = load_grouped_tensors(args.mobe_dir, i, "gate_proj", args.num_experts) up_dict = load_grouped_tensors(args.mobe_dir, i, "up_proj", args.num_experts) else: # Load single-file per layer gate_path = os.path.join(args.mobe_dir, f"model_layers_{i}_mlp_gate_proj_weight.safetensors") up_path = os.path.join(args.mobe_dir, f"model_layers_{i}_mlp_up_proj_weight.safetensors") gate_dict = load_safetensors(gate_path) up_dict = load_safetensors(up_path) # Merge experts for j in range(args.num_experts): gate_key = f'experts_{j}_gate_proj_weight' up_key = f'experts_{j}_up_proj_weight' state_dict[f'model.layers.{i}.mlp.experts.{j}.gate_proj.weight'] = gate_dict[gate_key] state_dict[f'model.layers.{i}.mlp.experts.{j}.up_proj.weight'] = up_dict[up_key] print("Loading merged state dict ...") model.load_state_dict(state_dict) print(f"Saving merged model to {args.save_dir}") model.save_pretrained(args.save_dir) tokenizer.save_pretrained(args.save_dir) print("Done.") if __name__ == "__main__": main()middle25projects/2024103840/src/scripts/MoBE-main/get_mobe.py000066400000000000000000000207241506651042300234300ustar00rootroot00000000000000import os import json import torch from tqdm import tqdm from transformers import AutoConfig, AutoTokenizer from models.modeling_deepseek_v3_mobe import DeepseekV3MoBEForCausalLM from models.modeling_qwen3_mobe import Qwen3MoBEForCausalLM from models.modeling_kimi_k2_mobe import KimiK2MoBEForCausalLM from models.modeling_bailing_mobe import BailingMoBEForCausalLM from safetensors import safe_open from safetensors.torch import load_file as torch_load_file import argparse def parse_args(): parser = argparse.ArgumentParser(description="Convert MoE to MoBE format with B/W/A parameters.") # Model paths parser.add_argument('--base_model', type=str, required=True, help='Path to the base model checkpoint (HuggingFace format).') parser.add_argument('--mobe_dir', type=str, required=True, help='Directory containing WAB .safetensors files') parser.add_argument('--save_dir', type=str, required=True, help='Path to save the converted MoBE model') # Model config parser.add_argument('--num_B', type=int, default=32, help='Number of basis matrices in MoBE') parser.add_argument('--num_experts', type=int, default=128, help='Total number of experts per layer') parser.add_argument('--start_layer', type=int, default=0, help='Start layer index for MoBE conversion.') parser.add_argument('--end_layer', type=int, default=94, help='End layer index for MoBE conversion') parser.add_argument('--dtype', type=str, default='bfloat16', choices=['bfloat16', 'float16', 'float32'], help='Data type for weights') parser.add_argument("--activation", type=str, choices=["silu", "tanh"], default="silu") parser.add_argument("--grouped_experts", action="store_true", help="If set, experts are stored in multiple grouped files like " "model_layers_0_mlp_gate_proj_group0_WAB.pth") return parser.parse_args() def get_layer_proj_dict(index_path, base_dir, layer_i, matrix_type): with open(index_path, 'r') as f: index_data = json.load(f) layer_dict = {} for weight_name, safetensor_file in index_data['weight_map'].items(): if f"model.layers.{layer_i}.mlp.experts" in weight_name and weight_name.endswith(f"{matrix_type}.weight"): safetensor_path = f"{base_dir}/{safetensor_file}" with safe_open(safetensor_path, framework="pt") as f: layer_dict[weight_name] = f.get_tensor(weight_name) return layer_dict def main(args): dtype_map = { 'bfloat16': torch.bfloat16, 'float16': torch.float16, 'float32': torch.float32 } dtype = dtype_map[args.dtype] config = AutoConfig.from_pretrained(args.base_model, trust_remote_code=True) config.num_B = args.num_B config.activation = args.activation if "Qwen3" in args.base_model: model = Qwen3MoBEForCausalLM.from_pretrained( args.base_model, ignore_mismatched_sizes=True, config=config, device_map='auto', torch_dtype=dtype ) elif "DeepSeek" in args.base_model: model = DeepseekV3MoBEForCausalLM.from_pretrained( args.base_model, ignore_mismatched_sizes=True, config=config, device_map='auto', torch_dtype=dtype ) elif "Kimi" in args.base_model: model = KimiK2MoBEForCausalLM.from_pretrained( args.base_model, ignore_mismatched_sizes=True, config=config, torch_dtype=dtype, device_map='auto', trust_remote_code=True ) elif "Ling" in args.base_model: model = BailingMoBEForCausalLM.from_pretrained( args.base_model, ignore_mismatched_sizes=True, config=config, torch_dtype=dtype, device_map='auto', trust_remote_code=True ) state_dict = model.state_dict() index_path = os.path.join(args.base_model, "model.safetensors.index.json") if not os.path.exists(index_path): raise FileNotFoundError(f"Model index file not found: {index_path}") for i in tqdm(range(args.start_layer, args.end_layer), desc="Processing layers"): if args.grouped_experts: up_weight_dict = get_layer_proj_dict(index_path, args.base_model, i, ".up_proj") gate_weight_dict = get_layer_proj_dict(index_path, args.base_model, i, ".gate_proj") up_tensors = [ up_weight_dict[f"model.layers.{i}.mlp.experts.{j}.up_proj.weight"].to(dtype) for j in range(args.num_experts) ] up_std = torch.stack(up_tensors).std() gate_tensors = [ gate_weight_dict[f"model.layers.{i}.mlp.experts.{j}.gate_proj.weight"].to(dtype) for j in range(args.num_experts) ] gate_std = torch.stack(gate_tensors).std() for g in range(2): gate_file = os.path.join(args.mobe_dir, f"model_layers_{i}_mlp_gate_proj_group{g}_WAB.pth") up_file = os.path.join(args.mobe_dir, f"model_layers_{i}_mlp_up_proj_group{g}_WAB.pth") for f_path in [gate_file, up_file]: if not os.path.exists(f_path): raise FileNotFoundError(f"Parameter file not found: {f_path}") gate_dict = torch.load(gate_file, map_location='cpu') up_dict = torch.load(up_file, map_location='cpu') state_dict[f'model.layers.{i}.mlp.B_up_{g}'] = up_dict['B_params'] state_dict[f'model.layers.{i}.mlp.B_gate_{g}'] = gate_dict['B_params'] state_dict[f'model.layers.{i}.mlp.W_up_{g}'] = torch.softmax(up_dict['w_params'], dim=1) state_dict[f'model.layers.{i}.mlp.W_gate_{g}'] = torch.softmax(gate_dict['w_params'], dim=1) for j in range(args.num_experts // 2): state_dict[f'model.layers.{i}.mlp.experts.{j+(args.num_experts//2)*g}.gate_proj.weight'] = gate_dict['A_params'][j] * gate_std state_dict[f'model.layers.{i}.mlp.experts.{j+(args.num_experts//2)*g}.up_proj.weight'] = up_dict['A_params'][j] * up_std else: gate_file = os.path.join(args.mobe_dir, f"model_layers_{i}_mlp_gate_proj_WAB.pth") up_file = os.path.join(args.mobe_dir, f"model_layers_{i}_mlp_up_proj_WAB.pth") for f_path in [gate_file, up_file]: if not os.path.exists(f_path): raise FileNotFoundError(f"Parameter file not found: {f_path}") gate_dict = torch.load(gate_file, map_location='cpu') up_dict = torch.load(up_file, map_location='cpu') up_weight_dict = get_layer_proj_dict(index_path, args.base_model, i, ".up_proj") gate_weight_dict = get_layer_proj_dict(index_path, args.base_model, i, ".gate_proj") up_tensors = [ up_weight_dict[f"model.layers.{i}.mlp.experts.{j}.up_proj.weight"].to(dtype) for j in range(args.num_experts) ] up_std = torch.stack(up_tensors).std() gate_tensors = [ gate_weight_dict[f"model.layers.{i}.mlp.experts.{j}.gate_proj.weight"].to(dtype) for j in range(args.num_experts) ] gate_std = torch.stack(gate_tensors).std() state_dict[f'model.layers.{i}.mlp.B_up'] = up_dict['B_params'] state_dict[f'model.layers.{i}.mlp.B_gate'] = gate_dict['B_params'] state_dict[f'model.layers.{i}.mlp.W_up'] = torch.softmax(up_dict['w_params'], dim=1) state_dict[f'model.layers.{i}.mlp.W_gate'] = torch.softmax(gate_dict['w_params'], dim=1) for j in range(args.num_experts): state_dict[f'model.layers.{i}.mlp.experts.{j}.gate_proj.weight'] = gate_dict['A_params'][j] * gate_std state_dict[f'model.layers.{i}.mlp.experts.{j}.up_proj.weight'] = up_dict['A_params'][j] * up_std model.load_state_dict(state_dict) model.save_pretrained(args.save_dir) config.save_pretrained(args.save_dir) tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True) tokenizer.save_pretrained(args.save_dir) print(f"✅ MoBE model saved to {args.save_dir}") if __name__ == "__main__": args = parse_args() main(args)middle25projects/2024103840/src/scripts/MoBE-main/models/000077500000000000000000000000001506651042300225535ustar00rootroot00000000000000middle25projects/2024103840/src/scripts/MoBE-main/models/configuration_bailing_moe.py000066400000000000000000000054471506651042300303330ustar00rootroot00000000000000""" Bailing MoE model configuration """ from transformers.configuration_utils import PretrainedConfig class BailingMoeConfig(PretrainedConfig): model_type = "bailing_moe" def __init__( self, vocab_size=30592, hidden_size=1024, intermediate_size=None, num_hidden_layers=24, num_attention_heads=16, num_key_value_heads=0, hidden_act="silu", use_qkv_bias=False, # bailing only use_bias=True, # bailing only rms_norm_eps=1e-05, norm_head=False, # bailing only tie_word_embeddings=False, # PretrainedConfig key, here change default value. embedding_dropout=0.1, attention_dropout=0.1, output_dropout=0.1, initializer_range=0.02, max_position_embeddings=16384, rope_theta=10000.0, use_cache=True, use_sliding_window=False, sliding_window=4096, max_window_layers=28, rope_scaling=None, pad_token_id=126081, num_experts=16, num_shared_experts=0, num_experts_per_tok=2, norm_topk_prob=True, moe_intermediate_size=None, first_k_dense_replace=0, head_dim=None, output_router_logits=False, **kwargs, ): self.num_hidden_layers = num_hidden_layers self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.use_qkv_bias = use_qkv_bias self.use_bias = use_bias self.norm_head = norm_head self.rms_norm_eps = rms_norm_eps self.embedding_dropout = embedding_dropout self.attention_dropout = attention_dropout self.output_dropout = output_dropout self.initializer_range = initializer_range self.max_position_embeddings = max_position_embeddings self.rope_theta = rope_theta self.use_cache = use_cache self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers self.head_dim = head_dim or self.hidden_size // self.num_attention_heads self.rope_scaling = rope_scaling # MoE configs self.num_experts = num_experts self.num_shared_experts = num_shared_experts self.num_experts_per_tok = num_experts_per_tok self.norm_topk_prob = norm_topk_prob self.moe_intermediate_size = moe_intermediate_size self.first_k_dense_replace = first_k_dense_replace self.output_router_logits = output_router_logits super().__init__(pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)middle25projects/2024103840/src/scripts/MoBE-main/models/configuration_deepseek.py000066400000000000000000000246261506651042300276530ustar00rootroot00000000000000# Copy from https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class DeepseekV3Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DeepSeek-V3. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 129280): Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DeepseekV3Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 11008): Dimension of the MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1407): Dimension of the MoE representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. num_nextn_predict_layers (`int`, *optional*, defaults to 1): Number of nextn predict layers in the DeepSeekV3 Model. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. n_shared_experts (`int`, *optional*, defaults to None): Number of shared experts, None means dense model. n_routed_experts (`int`, *optional*, defaults to None): Number of routed experts, None means dense model. routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor or routed experts. topk_method (`str`, *optional*, defaults to `gready`): Topk method used in routed gate. n_group (`int`, *optional*, defaults to None): Number of groups for routed experts. topk_group (`int`, *optional*, defaults to None): Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups). num_experts_per_tok (`int`, *optional*, defaults to None): Number of selected experts, None means dense model. moe_layer_freq (`int`, *optional*, defaults to 1): The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers. first_k_dense_replace (`int`, *optional*, defaults to 0): Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). \--k dense layers--/ norm_topk_prob (`bool`, *optional*, defaults to False): Whether to normalize the weights of the routed experts. scoring_func (`str`, *optional*, defaults to 'softmax'): Method of computing expert weights. aux_loss_alpha (`float`, *optional*, defaults to 0.001): Auxiliary loss weight coefficient. seq_aux = (`bool`, *optional*, defaults to True): Whether to compute the auxiliary loss for each individual sample. num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update `max_position_embeddings` to the expected new maximum. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import DeepseekV3Model, DeepseekV3Config >>> # Initializing a Deepseek-V3 style configuration >>> configuration = DeepseekV3Config() >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "deepseek_v3" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=129280, hidden_size=7168, intermediate_size=18432, moe_intermediate_size = 2048, num_hidden_layers=61, num_nextn_predict_layers=1, num_attention_heads=128, num_key_value_heads=128, n_shared_experts = 1, n_routed_experts = 256, ep_size = 1, routed_scaling_factor = 2.5, kv_lora_rank = 512, q_lora_rank = 1536, qk_rope_head_dim = 64, v_head_dim = 128, qk_nope_head_dim = 128, topk_method = 'noaux_tc', n_group = 8, topk_group = 4, num_experts_per_tok = 8, moe_layer_freq = 1, first_k_dense_replace = 3, norm_topk_prob = True, scoring_func = 'sigmoid', aux_loss_alpha = 0.001, seq_aux = True, hidden_act="silu", max_position_embeddings=4096, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=None, bos_token_id=0, eos_token_id=1, pretraining_tp=1, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, attention_bias=False, attention_dropout=0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.moe_intermediate_size = moe_intermediate_size self.num_hidden_layers = num_hidden_layers self.num_nextn_predict_layers = num_nextn_predict_layers self.num_attention_heads = num_attention_heads self.n_shared_experts = n_shared_experts self.n_routed_experts = n_routed_experts self.ep_size = ep_size self.routed_scaling_factor = routed_scaling_factor self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.topk_method = topk_method self.n_group = n_group self.topk_group = topk_group self.num_experts_per_tok = num_experts_per_tok self.moe_layer_freq = moe_layer_freq self.first_k_dense_replace = first_k_dense_replace self.norm_topk_prob = norm_topk_prob self.scoring_func = scoring_func self.aux_loss_alpha = aux_loss_alpha self.seq_aux = seq_aux # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.attention_bias = attention_bias self.attention_dropout = attention_dropout super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, )middle25projects/2024103840/src/scripts/MoBE-main/models/modeling_bailing_mobe.py000066400000000000000000002314001506651042300274120ustar00rootroot00000000000000# coding=utf-8 # Copyright 2023 Antgroup and The HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch BailingMoE model.""" import math import warnings from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_attn_mask_utils import ( AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa, ) from transformers.modeling_outputs import ( MoeModelOutputWithPast, MoeCausalLMOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13 from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) from transformers.utils.import_utils import is_torch_fx_available from models.configuration_bailing_moe import BailingMoeConfig if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. # It means that the function will not be traced through and simply appear as a node in the graph. if is_torch_fx_available(): if not is_torch_greater_or_equal_than_1_13: import torch.fx _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "BailingMoeConfig" # Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func def load_balancing_loss_func( gate_logits_and_topk: Union[torch.Tensor, Tuple[torch.Tensor], None], num_experts: Optional[int] = None, top_k=2, attention_mask: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, int]: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between experts is too unbalanced. Args: gate_logits: Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts: Number of experts top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. """ if gate_logits_and_topk is None or not isinstance(gate_logits_and_topk, tuple): return 0 if isinstance(gate_logits_and_topk, tuple): # concatenated_gate_logits.shape = [batch_size * num_layers * seq_len, num_experts] concatenated_gate_logits = torch.cat([layer_gate[0] for layer_gate in gate_logits_and_topk], dim=0) # selected_experts.shape = [batch_size * num_layers * seq_len, top_k_experts] selected_experts = torch.cat([layer_gate[1] for layer_gate in gate_logits_and_topk], dim=0) selected_experts.to(concatenated_gate_logits.device) routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) if attention_mask is None: tokens_per_expert = torch.mean(expert_mask.float(), dim=0) # Compute the average probability of routing to these experts router_prob_per_expert = torch.mean(routing_weights, dim=0) else: batch_size, sequence_length = attention_mask.shape num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask expert_attention_mask = ( attention_mask[None, :, :, None, None] .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) .reshape(-1, top_k, num_experts) ) # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( expert_attention_mask, dim=0 ) # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert router_per_expert_attention_mask = ( attention_mask[None, :, :, None] .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) .reshape(-1, num_experts) ) # Compute the average probability of routing to these experts router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(router_per_expert_attention_mask, dim=0) overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) return overall_loss def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): warnings.warn( "Calling `transformers.models.BailingMoe.modeling_BailingMoe._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask" ) return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len) def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ): warnings.warn( "Calling `transformers.models.BailingMoe.modeling_BailingMoe._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.BailingMoe.modeling_BailingMoe.AttentionMaskConverter._make_causal_mask" ) return AttentionMaskConverter._make_causal_mask( input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length ) class BailingMoeRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ BailingMoeRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) ALL_LAYERNORM_LAYERS.append(BailingMoeRMSNorm) class BailingMoeRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) self.max_seq_len_cached = None def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.outer(t, self.inv_freq.to(t.device)) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->BailingMoe class BailingMoeLinearScalingRotaryEmbedding(BailingMoeRotaryEmbedding): """BailingMoeRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->BailingMoe class BailingMoeDynamicNTKScalingRotaryEmbedding(BailingMoeRotaryEmbedding): """BailingMoeRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) # Inverse dim formula to find dim based on number of rotations def yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048): return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base)) # Find dim range bounds based on rotations def yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048): low = math.floor(yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)) high = math.ceil(yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)) return max(low, 0), min(high, dim - 1) # Clamp values just in case def yarn_get_mscale(scale=1, mscale=1): if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 def yarn_linear_ramp_mask(min, max, dim): if min == max: max += 0.001 # Prevent singularity linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) ramp_func = torch.clamp(linear_func, 0, 1) return ramp_func class BailingMoeYarnRotaryEmbedding(BailingMoeRotaryEmbedding): def __init__( self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, original_max_position_embeddings=4096, beta_fast=32, beta_slow=1, mscale=1, mscale_all_dim=0, ): self.scaling_factor = scaling_factor self.original_max_position_embeddings = original_max_position_embeddings self.beta_fast = beta_fast self.beta_slow = beta_slow self.mscale = mscale self.mscale_all_dim = mscale_all_dim super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len dim = self.dim freq_extra = 1.0 / (self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)) freq_inter = 1.0 / ( self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) ) low, high = yarn_find_correction_range( self.beta_fast, self.beta_slow, dim, self.base, self.original_max_position_embeddings, ) inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(device=device, dtype=torch.float32) inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(seq_len, device=device, dtype=torch.float32) freqs = torch.outer(t, inv_freq) _mscale = float( yarn_get_mscale(self.scaling_factor, self.mscale) / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim) ) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False) self.register_buffer("sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False) # Copied from transformers.models.llama.modeling_llama.rotate_half def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class BailingMoeMLP(nn.Module): def __init__(self, config: BailingMoeConfig, intermediate_size: int): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) class BailingMoBEMLP(nn.Module): def __init__(self, config, hidden_size=None, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x, B_gate, B_up): down_proj = self.down_proj(self.act_fn(self.gate_proj(F.linear(x, B_gate))) * self.up_proj(F.linear(x, B_up))) return down_proj class BailingMoeGate(nn.Module): def __init__(self, config): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.num_experts = config.num_experts # topk selection algorithm self.norm_topk_prob = config.norm_topk_prob self.gating_dim = config.hidden_size self.weight = nn.Parameter(torch.empty((self.num_experts, self.gating_dim))) self.reset_parameters() def reset_parameters(self) -> None: import torch.nn.init as init init.kaiming_uniform_(self.weight, a=math.sqrt(5)) def forward(self, hidden_states, sort=False): h = hidden_states.shape[-1] # compute gating score hidden_states = hidden_states.view(-1, h) logits = F.linear(hidden_states, self.weight, None) scores = logits.softmax(dim=-1, dtype=torch.float32) # select top-k experts topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=sort) # norm gate to sum 1 if self.top_k > 1 and self.norm_topk_prob: denominator = topk_weight.sum(dim=-1, keepdim=True) topk_weight = topk_weight / denominator return topk_idx, topk_weight, logits class BailingMoBESparseMoeBlock(nn.Module): """ A mixed expert module containing shared experts. """ def __init__(self, config: BailingMoeConfig): super().__init__() self.config = config self.num_experts = config.num_experts self.num_experts_per_tok = config.num_experts_per_tok self._setup_experts() self.gate = BailingMoeGate(config) if config.num_shared_experts is not None: self.shared_experts = BailingMoeMLP( config=config, intermediate_size=config.moe_intermediate_size * config.num_shared_experts ) self.B_up = nn.Parameter(torch.zeros(config.num_B, config.moe_intermediate_size, config.hidden_size)) self.W_up = nn.Parameter(torch.zeros(config.num_experts, config.num_B)) self.B_gate = nn.Parameter(torch.zeros(config.num_B, config.moe_intermediate_size, config.hidden_size)) self.W_gate = nn.Parameter(torch.zeros(config.num_experts, config.num_B)) self.num_experts = config.num_experts self.moe_intermediate_size = config.moe_intermediate_size self.hidden_size = config.hidden_size self.num_B = config.num_B if config.activation == 'tanh': self.activation = nn.Tanh() else: self.activation = nn.SiLU() def _setup_experts(self): self.experts = nn.ModuleList( [ BailingMoBEMLP(config=self.config, intermediate_size=self.config.moe_intermediate_size) for _ in range(self.config.num_experts) ] ) def forward(self, hidden_states): batch_size, sequence_length, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) selected_experts, routing_weights, router_logits = self.gate(hidden_states) final_hidden_states = torch.zeros( (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device ) B_gate = self.activation(torch.matmul(self.W_gate, self.B_gate.view(self.num_B, -1))).view(self.num_experts, self.moe_intermediate_size, self.hidden_size) B_up = self.activation(torch.matmul(self.W_up, self.B_up.view(self.num_B, -1))).view(self.num_experts, self.moe_intermediate_size, self.hidden_size) expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0) for expert_idx in range(self.num_experts): expert_layer = self.experts[expert_idx] idx, top_x = torch.where(expert_mask[expert_idx]) if top_x.shape[0] == 0: continue current_state = hidden_states[None, top_x].reshape(-1, hidden_dim) current_hidden_states = expert_layer(current_state, B_gate[expert_idx], B_up[expert_idx]) * routing_weights[top_x, idx, None] final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) if self.config.num_shared_experts is not None: final_hidden_states += self.shared_experts(hidden_states) final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) return final_hidden_states, router_logits # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) # Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->BailingMoe class BailingMoeAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: BailingMoeConfig, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = config.head_dim or self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.is_causal = True self.query_key_value = nn.Linear( self.hidden_size, (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim, bias=config.use_qkv_bias, ) self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias) self._init_rope() def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = BailingMoeRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = BailingMoeLinearScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "dynamic": self.rotary_emb = BailingMoeDynamicNTKScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "yarn": kwargs = { key: self.config.rope_scaling[key] for key in [ "original_max_position_embeddings", "beta_fast", "beta_slow", "mscale", "mscale_all_dim", ] if key in self.config.rope_scaling } self.rotary_emb = BailingMoeYarnRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, **kwargs, ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) bsz, q_len, _ = hidden_states.size() qkv = self.query_key_value(hidden_states) qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) query_states, key_states, value_states = qkv.split( [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2 ) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: if self.layer_idx is None: raise ValueError( f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " "with a layer index." ) kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states / math.sqrt(self.head_dim), key_states.transpose(2, 3)) if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.dense(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->BailingMoe class BailingMoeFlashAttention2(BailingMoeAttention): """ BailingMoe flash attention module. This module inherits from `BailingMoeAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # BailingMoeFlashAttention2 attention does not support output_attentions if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) # overwrite attention_mask with padding_mask attention_mask = kwargs.pop("padding_mask") output_attentions = False bsz, q_len, _ = hidden_states.size() # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape qkv = self.query_key_value(hidden_states) qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) query_states, key_states, value_states = qkv.split( [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2 ) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently cast in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slow down training & inference so it is recommended to not cast the LayerNorms # in fp32. (BailingMoeRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: # Handle the case where the model is quantized if hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype elif torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate ) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.dense(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) query_length (`int`): The length of the query sequence in terms of tokens. This represents the number of tokens in the `query_states` tensor along the sequence dimension. It is used to determine the effective sequence length for attention computations. """ if not self._flash_attn_uses_top_left_mask: causal = self.is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in BailingMoeFlashAttention2 __init__. causal = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal ) return attn_output def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) # Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->BailingMoe class BailingMoeSdpaAttention(BailingMoeAttention): """ BailingMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `BailingMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from BailingMoeAttention.forward def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "BailingMoeModel is using BailingMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) bsz, q_len, _ = hidden_states.size() qkv = self.query_key_value(hidden_states) qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) query_states, key_states, value_states = qkv.split( [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2 ) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. if query_states.device.type == "cuda" and attention_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=self.attention_dropout if self.training else 0.0, # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1. is_causal=self.is_causal and attention_mask is None and q_len > 1, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.dense(attn_output) return attn_output, None, past_key_value BAILING_MOE_ATTENTION_CLASSES = { "eager": BailingMoeAttention, "flash_attention_2": BailingMoeFlashAttention2, "sdpa": BailingMoeSdpaAttention, } class BailingMoeDecoderLayer(nn.Module): def __init__(self, config: BailingMoeConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.attention = BAILING_MOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) self.mlp = ( BailingMoBESparseMoeBlock(config) if (config.num_experts is not None and layer_idx >= config.first_k_dense_replace) else BailingMoeMLP(config=config, intermediate_size=config.intermediate_size) ) self.input_layernorm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, query_sequence_length, key_sequence_length)` if default attention is used. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states output_attentions (`bool`, *optional*): Whether to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.attention( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) if isinstance(hidden_states, tuple): hidden_states, router_logits = hidden_states else: router_logits = None hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) if output_router_logits: outputs += (router_logits,) return outputs BAILINGMOE_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`BailingMoeConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare BailingMoe Model outputting raw hidden-states without any specific head on top.", BAILINGMOE_START_DOCSTRING, ) class BailingMoePreTrainedModel(PreTrainedModel): config_class = BailingMoeConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["BailingMoeDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() BAILINGMOE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance; - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare BailingMoe Model outputting raw hidden-states without any specific head on top.", BAILINGMOE_START_DOCSTRING, ) class BailingMoeModel(BailingMoePreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BailingMoeDecoderLayer`] Args: config: BailingMoeConfig """ def __init__(self, config: BailingMoeConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [BailingMoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self._use_sdpa = config._attn_implementation == "sdpa" self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self.norm = BailingMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.word_embeddings def set_input_embeddings(self, value): self.word_embeddings = value @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, MoeModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers." ) use_cache = False past_key_values_length = 0 if use_cache: use_legacy_cache = not isinstance(past_key_values, Cache) if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values_length = past_key_values.get_usable_length(seq_length) if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None elif self._use_sdpa and not output_attentions: # output_attentions=True can not be supported when using SDPA, and we fall back on # the manual implementation that requires a 4D causal mask in all cases. attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, ) else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length ) # embed positions hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_router_logits = () if output_router_logits else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_values, output_attentions, output_router_logits, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, output_router_logits=output_router_logits, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) if output_router_logits and layer_outputs[-1] is not None: all_router_logits += (layer_outputs[-1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits] if v is not None ) return MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, router_logits=all_router_logits, ) class BailingMoBEForCausalLM(BailingMoePreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config: BailingMoeConfig): super().__init__(config) self.model = BailingMoeModel(config) self.vocab_size = config.vocab_size self.norm_head = config.norm_head self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.word_embeddings def set_input_embeddings(self, value): self.model.word_embeddings = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def compute_logit(self, hidden_states): if self.norm_head: weight_float = self.lm_head.weight.float() norm = torch.norm(weight_float, p=2, dim=0, keepdim=True).clamp(min=1e-7) norm_weight = (weight_float / norm).to(hidden_states.dtype) logits = F.linear(hidden_states, norm_weight, None) else: logits = self.lm_head(hidden_states) return logits @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, **kwargs, ) -> Union[Tuple, MoeCausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer >>> model = BailingMoeForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, **kwargs, ) hidden_states = outputs[0] logits = self.compute_logit(hidden_states=hidden_states) logits = logits.float() loss = None aux_loss = None if output_router_logits: aux_loss = load_balancing_loss_func( outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok, attention_mask, ) if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] if output_router_logits: output = (aux_loss,) + output return (loss,) + output if loss is not None else output return MoeCausalLMOutputWithPast( loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, token_type_ids=None, **kwargs ): if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens max_cache_length = ( past_key_values.get_max_length() if hasattr(past_key_values, "get_max_length") else past_key_values.get_max_cache_shape() ) else: cache_length = past_length = past_key_values[0][0].shape[2] max_cache_length = None # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as input) if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. if ( max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length ): attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past # Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->Qwen2Moe, LLAMA->QWEN2MOE class BailingMoeForTokenClassification(BailingMoePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.num_experts = config.num_experts self.num_experts_per_tok = config.num_experts_per_tok self.model = BailingMoeModel(config) if getattr(config, "classifier_dropout", None) is not None: classifier_dropout = config.classifier_dropout elif getattr(config, "hidden_dropout", None) is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 self.dropout = nn.Dropout(classifier_dropout) self.score = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value @add_start_docstrings_to_model_forward(BAILINGMOE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MoeCausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.score(sequence_output) loss = None aux_loss = None if labels is not None: loss = self.loss_function(logits, labels, self.config) if output_router_logits: aux_loss = load_balancing_loss_func( outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok, attention_mask, ) if not return_dict: output = (logits,) + outputs[1:] if output_router_logits: output = (aux_loss,) + output return (loss,) + output if loss is not None else output if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MoeCausalLMOutputWithPast( loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, )middle25projects/2024103840/src/scripts/MoBE-main/models/modeling_deepseek_v3_mobe.py000066400000000000000000001367671506651042300302260ustar00rootroot00000000000000# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/deepseek_v3/modular_deepseek_v3.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_deepseek_v3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 import math from functools import partial from typing import Callable, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import nn from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache, StaticCache from transformers.generation import GenerationMixin from transformers.modeling_attn_mask_utils import AttentionMaskConverter from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import ( LossKwargs, add_start_docstrings, add_start_docstrings_to_model_forward, can_return_tuple, is_torch_flex_attn_available, logging, replace_return_docstrings, ) from transformers.utils.deprecation import deprecate_kwarg from transformers.models.deepseek_v3.configuration_deepseek_v3 import DeepseekV3Config if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from transformers.integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DeepseekV3Config" class DeepseekV3RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ DeepseekV3RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class DeepseekV3RotaryEmbedding(nn.Module): def __init__(self, config: DeepseekV3Config, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) class DeepseekV3MLP(nn.Module): def __init__(self, config, hidden_size=None, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class DeepseekV3MoBEMLP(nn.Module): def __init__(self, config, hidden_size=None, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x, B_gate, B_up): down_proj = self.down_proj(self.act_fn(self.gate_proj(F.linear(x, B_gate))) * self.up_proj(F.linear(x, B_up))) return down_proj class DeepseekV3TopkRouter(nn.Module): def __init__(self, config): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.n_routed_experts = config.n_routed_experts self.routed_scaling_factor = config.routed_scaling_factor self.n_group = config.n_group self.topk_group = config.topk_group self.norm_topk_prob = config.norm_topk_prob self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size))) self.register_buffer("e_score_correction_bias", torch.zeros((self.n_routed_experts))) @torch.no_grad() def get_topk_indices(self, scores): scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0) group_scores = ( scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group) .topk(2, dim=-1)[0] .sum(dim=-1) ) group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1] group_mask = torch.zeros_like(group_scores) group_mask.scatter_(1, group_idx, 1) score_mask = ( group_mask.unsqueeze(-1) .expand(-1, self.n_group, self.n_routed_experts // self.n_group) .reshape(-1, self.n_routed_experts) ) scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1] return topk_indices def forward(self, hidden_states): hidden_states = hidden_states.view(-1, self.config.hidden_size) router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32)) scores = router_logits.sigmoid() topk_indices = self.get_topk_indices(scores) topk_weights = scores.gather(1, topk_indices) if self.norm_topk_prob: denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20 topk_weights /= denominator topk_weights = topk_weights * self.routed_scaling_factor return topk_indices, topk_weights class DeepseekV3MoBE(nn.Module): """ A mixed expert module containing shared experts. """ def __init__(self, config): super().__init__() self.config = config self.num_B = config.num_B self.experts = nn.ModuleList( [ DeepseekV3MoBEMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.n_routed_experts) ] ) self.gate = DeepseekV3TopkRouter(config) self.shared_experts = DeepseekV3MLP( config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts ) self.B_up = nn.Parameter(torch.zeros(config.num_B, config.moe_intermediate_size, config.hidden_size)) self.W_up = nn.Parameter(torch.zeros(config.n_routed_experts, config.num_B)) self.B_gate = nn.Parameter(torch.zeros(config.num_B, config.moe_intermediate_size, config.hidden_size)) self.W_gate = nn.Parameter(torch.zeros(config.n_routed_experts, config.num_B)) self.num_experts = config.n_routed_experts self.moe_intermediate_size = config.moe_intermediate_size self.hidden_size = config.hidden_size if config.activation == 'tanh': self.activation = nn.Tanh() else: self.activation = nn.SiLU() def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor): r""" CALL FOR CONTRIBUTION! I don't have time to optimise this right now, but expert weights need to be fused to not have to do a loop here (deepseek has 256 experts soooo yeah). """ final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype) expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts)) expert_mask = expert_mask.permute(2, 0, 1) B_gate = self.activation(torch.matmul(self.W_gate, self.B_gate.view(self.num_B, -1))).view(self.num_experts, self.moe_intermediate_size, self.hidden_size) B_up = self.activation(torch.matmul(self.W_up, self.B_up.view(self.num_B, -1))).view(self.num_experts, self.moe_intermediate_size, self.hidden_size) for expert_idx in range(len(self.experts)): expert = self.experts[expert_idx] mask = expert_mask[expert_idx] token_indices, weight_indices = torch.where(mask) if token_indices.numel() > 0: expert_weights = topk_weights[token_indices, weight_indices] expert_input = hidden_states[token_indices] expert_output = expert(expert_input, B_gate[expert_idx], B_up[expert_idx]) weighted_output = expert_output * expert_weights.unsqueeze(-1) final_hidden_states.index_add_(0, token_indices, weighted_output) # in original deepseek, the output of the experts are gathered once we leave this module # thus the moe module is itelsf an IsolatedParallel module # and all expert are "local" meaning we shard but we don't gather return final_hidden_states.type(hidden_states.dtype) def forward(self, hidden_states): residuals = hidden_states orig_shape = hidden_states.shape topk_indices, topk_weights = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape) hidden_states = hidden_states + self.shared_experts(residuals) return hidden_states def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): r""" TODO let's just use the original freqcis computation to not have the view transpose + reshape! This is not optimized! Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) b, h, s, d = q.shape q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) b, h, s, d = k.shape k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def yarn_get_mscale(scale=1, mscale=1): if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 class DeepseekV3Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: DeepseekV3Config, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.attention_dropout = config.attention_dropout self.num_heads = config.num_attention_heads self.rope_theta = config.rope_theta self.q_lora_rank = config.q_lora_rank self.qk_rope_head_dim = config.qk_rope_head_dim self.kv_lora_rank = config.kv_lora_rank self.v_head_dim = config.v_head_dim self.qk_nope_head_dim = config.qk_nope_head_dim self.qk_head_dim = config.qk_head_dim self.is_causal = True self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias) self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank) self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False) self.kv_a_proj_with_mqa = nn.Linear( config.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=config.attention_bias, ) self.kv_a_layernorm = DeepseekV3RMSNorm(self.kv_lora_rank) self.kv_b_proj = nn.Linear( self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False, ) self.o_proj = nn.Linear( self.num_heads * self.v_head_dim, config.hidden_size, bias=config.attention_bias, ) self.scaling = self.qk_head_dim ** (-0.5) if self.config.rope_scaling is not None: mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) scaling_factor = self.config.rope_scaling["factor"] if mscale_all_dim: mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) self.scaling = self.scaling * mscale * mscale def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: batch_size, seq_length = hidden_states.shape[:-1] query_shape = (batch_size, seq_length, -1, self.qk_head_dim) key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim) q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))).view(query_shape).transpose(1, 2) q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) compressed_kv = self.kv_a_proj_with_mqa(hidden_states) k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2) k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1) k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim) cos, sin = position_embeddings if self.config.rope_interleave: # support using interleaved weights for efficiency q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin) else: q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin) k_rot = k_rot.expand(*k_pass.shape[:-1], -1) query_states = torch.cat((q_pass, q_rot), dim=-1) key_states = torch.cat((k_pass, k_rot), dim=-1) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim: value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim]) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim: attn_output = attn_output[:, :, :, : self.v_head_dim] attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class DeepseekV3DecoderLayer(nn.Module): def __init__(self, config: DeepseekV3Config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = DeepseekV3Attention(config=config, layer_idx=layer_idx) if layer_idx >= config.first_k_dense_replace: self.mlp = DeepseekV3MoBE(config) else: self.mlp = DeepseekV3MLP(config) self.input_layernorm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs DEEPSEEK_V3_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) Parameters: config ([`DeepseekV3Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.", DEEPSEEK_V3_START_DOCSTRING, ) class DeepseekV3PreTrainedModel(PreTrainedModel): config_class = DeepseekV3Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["DeepseekV3DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_flex_attn = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True _supports_attention_backend = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, DeepseekV3TopkRouter): module.weight.data.normal_(mean=0.0, std=std) elif isinstance(module, nn.Parameter): module.weight.data.normal_(mean=0.0, std=std) DEEPSEEK_V3_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ @add_start_docstrings( "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.", DEEPSEEK_V3_START_DOCSTRING, ) class DeepseekV3Model(DeepseekV3PreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`] Args: config: DeepseekV3Config """ _keys_to_ignore_on_load_unexpected = [r"model\.layers\.61.*"] def __init__(self, config: DeepseekV3Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [DeepseekV3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = DeepseekV3RotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @can_return_tuple @add_start_docstrings_to_model_forward(DEEPSEEK_V3_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> BaseModelOutputWithPast: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache if not isinstance(past_key_values, (type(None), Cache)): raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache() if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( partial(decoder_layer.__call__, **flash_attn_kwargs), hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) if isinstance(attention_mask, BlockMask): return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... class DeepseekV3MoBEForCausalLM(DeepseekV3PreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = DeepseekV3Model(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model @can_return_tuple @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") @add_start_docstrings_to_model_forward(DEEPSEEK_V3_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[KwargsForCausalLM], ) -> CausalLMOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: Example: ```python >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM >>> model = DeepseekV3ForCausalLM.from_pretrained("meta-deepseek_v3/DeepseekV3-2-7b-hf") >>> tokenizer = AutoTokenizer.from_pretrained("meta-deepseek_v3/DeepseekV3-2-7b-hf") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs: BaseModelOutputWithPast = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = ["DeepseekV3PreTrainedModel", "DeepseekV3Model", "DeepseekV3ForCausalLM"]middle25projects/2024103840/src/scripts/MoBE-main/models/modeling_kimi_k2_mobe.py000066400000000000000000002242271506651042300273430ustar00rootroot00000000000000# coding=utf-8 # Copyright 2023 DeepSeek-AI and The HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DeepSeek model.""" import math import warnings from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_attn_mask_utils import ( AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask, ) from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.pytorch_utils import ( ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13, ) from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, ) from transformers.utils.import_utils import is_torch_fx_available from models.configuration_deepseek import DeepseekV3Config import torch.distributed as dist import numpy as np if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. # It means that the function will not be traced through and simply appear as a node in the graph. if is_torch_fx_available(): if not is_torch_greater_or_equal_than_1_13: import torch.fx _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DeepseekV3Config" def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad( torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0) ) return ( indices, cu_seqlens, max_seqlen_in_batch, ) class DeepseekV3RMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ DeepseekV3RMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) ALL_LAYERNORM_LAYERS.append(DeepseekV3RMSNorm) class DeepseekV3RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / ( self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) ) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(), ) self.max_seq_len_cached = None def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange( self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype ) freqs = torch.outer(t, self.inv_freq.to(t.device)) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if self.max_seq_len_cached is None or seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV3 class DeepseekV3LinearScalingRotaryEmbedding(DeepseekV3RotaryEmbedding): """DeepseekV3RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__( self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, ): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange( self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype ) t = t / self.scaling_factor freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV3 class DeepseekV3DynamicNTKScalingRotaryEmbedding(DeepseekV3RotaryEmbedding): """DeepseekV3RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__( self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, ): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / ( base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim) ) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange( self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype ) freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) # Inverse dim formula to find dim based on number of rotations def yarn_find_correction_dim( num_rotations, dim, base=10000, max_position_embeddings=2048 ): return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / ( 2 * math.log(base) ) # Find dim range bounds based on rotations def yarn_find_correction_range( low_rot, high_rot, dim, base=10000, max_position_embeddings=2048 ): low = math.floor( yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings) ) high = math.ceil( yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings) ) return max(low, 0), min(high, dim - 1) # Clamp values just in case def yarn_get_mscale(scale=1, mscale=1): if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 def yarn_linear_ramp_mask(min, max, dim): if min == max: max += 0.001 # Prevent singularity linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) ramp_func = torch.clamp(linear_func, 0, 1) return ramp_func class DeepseekV3YarnRotaryEmbedding(DeepseekV3RotaryEmbedding): def __init__( self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, original_max_position_embeddings=4096, beta_fast=32, beta_slow=1, mscale=1, mscale_all_dim=0, ): self.scaling_factor = scaling_factor self.original_max_position_embeddings = original_max_position_embeddings self.beta_fast = beta_fast self.beta_slow = beta_slow self.mscale = mscale self.mscale_all_dim = mscale_all_dim super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len dim = self.dim freq_extra = 1.0 / ( self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) ) freq_inter = 1.0 / ( self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) ) low, high = yarn_find_correction_range( self.beta_fast, self.beta_slow, dim, self.base, self.original_max_position_embeddings, ) inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to( device=device, dtype=torch.float32 ) inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(seq_len, device=device, dtype=torch.float32) freqs = torch.outer(t, inv_freq) _mscale = float( yarn_get_mscale(self.scaling_factor, self.mscale) / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim) ) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer( "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False ) self.register_buffer( "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False ) # Copied from transformers.models.llama.modeling_llama.rotate_half def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) b, h, s, d = q.shape q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) b, h, s, d = k.shape k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class DeepseekV3MoBEMLP(nn.Module): def __init__(self, config, hidden_size=None, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x, B_gate, B_up): down_proj = self.down_proj(self.act_fn(self.gate_proj(F.linear(x, B_gate))) * self.up_proj(F.linear(x, B_up))) return down_proj class DeepseekV3MLP(nn.Module): def __init__(self, config, hidden_size=None, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size if hidden_size is None else hidden_size self.intermediate_size = ( config.intermediate_size if intermediate_size is None else intermediate_size ) self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class MoEGate(nn.Module): def __init__(self, config): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.n_routed_experts = config.n_routed_experts self.routed_scaling_factor = config.routed_scaling_factor self.scoring_func = config.scoring_func self.seq_aux = config.seq_aux self.topk_method = config.topk_method self.n_group = config.n_group self.topk_group = config.topk_group # topk selection algorithm self.norm_topk_prob = config.norm_topk_prob self.gating_dim = config.hidden_size self.weight = nn.Parameter( torch.empty((self.n_routed_experts, self.gating_dim)) ) if self.topk_method == "noaux_tc": self.e_score_correction_bias = nn.Parameter( torch.empty((self.n_routed_experts)) ) self.reset_parameters() def reset_parameters(self) -> None: import torch.nn.init as init init.kaiming_uniform_(self.weight, a=math.sqrt(5)) def forward(self, hidden_states): bsz, seq_len, h = hidden_states.shape ### compute gating score hidden_states = hidden_states.view(-1, h) logits = F.linear( hidden_states.type(torch.float32), self.weight.type(torch.float32), None ) if self.scoring_func == "sigmoid": scores = logits.sigmoid() else: raise NotImplementedError( f"insupportable scoring function for MoE gating: {self.scoring_func}" ) ### select top-k experts if self.topk_method == "noaux_tc": assert not self.training scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0) group_scores = ( scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim = -1) ) # [n, n_group] group_idx = torch.topk( group_scores, k=self.topk_group, dim=-1, sorted=False )[ 1 ] # [n, top_k_group] group_mask = torch.zeros_like(group_scores) # [n, n_group] group_mask.scatter_(1, group_idx, 1) # [n, n_group] score_mask = ( group_mask.unsqueeze(-1) .expand( bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group ) .reshape(bsz * seq_len, -1) ) # [n, e] tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), 0.0) # [n, e] _, topk_idx = torch.topk( tmp_scores, k=self.top_k, dim=-1, sorted=False ) topk_weight = scores.gather(1, topk_idx) else: raise NotImplementedError( f"insupportable TopK function for MoE gating: {self.topk_method}" ) ### norm gate to sum 1 if self.top_k > 1 and self.norm_topk_prob: denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 topk_weight = topk_weight / denominator topk_weight = topk_weight * self.routed_scaling_factor # must multiply the scaling factor return topk_idx, topk_weight class DeepseekV3MoBE(nn.Module): """ A mixed expert module containing shared experts. """ def __init__(self, config): super().__init__() self.config = config self.num_B = config.num_B self.experts = nn.ModuleList( [ DeepseekV3MoBEMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.n_routed_experts) ] ) self.gate = MoEGate(config) self.shared_experts = DeepseekV3MLP( config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts ) self.B_up_0 = nn.Parameter(torch.zeros(config.num_B // 2, config.moe_intermediate_size, config.hidden_size)) self.W_up_0 = nn.Parameter(torch.zeros(config.n_routed_experts // 2, config.num_B // 2)) self.B_gate_0 = nn.Parameter(torch.zeros(config.num_B // 2, config.moe_intermediate_size, config.hidden_size)) self.W_gate_0 = nn.Parameter(torch.zeros(config.n_routed_experts // 2, config.num_B // 2)) self.B_up_1 = nn.Parameter(torch.zeros(config.num_B // 2, config.moe_intermediate_size, config.hidden_size)) self.W_up_1 = nn.Parameter(torch.zeros(config.n_routed_experts // 2, config.num_B // 2)) self.B_gate_1 = nn.Parameter(torch.zeros(config.num_B // 2, config.moe_intermediate_size, config.hidden_size)) self.W_gate_1 = nn.Parameter(torch.zeros(config.n_routed_experts // 2, config.num_B // 2)) self.num_experts = config.n_routed_experts self.moe_intermediate_size = config.moe_intermediate_size self.hidden_size = config.hidden_size if config.activation == 'tanh': self.activation = nn.Tanh() else: self.activation = nn.SiLU() def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor): r""" CALL FOR CONTRIBUTION! I don't have time to optimise this right now, but expert weights need to be fused to not have to do a loop here (deepseek has 256 experts soooo yeah). """ final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype) expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts)) expert_mask = expert_mask.permute(2, 0, 1) B_gate = torch.empty(self.num_experts, self.moe_intermediate_size, self.hidden_size, device=self.W_gate_0.device, dtype=self.B_gate_0.dtype) B_up = torch.empty(self.num_experts, self.moe_intermediate_size, self.hidden_size, device=self.W_up_0.device, dtype=self.B_up_0.dtype) B_gate[: self.num_experts // 2] = self.activation(torch.matmul(self.W_gate_0, self.B_gate_0.view(self.num_B // 2, -1))).view(self.num_experts // 2, self.moe_intermediate_size, self.hidden_size) B_up[: self.num_experts // 2] = self.activation(torch.matmul(self.W_up_0, self.B_up_0.view(self.num_B // 2, -1))).view(self.num_experts // 2, self.moe_intermediate_size, self.hidden_size) B_gate[self.num_experts // 2 :] = self.activation(torch.matmul(self.W_gate_1, self.B_gate_1.view(self.num_B // 2, -1))).view(self.num_experts // 2, self.moe_intermediate_size, self.hidden_size) B_up[self.num_experts // 2 :] = self.activation(torch.matmul(self.W_up_1, self.B_up_1.view(self.num_B // 2, -1))).view(self.num_experts // 2, self.moe_intermediate_size, self.hidden_size) for expert_idx in range(len(self.experts)): expert = self.experts[expert_idx] mask = expert_mask[expert_idx] token_indices, weight_indices = torch.where(mask) if token_indices.numel() > 0: expert_weights = topk_weights[token_indices, weight_indices] expert_input = hidden_states[token_indices] expert_output = expert(expert_input, B_gate[expert_idx], B_up[expert_idx]) weighted_output = expert_output * expert_weights.unsqueeze(-1) final_hidden_states.index_add_(0, token_indices, weighted_output) # in original deepseek, the output of the experts are gathered once we leave this module # thus the moe module is itelsf an IsolatedParallel module # and all expert are "local" meaning we shard but we don't gather return final_hidden_states.type(hidden_states.dtype) def forward(self, hidden_states): residuals = hidden_states orig_shape = hidden_states.shape topk_indices, topk_weights = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape) hidden_states = hidden_states + self.shared_experts(residuals) return hidden_states # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand( batch, num_key_value_heads, n_rep, slen, head_dim ) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) # Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->DeepseekV3 class DeepseekV3Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: DeepseekV3Config, layer_idx: Optional[int] = None): super().__init__() self.config = config self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.q_lora_rank = config.q_lora_rank self.qk_rope_head_dim = config.qk_rope_head_dim self.kv_lora_rank = config.kv_lora_rank self.v_head_dim = config.v_head_dim self.qk_nope_head_dim = config.qk_nope_head_dim self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim self.is_causal = True if self.q_lora_rank is None: self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.q_head_dim, bias=False ) else: self.q_a_proj = nn.Linear( self.hidden_size, config.q_lora_rank, bias=config.attention_bias ) self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank) self.q_b_proj = nn.Linear( config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False ) self.kv_a_proj_with_mqa = nn.Linear( self.hidden_size, config.kv_lora_rank + config.qk_rope_head_dim, bias=config.attention_bias, ) self.kv_a_layernorm = DeepseekV3RMSNorm(config.kv_lora_rank) self.kv_b_proj = nn.Linear( config.kv_lora_rank, self.num_heads * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), bias=False, ) self.o_proj = nn.Linear( self.num_heads * self.v_head_dim, self.hidden_size, bias=config.attention_bias, ) self._init_rope() self.softmax_scale = self.q_head_dim ** (-0.5) if self.config.rope_scaling is not None: mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) scaling_factor = self.config.rope_scaling["factor"] if mscale_all_dim: mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) self.softmax_scale = self.softmax_scale * mscale * mscale def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = DeepseekV3RotaryEmbedding( self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = DeepseekV3LinearScalingRotaryEmbedding( self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "dynamic": self.rotary_emb = DeepseekV3DynamicNTKScalingRotaryEmbedding( self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "yarn": kwargs = { key: self.config.rope_scaling[key] for key in [ "original_max_position_embeddings", "beta_fast", "beta_slow", "mscale", "mscale_all_dim", ] if key in self.config.rope_scaling } self.rotary_emb = DeepseekV3YarnRotaryEmbedding( self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, **kwargs, ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return ( tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim) .transpose(1, 2) .contiguous() ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) bsz, q_len, _ = hidden_states.size() if self.q_lora_rank is None: q = self.q_proj(hidden_states) else: q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) q_nope, q_pe = torch.split( q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1 ) compressed_kv = self.kv_a_proj_with_mqa(hidden_states) compressed_kv, k_pe = torch.split( compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1 ) k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) kv = ( self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) .transpose(1, 2) ) k_nope, value_states = torch.split( kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1 ) kv_seq_len = value_states.shape[-2] if past_key_value is not None: if self.layer_idx is None: raise ValueError( f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " "with a layer index." ) kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) query_states[:, :, :, : self.qk_nope_head_dim] = q_nope query_states[:, :, :, self.qk_nope_head_dim :] = q_pe key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) key_states[:, :, :, : self.qk_nope_head_dim] = k_nope key_states[:, :, :, self.qk_nope_head_dim :] = k_pe if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, cache_kwargs ) attn_weights = ( torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale ) if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}" ) assert attention_mask is not None if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax( attn_weights, dim=-1, dtype=torch.float32 ).to(query_states.dtype) attn_weights = nn.functional.dropout( attn_weights, p=self.attention_dropout, training=self.training ) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.v_head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV3 class DeepseekV3FlashAttention2(DeepseekV3Attention): """ DeepseekV3 flash attention module. This module inherits from `DeepseekV3Attention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # DeepseekV3FlashAttention2 attention does not support output_attentions if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) # overwrite attention_mask with padding_mask attention_mask = kwargs.pop("padding_mask") output_attentions = False bsz, q_len, _ = hidden_states.size() if self.q_lora_rank is None: q = self.q_proj(hidden_states) else: q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) q_nope, q_pe = torch.split( q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1 ) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape compressed_kv = self.kv_a_proj_with_mqa(hidden_states) compressed_kv, k_pe = torch.split( compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1 ) k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) kv = ( self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) .transpose(1, 2) ) k_nope, value_states = torch.split( kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1 ) kv_seq_len = value_states.shape[-2] kv_seq_len = value_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) query_states[:, :, :, : self.qk_nope_head_dim] = q_nope query_states[:, :, :, self.qk_nope_head_dim :] = q_pe key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) key_states[:, :, :, : self.qk_nope_head_dim] = k_nope key_states[:, :, :, self.qk_nope_head_dim :] = k_pe if self.q_head_dim != self.v_head_dim: value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim]) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, cache_kwargs ) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (DeepseekV3RMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: # Handle the case where the model is quantized if hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype elif torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() else: target_dtype = ( self.q_proj.weight.dtype if self.q_lora_rank is None else self.q_a_proj.weight.dtype ) logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, softmax_scale=self.softmax_scale, ) if self.q_head_dim != self.v_head_dim: attn_output = attn_output[:, :, :, : self.v_head_dim] attn_output = attn_output.reshape( bsz, q_len, self.num_heads * self.v_head_dim ).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None, ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) """ if not self._flash_attn_uses_top_left_mask: causal = self.is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in DeepseekV3FlashAttention2 __init__. causal = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] ( query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens, ) = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input( attn_output_unpad, indices_q, batch_size, query_length ) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, ) return attn_output def _upad_input( self, query_layer, key_layer, value_layer, attention_mask, query_length ): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k, ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k, ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k, ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( query_layer, attention_mask ) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) ATTENTION_CLASSES = { "eager": DeepseekV3Attention, "flash_attention_2": DeepseekV3FlashAttention2, } class DeepseekV3DecoderLayer(nn.Module): def __init__(self, config: DeepseekV3Config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = ATTENTION_CLASSES[config._attn_implementation]( config=config, layer_idx=layer_idx ) self.mlp = ( DeepseekV3MoBE(config) if ( config.n_routed_experts is not None and layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0 ) else DeepseekV3MLP(config) ) self.input_layernorm = DeepseekV3RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.post_attention_layernorm = DeepseekV3RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, **kwargs, ) -> Tuple[ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] ]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, query_sequence_length, key_sequence_length)` if default attention is used. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" ) residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, **kwargs, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs DeepseekV3_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`DeepseekV3Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.", DeepseekV3_START_DOCSTRING, ) class DeepseekV3PreTrainedModel(PreTrainedModel): config_class = DeepseekV3Config base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["DeepseekV3DecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True _supports_cache_class = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() DeepseekV3_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance; - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare DeepseekV3 Model outputting raw hidden-states without any specific head on top.", DeepseekV3_START_DOCSTRING, ) class DeepseekV3Model(DeepseekV3PreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV3DecoderLayer`] Args: config: DeepseekV3Config """ def __init__(self, config: DeepseekV3Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding( config.vocab_size, config.hidden_size, self.padding_idx ) self.layers = nn.ModuleList( [ DeepseekV3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers) ] ) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time" ) elif input_ids is not None: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") past_key_values_length = 0 if use_cache: use_legacy_cache = not isinstance(past_key_values, Cache) if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values_length = past_key_values.get_usable_length(seq_length) if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device, ) position_ids = position_ids.unsqueeze(0) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = ( attention_mask if (attention_mask is not None and 0 in attention_mask) else None ) else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, ) # embed positions hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = ( next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache ) if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) class KimiK2MoBEForCausalLM(DeepseekV3PreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = DeepseekV3Model(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC ) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) logits = logits.float() loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs, ): if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.seen_tokens max_cache_length = past_key_values.get_max_cache_shape() else: cache_length = past_length = past_key_values[0][0].shape[2] max_cache_length = None # Keep only the unprocessed tokens: # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as # input) if ( attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1] ): input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. if ( max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length ): attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple( past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past ), ) return reordered_past @add_start_docstrings( """ The DeepseekV3 Model transformer with a sequence classification head on top (linear layer). [`DeepseekV3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, DeepseekV3_START_DOCSTRING, ) class DeepseekV3ForSequenceClassification(DeepseekV3PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = DeepseekV3Model(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value @add_start_docstrings_to_model_forward(DeepseekV3_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) transformer_outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError( "Cannot handle batch sizes > 1 if no padding token is defined." ) if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = ( torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 ).to(logits.device) else: sequence_lengths = -1 pooled_logits = logits[ torch.arange(batch_size, device=logits.device), sequence_lengths ] loss = None if labels is not None: labels = labels.to(logits.device) if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and ( labels.dtype == torch.long or labels.dtype == torch.int ): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct( pooled_logits.view(-1, self.num_labels), labels.view(-1) ) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )middle25projects/2024103840/src/scripts/MoBE-main/models/modeling_qwen3_mobe.py000066400000000000000000001722271506651042300270550ustar00rootroot00000000000000# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/qwen3_moe/modular_qwen3_moe.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_qwen3_moe.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time from functools import partial from typing import Callable, List, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import nn from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache from transformers.generation import GenerationMixin from transformers.modeling_attn_mask_utils import AttentionMaskConverter from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, MoeCausalLMOutputWithPast, MoeModelOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import ( LossKwargs, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, can_return_tuple, logging, replace_return_docstrings, ) from transformers.utils.deprecation import deprecate_kwarg from transformers.models.qwen3_moe.configuration_qwen3_moe import Qwen3MoeConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "Qwen/Qwen3-MoE-15B-A2B" _CONFIG_FOR_DOC = "Qwen3MoeConfig" def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights class Qwen3MoeAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Qwen3MoeConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) self.q_norm = Qwen3MoeRMSNorm(self.head_dim, eps=config.rms_norm_eps) # unlike olmo, only on the head dim! self.k_norm = Qwen3MoeRMSNorm(self.head_dim, eps=config.rms_norm_eps) # thus post q_norm does not need reshape self.sliding_window = config.sliding_window if not ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): self.sliding_window = None def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2) key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=self.sliding_window, # diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class Qwen3MoeMLP(nn.Module): def __init__(self, config, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj class Qwen3MoBEMLP(nn.Module): def __init__(self, config, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size self.gate_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.intermediate_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x, B_gate, B_up): down_proj = self.down_proj(self.act_fn(self.gate_proj(F.linear(x, B_gate))) * self.up_proj(F.linear(x, B_up))) return down_proj class Qwen3SparseMoBEBlock(nn.Module): def __init__(self, config, layer_idx): super().__init__() self.num_experts = config.num_experts self.top_k = config.num_experts_per_tok self.norm_topk_prob = config.norm_topk_prob self.num_B = config.num_B self.moe_intermediate_size = config.moe_intermediate_size self.hidden_size = config.hidden_size # gating self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False) self.experts = nn.ModuleList( [Qwen3MoBEMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)] ) self.B_up = nn.Parameter(torch.zeros(config.num_B, config.moe_intermediate_size, config.hidden_size)) self.W_up = nn.Parameter(torch.zeros(self.num_experts, config.num_B)) self.B_gate = nn.Parameter(torch.zeros(config.num_B, config.moe_intermediate_size, config.hidden_size)) self.W_gate = nn.Parameter(torch.zeros(self.num_experts, config.num_B)) if config.activation == 'tanh': self.activation = nn.Tanh() else: self.activation = nn.SiLU() self.layer_idx = layer_idx def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: """ """ batch_size, sequence_length, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (batch * sequence_length, n_experts) router_logits = self.gate(hidden_states) routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) if self.norm_topk_prob: # only diff with mixtral sparse moe block! routing_weights /= routing_weights.sum(dim=-1, keepdim=True) # we cast back to the input dtype routing_weights = routing_weights.to(hidden_states.dtype) final_hidden_states = torch.zeros_like(hidden_states) # One hot encode the selected experts to create an expert mask # this will be used to easily index which expert is going to be sollicitated expert_mask = F.one_hot(selected_experts, self.num_experts).permute(2, 1, 0) B_gate = self.activation(torch.matmul(self.W_gate, self.B_gate.view(self.num_B, -1))).view(self.num_experts, self.moe_intermediate_size, self.hidden_size) B_up = self.activation(torch.matmul(self.W_up, self.B_up.view(self.num_B, -1))).view(self.num_experts, self.moe_intermediate_size, self.hidden_size) #print(B_gate) # Loop over all available experts in the model and perform the computation on each expert for expert_idx in range(self.num_experts): expert_layer = self.experts[expert_idx] idx, top_x = torch.where(expert_mask[expert_idx]) # Index the correct hidden states and compute the expert hidden state for # the current expert. We need to make sure to multiply the output hidden # states by `routing_weights` on the corresponding tokens (top-1 and top-2) current_state = hidden_states[None, top_x].reshape(-1, hidden_dim) current_hidden_states = expert_layer(current_state, B_gate[expert_idx], B_up[expert_idx]) * routing_weights[top_x, idx, None] # However `index_add_` only support torch tensors for indexing so we'll use # the `top_x` tensor here. final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) # if self.layer_idx == 0: # print("before:", current_state) final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) # if self.layer_idx == 0: # print("after:", final_hidden_states) # print(B_gate, B_up) return final_hidden_states, router_logits class Qwen3MoeRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ Qwen3MoeRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class Qwen3MoeDecoderLayer(nn.Module): def __init__(self, config: Qwen3MoeConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = Qwen3MoeAttention(config, layer_idx) self.mlp = Qwen3MoeMLP(config) self.self_attn = Qwen3MoeAttention(config, layer_idx) if (layer_idx not in config.mlp_only_layers) and ( config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0 ): self.mlp = Qwen3SparseMoBEBlock(config, layer_idx) else: self.mlp = Qwen3MoeMLP(config, intermediate_size=config.intermediate_size) self.input_layernorm = Qwen3MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = Qwen3MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, output_router_logits: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, sequence_length)` where padding elements are indicated by 0. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_router_logits (`bool`, *optional*): Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, with `head_dim` being the embedding dimension of each attention head. kwargs (`dict`, *optional*): Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code into the model """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) if isinstance(hidden_states, tuple): hidden_states, router_logits = hidden_states else: router_logits = None hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if output_router_logits: outputs += (router_logits,) return outputs class Qwen3MoeRotaryEmbedding(nn.Module): def __init__(self, config: Qwen3MoeConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) QWEN3_MOE_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) Parameters: config ([`Qwen3MoeConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare Qwen3Moe Model outputting raw hidden-states without any specific head on top.", QWEN3_MOE_START_DOCSTRING, ) class Qwen3MoePreTrainedModel(PreTrainedModel): config_class = Qwen3MoeConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Qwen3MoeDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_flex_attn = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported) _supports_attention_backend = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() QWEN3_MOE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ @add_start_docstrings( "The bare Qwen3Moe Model outputting raw hidden-states without any specific head on top.", QWEN3_MOE_START_DOCSTRING, ) class Qwen3MoeModel(Qwen3MoePreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen3MoeDecoderLayer`] Args: config: Qwen3MoeConfig """ def __init__(self, config: Qwen3MoeConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [Qwen3MoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = Qwen3MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen3MoeRotaryEmbedding(config=config) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @can_return_tuple @add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> MoeModelOutputWithPast: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if use_cache and past_key_values is None: past_key_values = DynamicCache() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_router_logits = () if output_router_logits else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( partial(decoder_layer.__call__, **flash_attn_kwargs), hidden_states, causal_mask, position_ids, past_key_values, output_attentions, output_router_logits, use_cache, cache_position, position_embeddings, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, output_router_logits=output_router_logits, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if output_router_logits: all_router_logits += (layer_outputs[-1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return MoeModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, router_logits=all_router_logits, ) def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and past_key_values is not None: is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0] if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Qwen3Moe. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if ( self.config._attn_implementation == "sdpa" and not (using_static_cache or using_sliding_window_cache) and not output_attentions ): if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, sliding_window=self.config.sliding_window, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] # SlidingWindowCache or StaticCache if using_sliding_window_cache or using_static_cache: target_length = past_key_values.get_max_cache_shape() # DynamicCache or no cache else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], config=self.config, past_key_values=past_key_values, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu"] and not output_attentions ): causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, config: Qwen3MoeConfig, past_key_values: Cache, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to place the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`Qwen3MoeConfig`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) if config.sliding_window is not None: # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also # the check is needed to verify is current checkpoint was trained with sliding window or not if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: sliding_attend_mask = torch.arange(target_length, device=device) <= ( cache_position.reshape(-1, 1) - config.sliding_window ) diagonal_attend_mask.bitwise_or_(sliding_attend_mask) causal_mask *= diagonal_attend_mask causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.shape[-1] > target_length: attention_mask = attention_mask[:, :target_length] mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... def load_balancing_loss_func( gate_logits: Union[torch.Tensor, Tuple[torch.Tensor], None], num_experts: Optional[int] = None, top_k=2, attention_mask: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, int]: r""" Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. Args: gate_logits: Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of shape [batch_size X sequence_length, num_experts]. num_experts: Number of experts top_k: The number of experts to route per-token, can be also interpreted as the `top-k` routing parameter. attention_mask (`torch.Tensor`, *optional*): The attention_mask used in forward function shape [batch_size X sequence_length] if not None. Returns: The auxiliary loss. """ if gate_logits is None or not isinstance(gate_logits, tuple): return 0 if isinstance(gate_logits, tuple): compute_device = gate_logits[0].device concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) if attention_mask is None: # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.mean(expert_mask.float(), dim=0) # Compute the average probability of routing to these experts router_prob_per_expert = torch.mean(routing_weights, dim=0) else: batch_size, sequence_length = attention_mask.shape num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask expert_attention_mask = ( attention_mask[None, :, :, None, None] .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) .reshape(-1, top_k, num_experts) .to(compute_device) ) # Compute the percentage of tokens routed to each experts tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( expert_attention_mask, dim=0 ) # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert router_per_expert_attention_mask = ( attention_mask[None, :, :, None] .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) .reshape(-1, num_experts) .to(compute_device) ) # Compute the average probability of routing to these experts router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( router_per_expert_attention_mask, dim=0 ) overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) return overall_loss * num_experts class Qwen3MoBEForCausalLM(Qwen3MoePreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = Qwen3MoeModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.router_aux_loss_coef = config.router_aux_loss_coef self.num_experts = config.num_experts self.num_experts_per_tok = config.num_experts_per_tok # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model @can_return_tuple @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") @add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_router_logits: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **kwargs: Unpack[KwargsForCausalLM], ) -> MoeCausalLMOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. logits_to_keep (`int` or `torch.Tensor`, *optional*): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length). Returns: Example: ```python >>> from transformers import AutoTokenizer, Qwen3MoeForCausalLM >>> model = Qwen3MoeForCausalLM.from_pretrained("Qwen/Qwen3-MoE-15B-A2B") >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-MoE-15B-A2B") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_router_logits = ( output_router_logits if output_router_logits is not None else self.config.output_router_logits ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs: MoeModelOutputWithPast = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_router_logits=output_router_logits, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) aux_loss = None if output_router_logits: aux_loss = load_balancing_loss_func( outputs.router_logits, self.num_experts, self.num_experts_per_tok, attention_mask, ) if labels is not None: loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device return MoeCausalLMOutputWithPast( loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, router_logits=outputs.router_logits, ) @add_start_docstrings( """ The Qwen3Moe Model transformer with a sequence classification head on top (linear layer). [`Qwen3MoeForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in each row of the batch). """, QWEN3_MOE_START_DOCSTRING, ) class Qwen3MoeForSequenceClassification(Qwen3MoePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = Qwen3MoeModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value @can_return_tuple @add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> SequenceClassifierOutputWithPast: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ transformer_outputs: BaseModelOutputWithPast = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) hidden_states = transformer_outputs.last_hidden_state logits = self.score(hidden_states) if input_ids is not None: batch_size = input_ids.shape[0] else: batch_size = inputs_embeds.shape[0] if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if self.config.pad_token_id is None: last_non_pad_token = -1 elif input_ids is not None: # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32) last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) else: last_non_pad_token = -1 logger.warning_once( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token] loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config) return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The Qwen3Moe Model transformer with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, QWEN3_MOE_START_DOCSTRING, ) class Qwen3MoeForTokenClassification(Qwen3MoePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.model = Qwen3MoeModel(config) if getattr(config, "classifier_dropout", None) is not None: classifier_dropout = config.classifier_dropout elif getattr(config, "hidden_dropout", None) is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 self.dropout = nn.Dropout(classifier_dropout) self.score = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value @can_return_tuple @add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> TokenClassifierOutput: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs: BaseModelOutputWithPast = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = outputs.last_hidden_state sequence_output = self.dropout(sequence_output) logits = self.score(sequence_output) loss = None if labels is not None: loss = self.loss_function(logits, labels, self.config) return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ The Qwen3Moe Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, QWEN3_MOE_START_DOCSTRING, ) class Qwen3MoeForQuestionAnswering(Qwen3MoePreTrainedModel): base_model_prefix = "transformer" def __init__(self, config): super().__init__(config) self.transformer = Qwen3MoeModel(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.transformer.embed_tokens def set_input_embeddings(self, value): self.transformer.embed_tokens = value @can_return_tuple @add_start_docstrings_to_model_forward(QWEN3_MOE_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, **kwargs, ) -> QuestionAnsweringModelOutput: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ outputs: BaseModelOutputWithPast = self.transformer( input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = outputs.last_hidden_state logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() loss = None if start_positions is not None and end_positions is not None: loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs) return QuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "Qwen3MoBEForCausalLM" ] middle25projects/2024103840/src/scripts/MoBE-main/requirements.txt000066400000000000000000000001241506651042300245510ustar00rootroot00000000000000accelerate==1.7.0 safetensors==0.5.3 torch==2.7.0 tqdm==4.67.1 transformers==4.51.3 middle25projects/2024103840/src/scripts/MoBE-main/results.jpg000066400000000000000000042002471506651042300235040ustar00rootroot00000000000000JFIF,,CCe>" }!1AQa"q2#BR$3br %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz w!1AQaq"2B #3Rbr $4%&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz ?((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((((Ia=Ͳ_],m Ekk>$5G> Ekk>$5_5#~%E,~ x yDGzZωQzZωW _L~?o⶙v>Uht2~Y!c,yY6gafx| Zժυ t֩G_^ O߈3LIp晾igXUy(ӥJxS*tҧVzhQZ=C?f?ڟWx^Ӥg_6mLbZ׵OԞb-졹KB6\̬k,| z/tصvދStۂEεN-lmu)nyٺNߚ_?9fM34ȵ=WU⵽-5O$Ma,p__ڬRKh4(׼k]{S? u%jwnne%ݎFRR8cD_g9mƔ#ǜ)zU0|\yRQ ia2R瀣,ǥNO[`BaV<<ڮ'9p2a6:#9V=x gďf=x gďf✞ĿHozcO5O_+^#Y#?O_+^#Y#!WYŞ/|?*oF{GTnl6yh-hmT;<a|E. 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save_file import copy import argparse import gc class MoBE(nn.Module): def __init__(self, initial_A, initial_B, initial_W, activation='silu'): super().__init__() self.A_params = nn.Parameter(initial_A) self.B_params = nn.Parameter(initial_B) self.w_params = nn.Parameter(initial_W) if activation not in ('silu', 'tanh'): raise ValueError("activation must be 'silu' or 'tanh'") self.activation = activation def forward(self, batch_indices): A = self.A_params[batch_indices] w = self.w_params[batch_indices] w = torch.softmax(w, dim=1) weighted_B = torch.einsum('bi,ijk->bjk', w, self.B_params) if self.activation == 'silu': activation = nn.functional.silu(weighted_B) else: activation = torch.tanh(weighted_B) hat_Z = A @ activation return hat_Z def get_layer_proj_dict(index_path, base_dir, layer_i, matrix_type): with open(index_path, 'r') as f: index_data = json.load(f) layer_dict = {} for weight_name, safetensor_file in index_data['weight_map'].items(): if f"model.layers.{layer_i}.mlp.experts" in weight_name and weight_name.endswith(f"{matrix_type}.weight"): safetensor_path = f"{base_dir}/{safetensor_file}" with safe_open(safetensor_path, framework="pt") as f: layer_dict[weight_name] = f.get_tensor(weight_name) return layer_dict def parse_args(): parser = argparse.ArgumentParser(description="MoBE Training") parser.add_argument("--index_path", type=str, required=True) parser.add_argument("--base_dir", type=str, required=True) parser.add_argument("--save_path", type=str, required=True) parser.add_argument("--num_hidden_layers", type=int, default=94) parser.add_argument("--num_matrices", type=int, default=128) parser.add_argument("--rows_per_matrix", type=int, default=1536) parser.add_argument("--cols", type=int, default=4096) parser.add_argument("--num_epochs", type=int, default=30000) parser.add_argument("--batch_size", type=int, default=128) parser.add_argument("--num_batches", type=int, default=1) parser.add_argument("--learning_rate", type=float, default=0.07) parser.add_argument("--num_B", type=int, default=32) parser.add_argument("--truncation", type=int, default=1536) parser.add_argument("--start_layer", type=int, default=0) parser.add_argument("--end_layer", type=int, default=94) parser.add_argument("--matrix_type", type=str, choices=["gate_proj", "up_proj"], default="gate_proj") parser.add_argument("--activation", type=str, choices=["silu", "tanh"], default="silu") return parser.parse_args() def main(): args = parse_args() device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(f"Using device: {device}") os.makedirs(args.save_path, exist_ok=True) num_group = args.num_B k = args.truncation num_matrices_per_group = args.num_matrices // num_group for n in range(args.start_layer, args.end_layer): best_real_loss = float('inf') best_model_state = None print(f'layer: {n}') state_dict = get_layer_proj_dict(args.index_path, args.base_dir, layer_i=n, matrix_type=args.matrix_type) target_list = [] for i in range(args.num_matrices): target_list.append(state_dict[f"model.layers.{n}.mlp.experts.{i}.{args.matrix_type}.weight"].to(torch.float16).to(device)) target = torch.stack(target_list) global_target_std = target.std() initial_A, initial_B, initial_W = None, None, None for group_i in range(num_group): W = target[group_i*num_matrices_per_group:(group_i+1)*num_matrices_per_group].view(-1, args.cols) W_float32 = W.to(torch.float32) U, S, Vt = torch.linalg.svd(W_float32, full_matrices=False) U_k = U[:, :k] S_k = S[:k] Vt_k = Vt[:k, :] group_A = U_k @ torch.diag(S_k) group_B = Vt_k.unsqueeze(0) group_W = torch.zeros(num_matrices_per_group, num_group, device=device) group_W[:, group_i] = 1 if initial_B is None: initial_B = group_B else: initial_B = torch.cat((initial_B, group_B), dim=0) for weight_i in range(num_matrices_per_group): start_r = weight_i * args.rows_per_matrix end_r = (weight_i + 1) * args.rows_per_matrix single_A = group_A[start_r:end_r, :] single_W = group_W[start_r:end_r, :] initial_A = single_A.unsqueeze(0) if initial_A is None else torch.cat((initial_A, single_A.unsqueeze(0)), dim=0) initial_W = single_W if initial_W is None else torch.cat((initial_W, single_W), dim=0) model = MoBE(initial_A, initial_B, initial_W, activation=args.activation).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate) grad_accum = args.num_batches for epoch in range(args.num_epochs): epoch_loss, epoch_real_loss, epoch_real_mae = 0.0, 0.0, 0.0 optimizer.zero_grad() for batch_idx in range(args.num_batches): start_idx = batch_idx * args.batch_size end_idx = min((batch_idx + 1) * args.batch_size, args.num_matrices) indices = torch.arange(start_idx, end_idx, device=device) if len(indices) == 0: continue outputs = model(indices) batch_target = target[indices] Z_scaled = batch_target / global_target_std Z_hat_unscaled = outputs * global_target_std loss = F.mse_loss(outputs, Z_scaled.to(torch.float32)) real_loss = F.mse_loss(Z_hat_unscaled, batch_target) real_mae = F.l1_loss(Z_hat_unscaled, batch_target) (loss / grad_accum).backward() epoch_loss += loss.item() * len(indices) epoch_real_loss += real_loss.item() * len(indices) epoch_real_mae += real_mae.item() * len(indices) optimizer.step() epoch_loss /= args.num_matrices epoch_real_loss /= args.num_matrices epoch_real_mae /= args.num_matrices if (epoch + 1) % 200 == 0 and epoch_real_loss < best_real_loss: best_real_loss = epoch_real_loss best_model_state = copy.deepcopy(model.state_dict()) if (epoch + 1) % 200 == 0: print(f"Epoch {epoch+1}, Scaled Loss: {epoch_loss:.10f}, Real MSE Loss: {epoch_real_loss:.10f}, Real MAE Loss: {epoch_real_mae:.10f}") model.load_state_dict(best_model_state) model.eval() reconstructed_dict = {} with torch.no_grad(): indices = torch.arange(0, args.num_matrices, device=device) outputs = model(indices) Z_hat_unscaled = outputs * global_target_std for weight_i in range(args.num_matrices): key = f'experts_{weight_i}_{args.matrix_type}_weight' reconstructed_dict[key] = Z_hat_unscaled[weight_i, :] output_path = f'{args.save_path}/model_layers_{n}_mlp_{args.matrix_type}_weight.safetensors' save_file(reconstructed_dict, output_path) out_file = f'{args.save_path}/model_layers_{n}_mlp_{args.matrix_type}_WAB.pth' torch.save(model.state_dict(), out_file) print(f"Saved best model weights for layer {n} at {out_file} with loss {best_real_loss:.10f}") model_params = sum(p.numel() for p in model.parameters()) compression_ratio = model_params / (args.num_matrices * args.rows_per_matrix * args.cols) print(f'Layer: {n}, Compression Ratio: {compression_ratio:.5f}') del target, target_list, state_dict, model, optimizer, model_params if 'initial_A' in globals(): del initial_A if 'initial_B' in globals(): del initial_B if 'initial_W' in globals(): del initial_W gc.collect() torch.cuda.empty_cache() if __name__ == "__main__": main()middle25projects/2024103840/src/scripts/MoBE-main/train_group.py000066400000000000000000000235361506651042300242040ustar00rootroot00000000000000import torch import torch.nn as nn import torch.nn.functional as F import json from safetensors import safe_open import os from safetensors.torch import save_file import copy import argparse import gc class MoBE(nn.Module): def __init__(self, initial_A, initial_B, initial_W, activation='silu'): super().__init__() self.A_params = nn.Parameter(initial_A) self.B_params = nn.Parameter(initial_B) self.w_params = nn.Parameter(initial_W) if activation not in ('silu', 'tanh'): raise ValueError("activation must be 'silu' or 'tanh'") self.activation = activation def forward(self, batch_indices): A = self.A_params[batch_indices] w = self.w_params[batch_indices] w = torch.softmax(w, dim=1) weighted_B = torch.einsum('bi,ijk->bjk', w, self.B_params) if self.activation == 'silu': activation = torch.nn.functional.silu(weighted_B) else: activation = torch.tanh(weighted_B) hat_Z = A @ activation return hat_Z def get_layer_proj_dict(index_path, base_dir, layer_i, matrix_type): with open(index_path, 'r') as f: index_data = json.load(f) layer_dict = {} for weight_name, safetensor_file in index_data['weight_map'].items(): if f"model.layers.{layer_i}.mlp.experts" in weight_name and weight_name.endswith(f"{matrix_type}.weight"): safetensor_path = f"{base_dir}/{safetensor_file}" with safe_open(safetensor_path, framework="pt") as f: layer_dict[weight_name] = f.get_tensor(weight_name) return layer_dict def parse_args(): parser = argparse.ArgumentParser(description="MoBE Training (group-wise)") parser.add_argument("--index_path", type=str, required=True) parser.add_argument("--base_dir", type=str, required=True) parser.add_argument("--save_path", type=str, required=True) parser.add_argument("--num_hidden_layers", type=int, default=61) parser.add_argument("--num_matrices", type=int, default=384) parser.add_argument("--rows_per_matrix", type=int, default=2048) parser.add_argument("--cols", type=int, default=7168) parser.add_argument("--num_epochs", type=int, default=15000) parser.add_argument("--batch_size", type=int, default=32) parser.add_argument("--num_batches", type=int, default=12) parser.add_argument("--learning_rate", type=float, default=0.07) parser.add_argument("--num_B", type=int, default=128) parser.add_argument("--truncation", type=int, default=2048) parser.add_argument("--start_layer", type=int, default=1) parser.add_argument("--end_layer", type=int, default=61) parser.add_argument("--matrix_type", type=str, choices=["gate_proj", "up_proj"], default="gate_proj") parser.add_argument("--num_groups", type=int, default=2) parser.add_argument("--activation", type=str, choices=["silu", "tanh"], default="silu") return parser.parse_args() def main(): args = parse_args() device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(f"Using device: {device}") os.makedirs(args.save_path, exist_ok=True) assert args.num_matrices % args.num_groups == 0, "num_matrices must be divisible by num_groups" assert args.num_B % args.num_groups == 0, "num_B must be divisible by num_groups" assert args.num_batches % args.num_groups == 0, "num_batches must be divisible by num_groups" num_matrices_group = args.num_matrices // args.num_groups num_B_group = args.num_B // args.num_groups num_batches_group = max(1, args.num_batches // args.num_groups) k = args.truncation for n in range(args.start_layer, args.end_layer): print(f'========== layer: {n} ==========') state_dict = get_layer_proj_dict(args.index_path, args.base_dir, layer_i=n, matrix_type=args.matrix_type) full_target = torch.stack([ state_dict[f"model.layers.{n}.mlp.experts.{i}.{args.matrix_type}.weight"] .to(torch.float16).to(device) for i in range(args.num_matrices) ]) global_target_std = full_target.std() for g in range(args.num_groups): if g == 0: ####OOM continue print(f'---- group {g+1}/{args.num_groups} ----') g_start = g * num_matrices_group g_end = (g + 1) * num_matrices_group target = full_target[g_start:g_end] initial_A, initial_B, initial_W = None, None, None for group_i in range(num_B_group): W = target[group_i * num_matrices_group // num_B_group: (group_i + 1) * num_matrices_group // num_B_group].view(-1, args.cols) W_float32 = W.to(torch.float32) U, S, Vt = torch.linalg.svd(W_float32, full_matrices=False) U_k = U[:, :k] S_k = S[:k] Vt_k = Vt[:k, :] group_A = U_k @ torch.diag(S_k) group_B = Vt_k.unsqueeze(0) group_W = torch.zeros(num_matrices_group // num_B_group, num_B_group, device=device) group_W[:, group_i] = 1 if initial_B is None: initial_B = group_B else: initial_B = torch.cat((initial_B, group_B), dim=0) for weight_i in range(num_matrices_group // num_B_group): start_r = weight_i * args.rows_per_matrix end_r = (weight_i + 1) * args.rows_per_matrix single_A = group_A[start_r:end_r, :] single_W = group_W[start_r:end_r, :] initial_A = single_A.unsqueeze(0) if initial_A is None \ else torch.cat((initial_A, single_A.unsqueeze(0)), dim=0) initial_W = single_W if initial_W is None \ else torch.cat((initial_W, single_W), dim=0) model = MoBE(initial_A, initial_B, initial_W, activation=args.activation).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate) best_real_loss = float('inf') best_model_state = None for epoch in range(args.num_epochs): epoch_loss, epoch_real_loss, epoch_real_mae = 0.0, 0.0, 0.0 optimizer.zero_grad() for batch_idx in range(num_batches_group): start_idx = batch_idx * args.batch_size end_idx = min((batch_idx + 1) * args.batch_size, num_matrices_group) indices = torch.arange(start_idx, end_idx, device=device) if len(indices) == 0: continue outputs = model(indices) batch_target = target[indices] Z_scaled = batch_target / global_target_std Z_hat_unscaled = outputs * global_target_std loss = F.mse_loss(outputs, Z_scaled.to(torch.float32)) real_loss = F.mse_loss(Z_hat_unscaled, batch_target) real_mae = F.l1_loss(Z_hat_unscaled, batch_target) (loss / num_batches_group).backward() epoch_loss += loss.item() * len(indices) epoch_real_loss += real_loss.item() * len(indices) epoch_real_mae += real_mae.item() * len(indices) optimizer.step() epoch_loss /= num_matrices_group epoch_real_loss /= num_matrices_group epoch_real_mae /= num_matrices_group if (epoch + 1) % 200 == 0 and epoch_real_loss < best_real_loss: best_real_loss = epoch_real_loss best_model_state = copy.deepcopy(model.state_dict()) if (epoch + 1) % 200 == 0: print(f"Epoch {epoch+1}, Scaled Loss: {epoch_loss:.10f}, " f"Real MSE Loss: {epoch_real_loss:.10f}, " f"Real MAE Loss: {epoch_real_mae:.10f}") del optimizer, initial_A, initial_B, initial_W gc.collect() torch.cuda.empty_cache() model.load_state_dict(best_model_state) model.eval() reconstructed_dict = {} batch_size_infer = 8 with torch.no_grad(): for start_idx in range(0, num_matrices_group, batch_size_infer): end_idx = min(start_idx + batch_size_infer, num_matrices_group) indices = torch.arange(start_idx, end_idx, device=device) outputs = model(indices) Z_hat_unscaled = outputs * global_target_std for i in range(Z_hat_unscaled.size(0)): weight_i = start_idx + i key = f'experts_{g_start + weight_i}_{args.matrix_type}_weight' reconstructed_dict[key] = Z_hat_unscaled[i].cpu() output_path = f'{args.save_path}/model_layers_{n}_mlp_{args.matrix_type}_group{g}.safetensors' save_file(reconstructed_dict, output_path) out_file = f'{args.save_path}/model_layers_{n}_mlp_{args.matrix_type}_group{g}_WAB.pth' torch.save(model.state_dict(), out_file) print(f"Saved best model weights for layer {n} group {g} " f"at {out_file} with loss {best_real_loss:.10f}") model_params = sum(p.numel() for p in model.parameters()) compression_ratio = model_params / (num_matrices_group * args.rows_per_matrix * args.cols) print(f'Layer: {n}, Group: {g}, Compression Ratio: {compression_ratio:.5f}') del model gc.collect() torch.cuda.empty_cache() del full_target, state_dict gc.collect() torch.cuda.empty_cache() if __name__ == "__main__": main()middle25projects/README.md000066400000000000000000000006731506651042300155240ustar00rootroot00000000000000# middle25projects reports collected for Middleware class project @2025 ## Report Naming Convension ``` 2023201001  ┃  ┃  ┗ experiment report (2023201001.md / 2023201001.tex / ...) ┃ ┗ src ┃ ┗ scripts(experiment scripts in Matlab / Python / ...) ┗ dataset(if over 100MB, use netdisk url) ┗ paper(OPTIONAL, share paper with info.ruc@163.com on overleaf) ``` [How to submit](./Submission-Howto.md)middle25projects/Submission-Howto.md000066400000000000000000000013241506651042300200120ustar00rootroot00000000000000# Submission Steps - create account on openi & login into openi.pcl.ac.cn - fork a copy of info-ruc/middle25projects - download and install git tool on your local computer - git config --global user.name "$yourname$" & git config --global user.email "$yourname@yourserver.com$" - git clone https://openi.pcl.ac.cn/$yourname$/middle25projects.git - make folders structures following Report Naming Convention in README.md - enter folders and edit reports (student_no.md / student_no.tex) and project files (datasets & scripts) - git add --all - git commit -m "description of edit" - git push (enter user name & password if needed) - create a pull request on openi to merge your commit with info-ruc/middle25projects