import{S as ac,i as rc,s as sc,e as r,k as i,w as y,t as l,M as nc,c as s,d as t,m as c,a as n,x as $,h as o,b as u,G as e,g as b,y as E,q as j,o as T,B as M,v as lc,L as Ba}from"../../chunks/vendor-hf-doc-builder.js";import{D as P}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Wa}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as vt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Ha}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function oc(C){let g,k,w,f,D;return f=new Wa({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsKQ==",highlighted:"debug_overflow = DebugUnderflowOverflow(model)"}}),{c(){g=r("p"),k=l("To activate the underflow/overflow detection, initialize the object with the model :"),w=i(),y(f.$$.fragment)},l(p){g=s(p,"P",{});var _=n(g);k=o(_,"To activate the underflow/overflow detection, initialize the object with the model :"),_.forEach(t),w=c(p),$(f.$$.fragment,p)},m(p,_){b(p,g,_),e(g,k),b(p,w,_),E(f,p,_),D=!0},p:Ba,i(p){D||(j(f.$$.fragment,p),D=!0)},o(p){T(f.$$.fragment,p),D=!1},d(p){p&&t(g),p&&t(w),M(f,p)}}}function ic(C){let g,k,w,f,D;return f=new Wa({props:{code:"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",highlighted:`<span class="hljs-attribute">Detected</span> inf/nan during batch_number=<span class="hljs-number">0</span>
<span class="hljs-attribute">Last</span> <span class="hljs-number">21</span> forward frames:
<span class="hljs-attribute">abs</span> min  abs max  metadata<span class="hljs-meta">
[...]</span>
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wi_0 Linear
<span class="hljs-attribute">2</span>.<span class="hljs-number">17</span>e-<span class="hljs-number">07</span> <span class="hljs-number">4</span>.<span class="hljs-number">50</span>e+<span class="hljs-number">00</span> weight
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">2</span>.<span class="hljs-number">68</span>e-<span class="hljs-number">06</span> <span class="hljs-number">3</span>.<span class="hljs-number">70</span>e+<span class="hljs-number">01</span> output
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wi_1 Linear
<span class="hljs-attribute">8</span>.<span class="hljs-number">08</span>e-<span class="hljs-number">07</span> <span class="hljs-number">2</span>.<span class="hljs-number">66</span>e+<span class="hljs-number">01</span> weight
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">1</span>.<span class="hljs-number">27</span>e-<span class="hljs-number">04</span> <span class="hljs-number">2</span>.<span class="hljs-number">37</span>e+<span class="hljs-number">02</span> output
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wo Linear
<span class="hljs-attribute">1</span>.<span class="hljs-number">01</span>e-<span class="hljs-number">06</span> <span class="hljs-number">6</span>.<span class="hljs-number">44</span>e+<span class="hljs-number">00</span> weight
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> <span class="hljs-number">9</span>.<span class="hljs-number">74</span>e+<span class="hljs-number">03</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> output
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense T5DenseGatedGeluDense
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> output
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.dropout Dropout
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span>      inf output`}}),{c(){g=r("p"),k=l("mixed precision :"),w=i(),y(f.$$.fragment)},l(p){g=s(p,"P",{});var _=n(g);k=o(_,"mixed precision :"),_.forEach(t),w=c(p),$(f.$$.fragment,p)},m(p,_){b(p,g,_),e(g,k),b(p,w,_),E(f,p,_),D=!0},p:Ba,i(p){D||(j(f.$$.fragment,p),D=!0)},o(p){T(f.$$.fragment,p),D=!1},d(p){p&&t(g),p&&t(w),M(f,p)}}}function cc(C){let g,k,w,f,D;return f=new Wa({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsJTJDJTIwbWF4X2ZyYW1lc190b19zYXZlJTNEMTAwKQ==",highlighted:'debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=<span class="hljs-number">100</span>)'}}),{c(){g=r("p"),k=l("By default the last 21 frames are printed. You can change the default to adjust for your needs. For example :"),w=i(),y(f.$$.fragment)},l(p){g=s(p,"P",{});var _=n(g);k=o(_,"By default the last 21 frames are printed. You can change the default to adjust for your needs. For example :"),_.forEach(t),w=c(p),$(f.$$.fragment,p)},m(p,_){b(p,g,_),e(g,k),b(p,w,_),E(f,p,_),D=!0},p:Ba,i(p){D||(j(f.$$.fragment,p),D=!0)},o(p){T(f.$$.fragment,p),D=!1},d(p){p&&t(g),p&&t(w),M(f,p)}}}function pc(C){let g,k,w,f,D;return f=new Wa({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsJTJDJTIwdHJhY2VfYmF0Y2hfbnVtcyUzRCU1QjElMkMlMjAzJTVEKQ==",highlighted:'debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>])'}}),{c(){g=r("p"),k=l("given batch, and only do that for batches 1 and 3. Then you instantiate this class as :"),w=i(),y(f.$$.fragment)},l(p){g=s(p,"P",{});var _=n(g);k=o(_,"given batch, and only do that for batches 1 and 3. Then you instantiate this class as :"),_.forEach(t),w=c(p),$(f.$$.fragment,p)},m(p,_){b(p,g,_),e(g,k),b(p,w,_),E(f,p,_),D=!0},p:Ba,i(p){D||(j(f.$$.fragment,p),D=!0)},o(p){T(f.$$.fragment,p),D=!1},d(p){p&&t(g),p&&t(w),M(f,p)}}}function dc(C){let g,k,w,f,D;return f=new Wa({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsJTJDJTIwdHJhY2VfYmF0Y2hfbnVtcyUzRCU1QjElMkMlMjAzJTVEJTJDJTIwYWJvcnRfYWZ0ZXJfYmF0Y2hfbnVtJTNEMyk=",highlighted:'debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>], abort_after_batch_num=<span class="hljs-number">3</span>)'}}),{c(){g=r("p"),k=l("You can also specify the batch number after which to stop the training, with :"),w=i(),y(f.$$.fragment)},l(p){g=s(p,"P",{});var _=n(g);k=o(_,"You can also specify the batch number after which to stop the training, with :"),_.forEach(t),w=c(p),$(f.$$.fragment,p)},m(p,_){b(p,g,_),e(g,k),b(p,w,_),E(f,p,_),D=!0},p:Ba,i(p){D||(j(f.$$.fragment,p),D=!0)},o(p){T(f.$$.fragment,p),D=!1},d(p){p&&t(g),p&&t(w),M(f,p)}}}function hc(C){let g,k,w,f,D,p,_,wt,Gr,Fa,ee,Or,tt,Nr,zr,Qa,at,Sr,Ra,G,te,yt,be,Hr,$t,Br,Ya,O,_e,Wr,Et,Fr,Xa,N,ve,Qr,jt,Rr,Ka,J,we,Yr,Tt,Xr,Kr,ye,rt,$e,qr,es,ts,st,Ee,as,rs,qa,z,je,ss,I,ns,Mt,ls,os,Dt,is,cs,xt,ps,ds,kt,hs,fs,er,S,Te,ms,At,us,tr,H,ae,Pt,Me,gs,Ut,bs,ar,B,De,_s,It,vs,rr,W,re,Ct,xe,ws,Jt,ys,sr,v,ke,$s,Zt,Es,js,Lt,Ts,Ms,Vt,Gt,Ds,xs,Ot,ks,As,F,nt,Ps,Nt,Us,Is,lt,Cs,zt,Js,Zs,ot,Ls,St,Vs,Gs,Ht,Os,Ns,Q,it,zs,Bt,Ss,Hs,ct,Bs,Wt,Ws,Fs,pt,Qs,Ft,Rs,Ys,Qt,Xs,Ks,Rt,Yt,qs,en,Xt,tn,an,Kt,qt,rn,sn,ea,nn,ln,se,Ae,on,Pe,cn,ta,pn,dn,hn,ne,Ue,fn,aa,mn,nr,R,le,ra,Ie,un,sa,gn,lr,A,Ce,bn,Je,_n,na,vn,wn,yn,Ze,$n,la,En,jn,Tn,L,Le,Mn,oa,Dn,xn,Ve,kn,ia,An,Pn,Un,oe,Ge,In,Oe,Cn,ca,Jn,Zn,Ln,ie,Ne,Vn,ze,Gn,pa,On,Nn,zn,ce,Se,Sn,He,Hn,da,Bn,Wn,or,Y,pe,ha,Be,Fn,fa,Qn,ir,d,We,Rn,X,Yn,ma,Xn,Kn,ua,qn,el,tl,ga,al,rl,Fe,ba,sl,nl,_a,ll,ol,va,il,cl,de,pl,Z,dl,wa,hl,fl,ya,ml,ul,$a,gl,bl,_l,Qe,Re,vl,Ea,wl,yl,$l,ja,El,jl,Ye,Tl,Ta,Ml,Dl,xl,he,kl,K,Al,Ma,Pl,Ul,Da,Il,Cl,Jl,xa,Zl,Ll,Xe,Vl,ka,Gl,Ol,Nl,fe,zl,Aa,Sl,Hl,Pa,Bl,Wl,Ua,Fl,Ql,Ke,Rl,Ia,Yl,Xl,Kl,me,ql,Ca,eo,to,Ja,ao,ro,Za,so,no,ue,lo,La,oo,io,dt,Va,co,po,ho,q,fo,Ga,mo,uo,Oa,go,bo,cr;return p=new vt({}),be=new vt({}),_e=new P({props:{name:"class transformers.EvalPrediction",anchor:"transformers.EvalPrediction",parameters:[{name:"predictions",val:": typing.Union[numpy.ndarray, typing.Tuple[numpy.ndarray]]"},{name:"label_ids",val:": typing.Union[numpy.ndarray, typing.Tuple[numpy.ndarray]]"},{name:"inputs",val:": typing.Union[numpy.ndarray, typing.Tuple[numpy.ndarray], NoneType] = None"}],parametersDescription:[{anchor:"transformers.EvalPrediction.predictions",description:"<strong>predictions</strong> (<code>np.ndarray</code>) &#x2014; 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<li>the dataclass instances in the same order as they were passed to the initializer.abspath</li>
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<li>The potential list of remaining argument strings. (same as argparse.ArgumentParser.parse_known_args)</li>
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<li>the dataclass instances in the same order as they were passed to the initializer.</li>
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namespace. Optional: To create sub argument groups use the `),la=s(jr,"CODE",{});var wi=n(la);En=o(wi,"_argument_group_name"),wi.forEach(t),jn=o(jr," attribute in the dataclass."),jr.forEach(t),Tn=c(U),L=s(U,"DIV",{class:!0});var ut=n(L);$(Le.$$.fragment,ut),Mn=c(ut),oa=s(ut,"P",{});var yi=n(oa);Dn=o(yi,"Parse command-line args into instances of the specified dataclass types."),yi.forEach(t),xn=c(ut),Ve=s(ut,"P",{});var Tr=n(Ve);kn=o(Tr,"This relies on argparse\u2019s "),ia=s(Tr,"CODE",{});var $i=n(ia);An=o($i,"ArgumentParser.parse_known_args"),$i.forEach(t),Pn=o(Tr,`. See the doc at:
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importantly `),ma=s(gt,"CODE",{});var ki=n(ma);Xn=o(ki,"nan"),ki.forEach(t),Kn=o(gt," or "),ua=s(gt,"CODE",{});var Ai=n(ua);qn=o(Ai,"inf"),Ai.forEach(t),el=o(gt," weight and activation elements."),gt.forEach(t),tl=c(h),ga=s(h,"P",{});var Pi=n(ga);al=o(Pi,"There are 2 working modes:"),Pi.forEach(t),rl=c(h),Fe=s(h,"OL",{});var Ir=n(Fe);ba=s(Ir,"LI",{});var Ui=n(ba);sl=o(Ui,"Underflow/overflow detection (default)"),Ui.forEach(t),nl=c(Ir),_a=s(Ir,"LI",{});var Ii=n(_a);ll=o(Ii,"Specific batch absolute min/max tracing without detection"),Ii.forEach(t),Ir.forEach(t),ol=c(h),va=s(h,"P",{});var Ci=n(va);il=o(Ci,"Mode 1: Underflow/overflow detection"),Ci.forEach(t),cl=c(h),$(de.$$.fragment,h),pl=c(h),Z=s(h,"P",{});var ge=n(Z);dl=o(ge,"then run the training as normal and if "),wa=s(ge,"CODE",{});var Ji=n(wa);hl=o(Ji,"nan"),Ji.forEach(t),fl=o(ge," or "),ya=s(ge,"CODE",{});var Zi=n(ya);ml=o(Zi,"inf"),Zi.forEach(t),ul=o(ge,` gets detected in at least one of the weight, input or output
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fp16 numbers.`),Si.forEach(t),Ll=c(h),Xe=s(h,"P",{});var Lr=n(Xe);Vl=o(Lr,"The tracking is done in a forward hook, which gets invoked immediately after "),ka=s(Lr,"CODE",{});var Hi=n(ka);Gl=o(Hi,"forward"),Hi.forEach(t),Ol=o(Lr," has completed."),Lr.forEach(t),Nl=c(h),$(fe.$$.fragment,h),zl=c(h),Aa=s(h,"P",{});var Bi=n(Aa);Sl=o(Bi,`To validate that you have set up this debugging feature correctly, and you intend to use it in a training that
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