import{s as ea,o as la,n as rl}from"../chunks/scheduler.36a0863c.js";import{S as aa,i as na,g as p,s as n,u as c,A as ta,h as i,f as l,c as t,j as J,v as m,y as r,k as b,o as T,a,w as M,d as j,t as d,x as w}from"../chunks/index.d0f7a4c4.js";import{T as il}from"../chunks/Tip.4c7d7eb1.js";import{I as ll}from"../chunks/IconCopyLink.bc5fad79.js";import{C as h}from"../chunks/CodeBlock.4236768d.js";function pa(Z){let o,y="Questa funzionalità al momento è disponibile solo per PyTorch.";return{c(){o=p("p"),o.textContent=y},l(u){o=i(u,"P",{"data-svelte-h":!0}),r(o)!=="svelte-ecm9v3"&&(o.textContent=y)},m(u,I){a(u,o,I)},p:rl,d(u){u&&l(o)}}}function ia(Z){let o,y="Per addestramento multi-GPU richiede DDP (<code>torch.distributed.launch</code>).";return{c(){o=p("p"),o.innerHTML=y},l(u){o=i(u,"P",{"data-svelte-h":!0}),r(o)!=="svelte-o96oxl"&&(o.innerHTML=y)},m(u,I){a(u,o,I)},p:rl,d(u){u&&l(o)}}}function ra(Z){let o,y="Questa funzionalità può essere usata con modelli basati su <code>nn.Module</code>.";return{c(){o=p("p"),o.innerHTML=y},l(u){o=i(u,"P",{"data-svelte-h":!0}),r(o)!=="svelte-9uk85f"&&(o.innerHTML=y)},m(u,I){a(u,o,I)},p:rl,d(u){u&&l(o)}}}function oa(Z){let o,y,u,I,U,A,Es,$,al,Ns,ol="Debugging",Ys,f,G,Qs,k,nl,xs,ul="Debug dei problemi di rete multi-GPU",Fs,N,cl="Quando addestri o fai inferenza con <code>DistributedDataParallel</code> e GPU multiple, se si verificano problemi di intercomunicazione tra processi e/o nodi, puoi utilizzare il seguente script per diagnosticare i problemi della rete.",Ps,x,qs,L,ml="Per esempio per testare come 2 GPU interagiscono fai:",Ks,W,Os,E,Ml="Se entrambi i processi sono in grado di comunicare tra loro e di allocare la memoria della GPU, ciascuno di essi stamperà lo stato OK.",se,Q,jl="Per più GPU o nodi adatta gli argumenti nello script.",ee,X,dl="All’interno dello script di diagnostica troverai molti altri dettagli e anche una guida per eseguirlo in ambiente SLURM.",le,z,wl="Un livello di debug superiore è aggiungere la variabile d’ambiente <code>NCCL_DEBUG=INFO</code> come di seguito:",ae,D,ne,S,bl="In questo modo si scaricano molte informazioni di debug relative a NCCL, che puoi cercare online in caso di problemi. Oppure, se non hai la sicurezza di come interpretare l’output, puoi condividere il file di log in una Issue.",te,C,V,Xs,H,tl,Ls,Tl="Rilevamento di Underflow e Overflow",pe,g,ie,B,re,R,oe,Y,hl=`Se inizi a ottenere <code>loss=NaN</code> o il modello presenta qualche altro comportamento anomalo a causa di valori <code>inf</code> o <code>nan</code> in
attivazioni o nei pesi, è necessario scoprire dove si verifica il primo underflow o overflow e cosa lo ha determinato. Fortunatamente
è possibile farlo facilmente attivando un modulo speciale che effettuerà il rilevamento automaticamente.`,ue,F,Jl="Se stai usando <code>Trainer</code>, hai bisogno di aggiungere solo:",ce,P,me,q,yl=`ai normali argomenti della riga di comando, o passa <code>debug=&quot;underflow_overflow&quot;</code> quando viene creato l’oggetto
<code>TrainingArguments</code>.`,Me,K,Il="Se stai usando il tuo ciclo di allenamento o un altro trainer, puoi ottenere lo stesso risultato con:",je,O,de,ss,Ul=`<code>DebugUnderflowOverflow</code> inserisce dei ganci nel modello che dopo ogni chiamata
testeranno le variabili di ingresso e di uscita e anche i pesi del modulo corrispondente. Non appena viene rilevato <code>inf</code> o
o <code>nan</code> in almeno un elemento delle attivazioni o dei pesi, il programma lo notifica e stampa un rapporto come il seguente (questo è stato rilevato con <code>google/mt5-small</code> sotto fp16 mixed precision):`,we,es,be,ls,fl="L’output di esempio è stato tagliato al centro per brevità.",Te,as,Cl=`La seconda colonna mostra il valore dell’elemento più grande in assoluto,così se osserviamo da vicino gli ultimi istanti,
input e output sono nel range di <code>1e4</code>. Questo addestramento è stato eseguito con una mixed precision fp16 e l’ultimo passo usciva fuori (sotto <code>fp16</code> il valore più grande prima di <code>inf</code> è <code>64e3</code>). Per evitare overflows sotto <code>fp16</code> le attivazionioni devono rimanere molto al di sotto di <code>1e4</code>, perché <code>1e4 * 1e4 = 1e8</code> quindi qualsiasi moltiplicazione di matrice con grandi attivazioni porterà a una condizione di overflow numerico.`,he,ns,vl="All’inizio della traccia è possibile scoprire a quale lotto si è verificato il problema (questo <code>Detected inf/nan during batch_number=0</code> significa che il problema si è verificato nel primo lotto).",Je,ts,Zl=`Ogni frame segnalato inizia dichiarando la voce completamente qualificata per il modulo corrispondente per il quale il frame è stato segnalato.
Se osserviamo il seguente frame:`,ye,ps,Ie,is,Al="Questo, <code>encoder.block.2.layer.1.layer_norm</code> indica che si tratta di un layer norm nel primo layer, del secondo blocco dell’encoder. E le chiamata specifica di <code>forward</code> è <code>T5LayerNorm</code>.",Ue,rs,Gl="Osserviamo gli ultimi frame del report:",fe,os,Ce,us,Vl="L’ultimo frame report per la funzione <code>Dropout.forward</code> con la prima voce per l’unico input e la seconda per l’unico output. Si può notare che è stato richiamato da un attibuto <code>dropout</code> dentro la classe <code>DenseReluDense</code>. Si può notare che ciò è avvenuto durante il primo strato, del 2° blocco, durante il primissimo lotto. Infine, gli elementi di input più grandi in assoluto sono stati <code>6.27e+04</code> e l’equivalente per l’output era <code>inf</code>.",ve,cs,gl="Puoi vedere qui, che <code>T5DenseGatedGeluDense.forward</code> risulta in output activations, il cui valore massimo assoluto era circa 62,7K, che è molto vicino al limite massimo di 64K di fp16. Nel prossimo frame abbiamo <code>Dropout</code> che rinormalizza i pesi, dopo aver azzerato alcuni elementi, il che spinge il valore massimo assoluto a più di 64K e si verifica un overflow.(<code>inf</code>).",Ze,ms,Bl="Come puoi notare, è nei frames precedenti che occorre esaminare quando i numeri iniziano a diventare molto grandi per i valori fp16.",Ae,Ms,Rl="Confrontiamo il report al codice <code>models/t5/modeling_t5.py</code>:",Ge,js,Ve,ds,_l="Ora è facile vedere la chiamata <code>dropout</code>, e tutte le chiamate precedenti.",ge,ws,$l="Poiché il rilevamento avviene in un avanzamento (forward hook in eng.), i rapporti vengono creati immeditamente dopo ogni rientro da <code>forward</code> (forward returns in eng.).",Be,bs,kl="Tornando al rapporto completo, per agire e risolvere il problema, dobbiamo andare qualche frame più in alto, dove i numeri hanno iniziato a salire, e probabilmente passare alla modalità <code>fp32</code>, in modo che i numeri non trabocchino quando vengono moltiplicati o sommati. Naturalmente, potrebbero esserci altre soluzioni. Per esempio, potremmo spegnere temporanemante <code>amp</code> se è abilitato, successivamente spostare <code>forward</code> in un helper wrapper, come:",Re,Ts,_e,hs,Nl=`Poiché il rilevatore automatico riporta solo gli ingressi e le uscite di fotogrammi completi, una volta che si sa dove cercare, si può
analizzare anche le fasi intermedie di una specifica funzione <code>forward</code>. In alcuni casi puoi usare la funzione di supporto <code>detect_overflow</code> per indirizzare il rilevatore dove preferisci, ad esempio:`,$e,Js,ke,ys,xl=`Si può vedere che abbiamo aggiunto 2 di questi e ora teniamo traccia se <code>inf</code> o <code>nan</code> per <code>forwarded_states</code> è stato rilevato
da qualche parte.`,Ne,Is,Ll=`In realtà, il rilevatore li riporta già, perché ciascuna delle chiamate nell’esempio precedente è un <code>nn.Module</code>, ma
diciamo che se avessimo dei calcoli diretti locali, questo è il modo in cui lo faremmo.`,xe,Us,Wl=`Inoltre, se si istanzia il debugger nel proprio codice, è possibile modificare il numero di fotogrammi stampati rispetto a
predefinito, ad esempio.:`,Le,fs,We,v,_,zs,Cs,pl,Ws,El="Tracciamento della mistura assoluta del lotto specifico e del valore massimo",Ee,vs,Ql="La stessa classe di debug può essere utilizzata per il tracciamento per-batch con la funzione di rilevamento di underflow/overflow disattivata.",Qe,Zs,Xl=`Supponiamo di voler osservare i valori minimi e massimi assoluti per tutti gli ingredienti di ogni chiamata <code>forward</code> di un dato lotto.
lotto, e che lo si voglia fare solo per i lotti 1 e 3. Si istanzia questa classe come:`,Xe,As,ze,Gs,zl="Ora i batch completi 1 e 3 saranno tracciati utilizzando lo stesso formato del rilevatore di underflow/overflow.",De,Vs,Dl="I batches sono 0-indexed.",Se,gs,Sl=`Questo è utile se si sa che il programma inizia a comportarsi male dopo un certo numero di batch, in modo da poter avanzare velocemente fino a quell’area.
direttamente a quell’area. Ecco un esempio di output troncato per questa configurazione:`,He,Bs,Ye,Rs,Hl="Qui verrà scaricato un numero enorme di fotogrammi, tanti quanti sono le chiamate in avanti nel modello, quindi può essere o non essere quello che volete, ma a volte può essere più utile usarlo di un classico debugger. Per esempio, se il problema inizia a verificarsi a partire dal lotto numero 150. Quindi è possibile scaricare le tracce dei lotti 149 e 150 e confrontare i punti in cui i numeri hanno iniziato a divergere.",Fe,_s,Yl="È inoltre possibile specificare il numero di batch dopo il quale interrompere l’addestramento, con:",Pe,$s,qe,Ds,Ke;return $=new ll({}),k=new ll({}),x=new h({props:{code:"d2dldCUyMGh0dHBzJTNBJTJGJTJGcmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSUyRmh1Z2dpbmdmYWNlJTJGdHJhbnNmb3JtZXJzJTJGbWFpbiUyRnNjcmlwdHMlMkZkaXN0cmlidXRlZCUyRnRvcmNoLWRpc3RyaWJ1dGVkLWdwdS10ZXN0LnB5",highlighted:"wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py",wrap:!1}}),W=new h({props:{code:"cHl0aG9uJTIwLW0lMjB0b3JjaC5kaXN0cmlidXRlZC5ydW4lMjAtLW5wcm9jX3Blcl9ub2RlJTIwMiUyMC0tbm5vZGVzJTIwMSUyMHRvcmNoLWRpc3RyaWJ1dGVkLWdwdS10ZXN0LnB5",highlighted:"python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py",wrap:!1}}),D=new h({props:{code:"TkNDTF9ERUJVRyUzRElORk8lMjBweXRob24lMjAtbSUyMHRvcmNoLmRpc3RyaWJ1dGVkLnJ1biUyMC0tbnByb2NfcGVyX25vZGUlMjAyJTIwLS1ubm9kZXMlMjAxJTIwdG9yY2gtZGlzdHJpYnV0ZWQtZ3B1LXRlc3QucHk=",highlighted:"NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py",wrap:!1}}),H=new ll({}),g=new il({props:{$$slots:{default:[pa]},$$scope:{ctx:Z}}}),B=new il({props:{$$slots:{default:[ia]},$$scope:{ctx:Z}}}),R=new il({props:{$$slots:{default:[ra]},$$scope:{ctx:Z}}}),P=new h({props:{code:"LS1kZWJ1ZyUyMHVuZGVyZmxvd19vdmVyZmxvdw==",highlighted:"--debug underflow_overflow",wrap:!1}}),O=new h({props:{code:"ZnJvbSUyMC5kZWJ1Z191dGlscyUyMGltcG9ydCUyMERlYnVnVW5kZXJmbG93T3ZlcmZsb3clMEElMEFkZWJ1Z19vdmVyZmxvdyUyMCUzRCUyMERlYnVnVW5kZXJmbG93T3ZlcmZsb3cobW9kZWwp",highlighted:`<span class="hljs-keyword">from</span> .debug_utils <span class="hljs-keyword">import</span> DebugUnderflowOverflow

debug_overflow = DebugUnderflowOverflow(model)`,wrap:!1}}),es=new h({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-attribute">encoder</span>.block.<span class="hljs-number">1</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.dropout Dropout
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> <span class="hljs-number">2</span>.<span class="hljs-number">57</span>e+<span class="hljs-number">02</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> <span class="hljs-number">2</span>.<span class="hljs-number">85</span>e+<span class="hljs-number">02</span> output<span class="hljs-meta">
[...]</span>
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">0</span> T5LayerSelfAttention
<span class="hljs-attribute">6</span>.<span class="hljs-number">78</span>e-<span class="hljs-number">04</span> <span class="hljs-number">3</span>.<span class="hljs-number">15</span>e+<span class="hljs-number">03</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">2</span>.<span class="hljs-number">65</span>e-<span class="hljs-number">04</span> <span class="hljs-number">3</span>.<span class="hljs-number">42</span>e+<span class="hljs-number">03</span> output[<span class="hljs-number">0</span>]
             <span class="hljs-attribute">None</span> output[<span class="hljs-number">1</span>]
<span class="hljs-attribute">2</span>.<span class="hljs-number">25</span>e-<span class="hljs-number">01</span> <span class="hljs-number">1</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">04</span> output[<span class="hljs-number">2</span>]
                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.layer_norm T5LayerNorm
<span class="hljs-attribute">8</span>.<span class="hljs-number">69</span>e-<span class="hljs-number">02</span> <span class="hljs-number">4</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">01</span> weight
<span class="hljs-attribute">2</span>.<span class="hljs-number">65</span>e-<span class="hljs-number">04</span> <span class="hljs-number">3</span>.<span class="hljs-number">42</span>e+<span class="hljs-number">03</span> input[<span class="hljs-number">0</span>]
<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> output
                  <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.dropout Dropout
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> <span class="hljs-number">8</span>.<span class="hljs-number">76</span>e+<span class="hljs-number">03</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> <span class="hljs-number">9</span>.<span class="hljs-number">74</span>e+<span class="hljs-number">03</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`,wrap:!1}}),ps=new h({props:{code:"JTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwZW5jb2Rlci5ibG9jay4yLmxheWVyLjEubGF5ZXJfbm9ybSUyMFQ1TGF5ZXJOb3JtJTBBOC42OWUtMDIlMjA0LjE4ZS0wMSUyMHdlaWdodCUwQTIuNjVlLTA0JTIwMy40MmUlMkIwMyUyMGlucHV0JTVCMCU1RCUwQTEuNzllLTA2JTIwNC42NWUlMkIwMCUyMG91dHB1dA==",highlighted:`                  <span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.layer_norm T5LayerNorm
<span class="hljs-attribute">8</span>.<span class="hljs-number">69</span>e-<span class="hljs-number">02</span> <span class="hljs-number">4</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">01</span> weight
<span class="hljs-attribute">2</span>.<span class="hljs-number">65</span>e-<span class="hljs-number">04</span> <span class="hljs-number">3</span>.<span class="hljs-number">42</span>e+<span class="hljs-number">03</span> input[<span class="hljs-number">0</span>]
<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> output`,wrap:!1}}),os=new h({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`,wrap:!1}}),js=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">T5DenseGatedGeluDense</span>(nn.Module):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, config</span>):
        <span class="hljs-built_in">super</span>().__init__()
        self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=<span class="hljs-literal">False</span>)
        self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=<span class="hljs-literal">False</span>)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=<span class="hljs-literal">False</span>)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.gelu_act = ACT2FN[<span class="hljs-string">&quot;gelu_new&quot;</span>]

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
        hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.wo(hidden_states)
        <span class="hljs-keyword">return</span> hidden_states`,wrap:!1}}),Ts=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">_forward</span>(<span class="hljs-params">self, hidden_states</span>):
    hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
    hidden_linear = self.wi_1(hidden_states)
    hidden_states = hidden_gelu * hidden_linear
    hidden_states = self.dropout(hidden_states)
    hidden_states = self.wo(hidden_states)
    <span class="hljs-keyword">return</span> hidden_states


<span class="hljs-keyword">import</span> torch


<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
    <span class="hljs-keyword">if</span> torch.is_autocast_enabled():
        <span class="hljs-keyword">with</span> torch.cuda.amp.autocast(enabled=<span class="hljs-literal">False</span>):
            <span class="hljs-keyword">return</span> self._forward(hidden_states)
    <span class="hljs-keyword">else</span>:
        <span class="hljs-keyword">return</span> self._forward(hidden_states)`,wrap:!1}}),Js=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> debug_utils <span class="hljs-keyword">import</span> detect_overflow


<span class="hljs-keyword">class</span> <span class="hljs-title class_">T5LayerFF</span>(nn.Module):
    [...]

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
        forwarded_states = self.layer_norm(hidden_states)
        detect_overflow(forwarded_states, <span class="hljs-string">&quot;after layer_norm&quot;</span>)
        forwarded_states = self.DenseReluDense(forwarded_states)
        detect_overflow(forwarded_states, <span class="hljs-string">&quot;after DenseReluDense&quot;</span>)
        <span class="hljs-keyword">return</span> hidden_states + self.dropout(forwarded_states)`,wrap:!1}}),fs=new h({props:{code:"ZnJvbSUyMC5kZWJ1Z191dGlscyUyMGltcG9ydCUyMERlYnVnVW5kZXJmbG93T3ZlcmZsb3clMEElMEFkZWJ1Z19vdmVyZmxvdyUyMCUzRCUyMERlYnVnVW5kZXJmbG93T3ZlcmZsb3cobW9kZWwlMkMlMjBtYXhfZnJhbWVzX3RvX3NhdmUlM0QxMDAp",highlighted:`<span class="hljs-keyword">from</span> .debug_utils <span class="hljs-keyword">import</span> DebugUnderflowOverflow

debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=<span class="hljs-number">100</span>)`,wrap:!1}}),Cs=new ll({}),As=new h({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsJTJDJTIwdHJhY2VfYmF0Y2hfbnVtcyUzRCU1QjElMkMlMjAzJTVEKQ==",highlighted:'debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>])',wrap:!1}}),Bs=new h({props:{code:"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",highlighted:`                  *** Starting batch number=1 ***
abs min  abs max  metadata
                  shared Embedding
1.01e<span class="hljs-string">-06</span> 7.92e<span class="hljs-string">+02</span> weight
0.00e<span class="hljs-string">+00</span> 2.47e<span class="hljs-string">+04</span> input[0]
5.36e<span class="hljs-string">-05</span> 7.92e<span class="hljs-string">+02</span> output
[...]
                  decoder.dropout Dropout
1.60e<span class="hljs-string">-07</span> 2.27e<span class="hljs-string">+01</span> input[0]
0.00e<span class="hljs-string">+00</span> 2.52e<span class="hljs-string">+01</span> output
                  decoder T5Stack
     not a tensor output
                  lm_head Linear
1.01e<span class="hljs-string">-06</span> 7.92e<span class="hljs-string">+02</span> weight
0.00e<span class="hljs-string">+00</span> 1.11e<span class="hljs-string">+00</span> input[0]
6.06e<span class="hljs-string">-02</span> 8.39e<span class="hljs-string">+01</span> output
                   T5ForConditionalGeneration
     not a tensor output

                  *** Starting batch number=3 ***
abs min  abs max  metadata
                  shared Embedding
1.01e<span class="hljs-string">-06</span> 7.92e<span class="hljs-string">+02</span> weight
0.00e<span class="hljs-string">+00</span> 2.78e<span class="hljs-string">+04</span> input[0]
5.36e<span class="hljs-string">-05</span> 7.92e<span class="hljs-string">+02</span> output
[...]`,wrap:!1}}),$s=new h({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 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