import{s as Sn,o as On,n as $t}from"../chunks/scheduler.bdbef820.js";import{S as Xn,i as qn,g as r,s,r as b,A as Kn,h as l,f as o,c as n,j as k,u as _,x as p,k as D,y as t,a as f,v,d as w,t as $,w as x}from"../chunks/index.33f81d56.js";import{D as U}from"../chunks/Docstring.b9574034.js";import{C as xt}from"../chunks/CodeBlock.3bad7fc9.js";import{E as wt}from"../chunks/ExampleCodeBlock.633185da.js";import{H as _t,E as ea}from"../chunks/index.9edc4842.js";function ta(L){let c,T="To activate the underflow/overflow detection, initialize the object with the model :",y,u,g;return u=new xt({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsKQ==",highlighted:"debug_overflow = DebugUnderflowOverflow(model)",wrap:!1}}),{c(){c=r("p"),c.textContent=T,y=s(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-e61xrj"&&(c.textContent=T),y=n(a),_(u.$$.fragment,a)},m(a,M){f(a,c,M),f(a,y,M),v(u,a,M),g=!0},p:$t,i(a){g||(w(u.$$.fragment,a),g=!0)},o(a){$(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(y)),x(u,a)}}}function sa(L){let c,T="mixed precision :",y,u,g;return u=new xt({props:{code:"RGV0ZWN0ZWQlMjBpbmYlMkZuYW4lMjBkdXJpbmclMjBiYXRjaF9udW1iZXIlM0QwJTBBTGFzdCUyMDIxJTIwZm9yd2FyZCUyMGZyYW1lcyUzQSUwQWFicyUyMG1pbiUyMCUyMGFicyUyMG1heCUyMCUyMG1ldGFkYXRhJTBBJTVCLi4uJTVEJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwZW5jb2Rlci5ibG9jay4yLmxheWVyLjEuRGVuc2VSZWx1RGVuc2Uud2lfMCUyMExpbmVhciUwQTIuMTdlLTA3JTIwNC41MGUlMkIwMCUyMHdlaWdodCUwQTEuNzllLTA2JTIwNC42NWUlMkIwMCUyMGlucHV0JTVCMCU1RCUwQTIuNjhlLTA2JTIwMy43MGUlMkIwMSUyMG91dHB1dCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGVuY29kZXIuYmxvY2suMi5sYXllci4xLkRlbnNlUmVsdURlbnNlLndpXzElMjBMaW5lYXIlMEE4LjA4ZS0wNyUyMDIuNjZlJTJCMDElMjB3ZWlnaHQlMEExLjc5ZS0wNiUyMDQuNjVlJTJCMDAlMjBpbnB1dCU1QjAlNUQlMEExLjI3ZS0wNCUyMDIuMzdlJTJCMDIlMjBvdXRwdXQlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBlbmNvZGVyLmJsb2NrLjIubGF5ZXIuMS5EZW5zZVJlbHVEZW5zZS53byUyMExpbmVhciUwQTEuMDFlLTA2JTIwNi40NGUlMkIwMCUyMHdlaWdodCUwQTAuMDBlJTJCMDAlMjA5Ljc0ZSUyQjAzJTIwaW5wdXQlNUIwJTVEJTBBMy4xOGUtMDQlMjA2LjI3ZSUyQjA0JTIwb3V0cHV0JTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwZW5jb2Rlci5ibG9jay4yLmxheWVyLjEuRGVuc2VSZWx1RGVuc2UlMjBUNURlbnNlR2F0ZWRHZWx1RGVuc2UlMEExLjc5ZS0wNiUyMDQuNjVlJTJCMDAlMjBpbnB1dCU1QjAlNUQlMEEzLjE4ZS0wNCUyMDYuMjdlJTJCMDQlMjBvdXRwdXQlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBlbmNvZGVyLmJsb2NrLjIubGF5ZXIuMS5kcm9wb3V0JTIwRHJvcG91dCUwQTMuMThlLTA0JTIwNi4yN2UlMkIwNCUyMGlucHV0JTVCMCU1RCUwQTAuMDBlJTJCMDAlMjAlMjAlMjAlMjAlMjAlMjBpbmYlMjBvdXRwdXQ=",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}}),{c(){c=r("p"),c.textContent=T,y=s(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-1705ugl"&&(c.textContent=T),y=n(a),_(u.$$.fragment,a)},m(a,M){f(a,c,M),f(a,y,M),v(u,a,M),g=!0},p:$t,i(a){g||(w(u.$$.fragment,a),g=!0)},o(a){$(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(y)),x(u,a)}}}function na(L){let c,T="By default the last 21 frames are printed. You can change the default to adjust for your needs. For example :",y,u,g;return u=new xt({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsJTJDJTIwbWF4X2ZyYW1lc190b19zYXZlJTNEMTAwKQ==",highlighted:'debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=<span class="hljs-number">100</span>)',wrap:!1}}),{c(){c=r("p"),c.textContent=T,y=s(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-jxu20j"&&(c.textContent=T),y=n(a),_(u.$$.fragment,a)},m(a,M){f(a,c,M),f(a,y,M),v(u,a,M),g=!0},p:$t,i(a){g||(w(u.$$.fragment,a),g=!0)},o(a){$(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(y)),x(u,a)}}}function aa(L){let c,T="given batch, and only do that for batches 1 and 3. Then you instantiate this class as :",y,u,g;return u=new xt({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}}),{c(){c=r("p"),c.textContent=T,y=s(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-1009pyu"&&(c.textContent=T),y=n(a),_(u.$$.fragment,a)},m(a,M){f(a,c,M),f(a,y,M),v(u,a,M),g=!0},p:$t,i(a){g||(w(u.$$.fragment,a),g=!0)},o(a){$(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(y)),x(u,a)}}}function ra(L){let c,T="You can also specify the batch number after which to stop the training, with :",y,u,g;return u=new xt({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>)',wrap:!1}}),{c(){c=r("p"),c.textContent=T,y=s(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-psjyqa"&&(c.textContent=T),y=n(a),_(u.$$.fragment,a)},m(a,M){f(a,c,M),f(a,y,M),v(u,a,M),g=!0},p:$t,i(a){g||(w(u.$$.fragment,a),g=!0)},o(a){$(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(y)),x(u,a)}}}function la(L){let c,T,y,u,g,a,M,qs='このページには、<a href="/docs/transformers/main/ja/main_classes/trainer#transformers.Trainer">Trainer</a> で使用されるすべてのユーティリティ関数がリストされています。',yt,X,Ks="これらのほとんどは、ライブラリ内のトレーナーのコードを学習する場合にのみ役に立ちます。",jt,q,Mt,J,K,Rt,ye,en="Evaluation output (always contains labels), to be used to compute metrics.",Tt,H,ee,Yt,je,tn="An enumeration.",Ct,A,te,St,Me,sn="Helper function for reproducible behavior during distributed training. See",Ot,Te,nn='<li><a href="https://pytorch.org/docs/stable/notes/randomness.html" rel="nofollow">https://pytorch.org/docs/stable/notes/randomness.html</a> for pytorch</li> <li><a href="https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_op_determinism" rel="nofollow">https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_op_determinism</a> for tensorflow</li>',kt,E,se,Xt,Ce,an="Helper function for reproducible behavior to set the seed in <code>random</code>, <code>numpy</code>, <code>torch</code> and/or <code>tf</code> (if installed).",Dt,Z,ne,qt,ke,rn="Decorator to make all processes in distributed training wait for each local_master to do something.",Ut,ae,Pt,V,re,Kt,De,ln="Internal class that just calls the list of callbacks in order.",Lt,le,At,h,oe,es,Ue,on="A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks.",ts,Pe,pn=`If our dataset has 16 samples with a batch size of 2 on 3 processes and we gather then transfer on CPU at every
step, our sampler will generate the following indices:`,ss,Le,mn="<code>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 0, 1]</code>",ns,Ae,dn=`to get something of size a multiple of 3 (so that each process gets the same dataset length). Then process 0, 1 and
2 will be responsible of making predictions for the following samples:`,as,Ie,cn="<li>P0: <code>[0, 1, 2, 3, 4, 5]</code></li> <li>P1: <code>[6, 7, 8, 9, 10, 11]</code></li> <li>P2: <code>[12, 13, 14, 15, 0, 1]</code></li>",rs,Je,un="The first batch treated on each process will be:",ls,He,fn="<li>P0: <code>[0, 1]</code></li> <li>P1: <code>[6, 7]</code></li> <li>P2: <code>[12, 13]</code></li>",os,Ee,hn=`So if we gather at the end of the first batch, we will get a tensor (nested list/tuple of tensor) corresponding to
the following indices:`,is,Ze,gn="<code>[0, 1, 6, 7, 12, 13]</code>",ps,Ve,bn=`If we directly concatenate our results without taking any precautions, the user will then get the predictions for
the indices in this order at the end of the prediction loop:`,ms,Ge,_n="<code>[0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1]</code>",ds,Ne,vn="For some reason, that’s not going to roll their boat. This class is there to solve that problem.",cs,N,ie,us,ze,wn=`Add <code>arrays</code> to the internal storage, Will initialize the storage to the full size at the first arrays passed
so that if we’re bound to get an OOM, it happens at the beginning.`,fs,z,pe,hs,We,$n=`Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras
to get each process a dataset of the same length).`,It,me,Jt,C,de,gs,Be,xn="This subclass of <code>argparse.ArgumentParser</code> uses type hints on dataclasses to generate arguments.",bs,Qe,yn=`The class is designed to play well with the native argparse. In particular, you can add more (non-dataclass backed)
arguments to the parser after initialization and you’ll get the output back after parsing as an additional
namespace. Optional: To create sub argument groups use the <code>_argument_group_name</code> attribute in the dataclass.`,_s,I,ce,vs,Fe,jn="Parse command-line args into instances of the specified dataclass types.",ws,Re,Mn=`This relies on argparse’s <code>ArgumentParser.parse_known_args</code>. See the doc at:
docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.parse_args`,$s,W,ue,xs,Ye,Tn=`Alternative helper method that does not use <code>argparse</code> at all, instead uses a dict and populating the dataclass
types.`,ys,B,fe,js,Se,Cn=`Alternative helper method that does not use <code>argparse</code> at all, instead loading a json file and populating the
dataclass types.`,Ms,Q,he,Ts,Oe,kn=`Alternative helper method that does not use <code>argparse</code> at all, instead loading a yaml file and populating the
dataclass types.`,Ht,ge,Et,i,be,Cs,Xe,Dn=`This debug class helps detect and understand where the model starts getting very large or very small, and more
importantly <code>nan</code> or <code>inf</code> weight and activation elements.`,ks,qe,Un="There are 2 working modes:",Ds,Ke,Pn="<li>Underflow/overflow detection (default)</li> <li>Specific batch absolute min/max tracing without detection</li>",Us,et,Ln="Mode 1: Underflow/overflow detection",Ps,F,Ls,tt,An=`then run the training as normal and if <code>nan</code> or <code>inf</code> gets detected in at least one of the weight, input or output
elements this module will throw an exception and will print <code>max_frames_to_save</code> frames that lead to this event,
each frame reporting`,As,st,In="<li>the fully qualified module name plus the class name whose <code>forward</code> was run</li> <li>the absolute min and max value of all elements for each module weights, and the inputs and output</li>",Is,nt,Jn="For example, here is the header and the last few frames in detection report for <code>google/mt5-small</code> run in fp16",Js,R,Hs,at,Hn=`You can see here, that <code>T5DenseGatedGeluDense.forward</code> resulted in output activations, whose absolute max value was
around 62.7K, which is very close to fp16’s top limit of 64K. In the next frame we have <code>Dropout</code> which
renormalizes the weights, after it zeroed some of the elements, which pushes the absolute max value to more than
64K, and we get an overflow.`,Es,rt,En=`As you can see it’s the previous frames that we need to look into when the numbers start going into very large for
fp16 numbers.`,Zs,lt,Zn="The tracking is done in a forward hook, which gets invoked immediately after <code>forward</code> has completed.",Vs,Y,Gs,ot,Vn=`To validate that you have set up this debugging feature correctly, and you intend to use it in a training that
may take hours to complete, first run it with normal tracing enabled for one of a few batches as explained in
the next section.`,Ns,it,Gn="Mode 2. Specific batch absolute min/max tracing without detection",zs,pt,Nn="The second work mode is per-batch tracing with the underflow/overflow detection feature turned off.",Ws,mt,zn="Let’s say you want to watch the absolute min and max values for all the ingredients of each <code>forward</code> call of a",Bs,S,Qs,dt,Wn="And now full batches 1 and 3 will be traced using the same format as explained above. Batches are 0-indexed.",Fs,ct,Bn=`This is helpful if you know that the program starts misbehaving after a certain batch number, so you can
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