import{S as Ya,i as Ja,s as Ka,e as r,k as m,w as d,t as l,M as Wa,c as a,d as t,m as f,a as n,x as c,h as i,b as u,G as s,g as p,y as v,q as g,o as _,B as w,v as Qa}from"../chunks/vendor-hf-doc-builder.js";import{T as Ra}from"../chunks/Tip-hf-doc-builder.js";import{Y as Va}from"../chunks/Youtube-hf-doc-builder.js";import{I as xe}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as T}from"../chunks/CodeBlock-hf-doc-builder.js";function Xa(Be){let h,A,$,k,j;return{c(){h=r("p"),A=l("Refer to the Performance "),$=r("a"),k=l("guide"),j=l(" for more details about memory-saving techniques."),this.h()},l(b){h=a(b,"P",{});var y=n(h);A=i(y,"Refer to the Performance "),$=a(y,"A",{href:!0});var C=n($);k=i(C,"guide"),C.forEach(t),j=i(y," for more details about memory-saving techniques."),y.forEach(t),this.h()},h(){u($,"href","performance")},m(b,y){p(b,h,y),s(h,A),s(h,$),s($,k),s(h,j)},d(b){b&&t(h)}}}function Za(Be){let h,A,$,k,j;return{c(){h=r("p"),A=l("By default, the tokenizer creates an "),$=r("code"),k=l("attention_mask"),j=l(" for you based on your specific tokenizer\u2019s defaults.")},l(b){h=a(b,"P",{});var y=n(h);A=i(y,"By default, the tokenizer creates an "),$=a(y,"CODE",{});var C=n($);k=i(C,"attention_mask"),C.forEach(t),j=i(y," for you based on your specific tokenizer\u2019s defaults."),y.forEach(t)},m(b,y){p(b,h,y),s(h,A),s(h,$),s($,k),s(h,j)},d(b){b&&t(h)}}}function en(Be){let h,A,$,k,j,b,y,C,Ks,Lt,Le,Ws,Nt,ne,Gt,Ne,I,Qs,le,Xs,Zs,ie,eo,to,pe,so,oo,Ht,me,Ot,F,pt,fe,ro,ue,ao,no,lo,mt,he,io,Ge,po,mo,zt,H,fo,de,uo,ho,Rt,U,O,ft,ce,co,ut,vo,Vt,He,go,Yt,ve,Jt,z,_o,Oe,wo,$o,Kt,q,R,ht,ge,bo,dt,yo,Wt,ze,ko,Qt,_e,Xt,Re,Eo,Zt,V,D,jo,Ve,ct,To,Ao,Ye,Po,Co,Io,M,So,Je,vt,Fo,Uo,Ke,qo,Do,es,Y,ts,x,J,gt,we,Mo,_t,xo,ss,K,Bo,$e,Lo,No,os,We,S,Go,wt,Ho,Oo,be,$t,zo,Ro,Qe,Vo,Yo,rs,ye,as,Xe,B,Jo,bt,Ko,Wo,Ze,Qo,Xo,ns,ke,ls,L,W,yt,Ee,Zo,kt,er,is,Q,tr,Et,sr,or,ps,je,ms,et,rr,fs,Te,us,N,X,jt,Ae,ar,Tt,nr,hs,tt,lr,ds,Pe,cs,st,ir,vs,Ce,gs,ot,pr,_s,Ie,ws,G,Z,At,Se,mr,Pt,fr,$s,E,ur,Ct,hr,dr,It,cr,vr,St,gr,_r,Ft,wr,$r,Ut,br,yr,bs,Fe,ys,rt,kr,ks,Ue,Es,at,Er,js,qe,Ts,ee,jr,qt,Tr,Ar,As,te,Ps,De,Cs,se,Pr,Dt,Cr,Ir,Is,oe,Mt,Sr,Fr,xt,Ur,Ss;return b=new xe({}),ne=new Va({props:{id:"S2EEG3JIt2A"}}),me=new Va({props:{id:"_PAli-V4wj0"}}),ce=new xe({}),ve=new T({props:{code:`ValueError: Connection error, and we cannot find the requested files in the cached path.
Please try again or make sure your Internet connection is on.`,highlighted:`ValueError: Connection error, <span class="hljs-built_in">and</span> we cannot <span class="hljs-keyword">find</span> the requested <span class="hljs-keyword">files</span> in the cached path.
Please <span class="hljs-keyword">try</span> again <span class="hljs-built_in">or</span> <span class="hljs-keyword">make</span> sure your Internet connection <span class="hljs-keyword">is</span> <span class="hljs-keyword">on</span>.`}}),ge=new xe({}),_e=new T({props:{code:"CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch)",highlighted:'<span class="hljs-attribute">CUDA</span> out of memory. Tried to allocate <span class="hljs-number">256</span>.<span class="hljs-number">00</span> MiB (GPU <span class="hljs-number">0</span>; <span class="hljs-number">11</span>.<span class="hljs-number">17</span> GiB total capacity; <span class="hljs-number">9</span>.<span class="hljs-number">70</span> GiB already allocated; <span class="hljs-number">179</span>.<span class="hljs-number">81</span> MiB free; <span class="hljs-number">9</span>.<span class="hljs-number">85</span> GiB reserved in total by PyTorch)'}}),Y=new Ra({props:{$$slots:{default:[Xa]},$$scope:{ctx:Be}}}),we=new xe({}),ye=new T({props:{code:`from transformers import TFPreTrainedModel
from tensorflow import keras

model.save_weights("some_folder/tf_model.h5")
model = TFPreTrainedModel.from_pretrained("some_folder")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFPreTrainedModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> tensorflow <span class="hljs-keyword">import</span> keras

<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_weights(<span class="hljs-string">&quot;some_folder/tf_model.h5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFPreTrainedModel.from_pretrained(<span class="hljs-string">&quot;some_folder&quot;</span>)`}}),ke=new T({props:{code:`from transformers import TFPreTrainedModel

model.save_pretrained("path_to/model")
model = TFPreTrainedModel.from_pretrained("path_to/model")`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFPreTrainedModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;path_to/model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFPreTrainedModel.from_pretrained(<span class="hljs-string">&quot;path_to/model&quot;</span>)`}}),Ee=new xe({}),je=new T({props:{code:"ImportError: cannot import name 'ImageGPTImageProcessor' from 'transformers' (unknown location)",highlighted:'ImportError: cannot <span class="hljs-keyword">import</span> <span class="hljs-type">name</span> <span class="hljs-string">&#x27;ImageGPTImageProcessor&#x27;</span> <span class="hljs-keyword">from</span> <span class="hljs-string">&#x27;transformers&#x27;</span> (<span class="hljs-type">unknown</span> <span class="hljs-keyword">location</span>)'}}),Te=new T({props:{code:"pip install transformers --upgrade",highlighted:"pip install transformers --upgrade"}}),Ae=new xe({}),Pe=new T({props:{code:"RuntimeError: CUDA error: device-side assert triggered",highlighted:'RuntimeError: CUDA <span class="hljs-literal">error</span>: device-<span class="hljs-literal">side</span> <span class="hljs-keyword">assert</span> triggered'}}),Ce=new T({props:{code:`import os

os.environ["CUDA_VISIBLE_DEVICES"] = ""`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os

<span class="hljs-meta">&gt;&gt;&gt; </span>os.environ[<span class="hljs-string">&quot;CUDA_VISIBLE_DEVICES&quot;</span>] = <span class="hljs-string">&quot;&quot;</span>`}}),Ie=new T({props:{code:`import os

os.environ["CUDA_LAUNCH_BLOCKING"] = "1"`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os

<span class="hljs-meta">&gt;&gt;&gt; </span>os.environ[<span class="hljs-string">&quot;CUDA_LAUNCH_BLOCKING&quot;</span>] = <span class="hljs-string">&quot;1&quot;</span>`}}),Se=new xe({}),Fe=new T({props:{code:`from transformers import AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
model.config.pad_token_id`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.pad_token_id
<span class="hljs-number">0</span>`}}),Ue=new T({props:{code:`input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]])
output = model(input_ids)
print(output.logits)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([[<span class="hljs-number">7592</span>, <span class="hljs-number">2057</span>, <span class="hljs-number">2097</span>, <span class="hljs-number">2393</span>, <span class="hljs-number">9611</span>, <span class="hljs-number">2115</span>], [<span class="hljs-number">7592</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output.logits)
tensor([[ <span class="hljs-number">0.0082</span>, -<span class="hljs-number">0.2307</span>],
        [ <span class="hljs-number">0.1317</span>, -<span class="hljs-number">0.1683</span>]], grad_fn=&lt;AddmmBackward0&gt;)`}}),qe=new T({props:{code:`input_ids = torch.tensor([[7592]])
output = model(input_ids)
print(output.logits)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([[<span class="hljs-number">7592</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(input_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output.logits)
tensor([[-<span class="hljs-number">0.1008</span>, -<span class="hljs-number">0.4061</span>]], grad_fn=&lt;AddmmBackward0&gt;)`}}),te=new Ra({props:{$$slots:{default:[Za]},$$scope:{ctx:Be}}}),De=new T({props:{code:`attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]])
output = model(input_ids, attention_mask=attention_mask)
print(output.logits)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>attention_mask = torch.tensor([[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>], [<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(input_ids, attention_mask=attention_mask)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(output.logits)
tensor([[ <span class="hljs-number">0.0082</span>, -<span class="hljs-number">0.2307</span>],
        [-<span class="hljs-number">0.1008</span>, -<span class="hljs-number">0.4061</span>]], grad_fn=&lt;AddmmBackward0&gt;)`}}),{c(){h=r("meta"),A=m(),$=r("h1"),k=r("a"),j=r("span"),d(b.$$.fragment),y=m(),C=r("span"),Ks=l("Troubleshoot"),Lt=m(),Le=r("p"),Ws=l("Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we\u2019ve seen and how you can resolve them. However, this guide isn\u2019t meant to be a comprehensive collection of every \u{1F917} Transformers issue. For more help with troubleshooting your issue, try:"),Nt=m(),d(ne.$$.fragment),Gt=m(),Ne=r("ol"),I=r("li"),Qs=l("Asking for help on the "),le=r("a"),Xs=l("forums"),Zs=l(". There are specific categories you can post your question to, like "),ie=r("a"),eo=l("Beginners"),to=l(" or "),pe=r("a"),so=l("\u{1F917} Transformers"),oo=l(". Make sure you write a good descriptive forum post with some reproducible code to maximize the likelihood that your problem is solved!"),Ht=m(),d(me.$$.fragment),Ot=m(),F=r("ol"),pt=r("li"),fe=r("p"),ro=l("Create an "),ue=r("a"),ao=l("Issue"),no=l(" on the \u{1F917} Transformers repository if it is a bug related to the library. Try to include as much information describing the bug as possible to help us better figure out what\u2019s wrong and how we can fix it."),lo=m(),mt=r("li"),he=r("p"),io=l("Check the "),Ge=r("a"),po=l("Migration"),mo=l(" guide if you use an older version of \u{1F917} Transformers since some important changes have been introduced between versions."),zt=m(),H=r("p"),fo=l("For more details about troubleshooting and getting help, take a look at "),de=r("a"),uo=l("Chapter 8"),ho=l(" of the Hugging Face course."),Rt=m(),U=r("h2"),O=r("a"),ft=r("span"),d(ce.$$.fragment),co=m(),ut=r("span"),vo=l("Firewalled environments"),Vt=m(),He=r("p"),go=l("Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. When your script attempts to download model weights or datasets, the download will hang and then timeout with the following message:"),Yt=m(),d(ve.$$.fragment),Jt=m(),z=r("p"),_o=l("In this case, you should try to run \u{1F917} Transformers on "),Oe=r("a"),wo=l("offline mode"),$o=l(" to avoid the connection error."),Kt=m(),q=r("h2"),R=r("a"),ht=r("span"),d(ge.$$.fragment),bo=m(),dt=r("span"),yo=l("CUDA out of memory"),Wt=m(),ze=r("p"),ko=l("Training large models with millions of parameters can be challenging without the appropriate hardware. A common error you may encounter when the GPU runs out of memory is:"),Qt=m(),d(_e.$$.fragment),Xt=m(),Re=r("p"),Eo=l("Here are some potential solutions you can try to lessen memory use:"),Zt=m(),V=r("ul"),D=r("li"),jo=l("Reduce the "),Ve=r("a"),ct=r("code"),To=l("per_device_train_batch_size"),Ao=l(" value in "),Ye=r("a"),Po=l("TrainingArguments"),Co=l("."),Io=m(),M=r("li"),So=l("Try using "),Je=r("a"),vt=r("code"),Fo=l("gradient_accumulation_steps"),Uo=l(" in "),Ke=r("a"),qo=l("TrainingArguments"),Do=l(" to effectively increase overall batch size."),es=m(),d(Y.$$.fragment),ts=m(),x=r("h2"),J=r("a"),gt=r("span"),d(we.$$.fragment),Mo=m(),_t=r("span"),xo=l("Unable to load a saved TensorFlow model"),ss=m(),K=r("p"),Bo=l("TensorFlow\u2019s "),$e=r("a"),Lo=l("model.save"),No=l(" method will save the entire model - architecture, weights, training configuration - in a single file. However, when you load the model file again, you may run into an error because \u{1F917} Transformers may not load all the TensorFlow-related objects in the model file. To avoid issues with saving and loading TensorFlow models, we recommend you:"),os=m(),We=r("ul"),S=r("li"),Go=l("Save the model weights as a "),wt=r("code"),Ho=l("h5"),Oo=l(" file extension with "),be=r("a"),$t=r("code"),zo=l("model.save_weights"),Ro=l(" and then reload the model with "),Qe=r("a"),Vo=l("from_pretrained()"),Yo=l(":"),rs=m(),d(ye.$$.fragment),as=m(),Xe=r("ul"),B=r("li"),Jo=l("Save the model with "),bt=r("code"),Ko=l("~TFPretrainedModel.save_pretrained"),Wo=l(" and load it again with "),Ze=r("a"),Qo=l("from_pretrained()"),Xo=l(":"),ns=m(),d(ke.$$.fragment),ls=m(),L=r("h2"),W=r("a"),yt=r("span"),d(Ee.$$.fragment),Zo=m(),kt=r("span"),er=l("ImportError"),is=m(),Q=r("p"),tr=l("Another common error you may encounter, especially if it is a newly released model, is "),Et=r("code"),sr=l("ImportError"),or=l(":"),ps=m(),d(je.$$.fragment),ms=m(),et=r("p"),rr=l("For these error types, check to make sure you have the latest version of \u{1F917} Transformers installed to access the most recent models:"),fs=m(),d(Te.$$.fragment),us=m(),N=r("h2"),X=r("a"),jt=r("span"),d(Ae.$$.fragment),ar=m(),Tt=r("span"),nr=l("CUDA error: device-side assert triggered"),hs=m(),tt=r("p"),lr=l("Sometimes you may run into a generic CUDA error about an error in the device code."),ds=m(),d(Pe.$$.fragment),cs=m(),st=r("p"),ir=l("You should try to run the code on a CPU first to get a more descriptive error message. Add the following environment variable to the beginning of your code to switch to a CPU:"),vs=m(),d(Ce.$$.fragment),gs=m(),ot=r("p"),pr=l("Another option is to get a better traceback from the GPU. Add the following environment variable to the beginning of your code to get the traceback to point to the source of the error:"),_s=m(),d(Ie.$$.fragment),ws=m(),G=r("h2"),Z=r("a"),At=r("span"),d(Se.$$.fragment),mr=m(),Pt=r("span"),fr=l("Incorrect output when padding tokens aren't masked"),$s=m(),E=r("p"),ur=l("In some cases, the output "),Ct=r("code"),hr=l("hidden_state"),dr=l(" may be incorrect if the "),It=r("code"),cr=l("input_ids"),vr=l(" include padding tokens. To demonstrate, load a model and tokenizer. You can access a model\u2019s "),St=r("code"),gr=l("pad_token_id"),_r=l(" to see its value. The "),Ft=r("code"),wr=l("pad_token_id"),$r=l(" may be "),Ut=r("code"),br=l("None"),yr=l(" for some models, but you can always manually set it."),bs=m(),d(Fe.$$.fragment),ys=m(),rt=r("p"),kr=l("The following example shows the output without masking the padding tokens:"),ks=m(),d(Ue.$$.fragment),Es=m(),at=r("p"),Er=l("Here is the actual output of the second sequence:"),js=m(),d(qe.$$.fragment),Ts=m(),ee=r("p"),jr=l("Most of the time, you should provide an "),qt=r("code"),Tr=l("attention_mask"),Ar=l(" to your model to ignore the padding tokens to avoid this silent error. Now the output of the second sequence matches its actual output:"),As=m(),d(te.$$.fragment),Ps=m(),d(De.$$.fragment),Cs=m(),se=r("p"),Pr=l("\u{1F917} Transformers doesn\u2019t automatically create an "),Dt=r("code"),Cr=l("attention_mask"),Ir=l(" to mask a padding token if it is provided because:"),Is=m(),oe=r("ul"),Mt=r("li"),Sr=l("Some models don\u2019t have a padding token."),Fr=m(),xt=r("li"),Ur=l("For some use-cases, users want a model to attend to a padding token."),this.h()},l(e){const o=Wa('[data-svelte="svelte-1phssyn"]',document.head);h=a(o,"META",{name:!0,content:!0}),o.forEach(t),A=f(e),$=a(e,"H1",{class:!0});var Me=n($);k=a(Me,"A",{id:!0,class:!0,href:!0});var Bt=n(k);j=a(Bt,"SPAN",{});var qr=n(j);c(b.$$.fragment,qr),qr.forEach(t),Bt.forEach(t),y=f(Me),C=a(Me,"SPAN",{});var Dr=n(C);Ks=i(Dr,"Troubleshoot"),Dr.forEach(t),Me.forEach(t),Lt=f(e),Le=a(e,"P",{});var Mr=n(Le);Ws=i(Mr,"Sometimes errors occur, but we are here to help! 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Try to include as much information describing the bug as possible to help us better figure out what\u2019s wrong and how we can fix it."),Us.forEach(t),Gr.forEach(t),lo=f(Fs),mt=a(Fs,"LI",{});var Or=n(mt);he=a(Or,"P",{});var qs=n(he);io=i(qs,"Check the "),Ge=a(qs,"A",{href:!0});var zr=n(Ge);po=i(zr,"Migration"),zr.forEach(t),mo=i(qs," guide if you use an older version of \u{1F917} Transformers since some important changes have been introduced between versions."),qs.forEach(t),Or.forEach(t),Fs.forEach(t),zt=f(e),H=a(e,"P",{});var Ds=n(H);fo=i(Ds,"For more details about troubleshooting and getting help, take a look at "),de=a(Ds,"A",{href:!0,rel:!0});var Rr=n(de);uo=i(Rr,"Chapter 8"),Rr.forEach(t),ho=i(Ds," of the Hugging Face course."),Ds.forEach(t),Rt=f(e),U=a(e,"H2",{class:!0});var Ms=n(U);O=a(Ms,"A",{id:!0,class:!0,href:!0});var Vr=n(O);ft=a(Vr,"SPAN",{});var Yr=n(ft);c(ce.$$.fragment,Yr),Yr.forEach(t),Vr.forEach(t),co=f(Ms),ut=a(Ms,"SPAN",{});var Jr=n(ut);vo=i(Jr,"Firewalled environments"),Jr.forEach(t),Ms.forEach(t),Vt=f(e),He=a(e,"P",{});var Kr=n(He);go=i(Kr,"Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. 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