import{s as Ss,o as Os,n as wt}from"../chunks/scheduler.9bc65507.js";import{S as Xs,i as qs,g as r,s as n,r as b,A as Ks,h as l,f as o,c as s,j as U,u as _,x as p,k,y as t,a as f,v,d as $,t as w,w as x}from"../chunks/index.707bf1b6.js";import{D as L}from"../chunks/Docstring.570ddb64.js";import{C as xt}from"../chunks/CodeBlock.54a9f38d.js";import{E as $t}from"../chunks/ExampleCodeBlock.bc2be6bc.js";import{H as _t,E as ea}from"../chunks/EditOnGithub.922df6ba.js";function ta(D){let c,T="To activate the underflow/overflow detection, initialize the object with the model :",j,u,g;return u=new xt({props:{code:"ZGVidWdfb3ZlcmZsb3clMjAlM0QlMjBEZWJ1Z1VuZGVyZmxvd092ZXJmbG93KG1vZGVsKQ==",highlighted:"debug_overflow = DebugUnderflowOverflow(model)",wrap:!1}}),{c(){c=r("p"),c.textContent=T,j=n(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-e61xrj"&&(c.textContent=T),j=s(a),_(u.$$.fragment,a)},m(a,y){f(a,c,y),f(a,j,y),v(u,a,y),g=!0},p:wt,i(a){g||($(u.$$.fragment,a),g=!0)},o(a){w(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(j)),x(u,a)}}}function na(D){let c,T="mixed precision :",j,u,g;return u=new xt({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}}),{c(){c=r("p"),c.textContent=T,j=n(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-1705ugl"&&(c.textContent=T),j=s(a),_(u.$$.fragment,a)},m(a,y){f(a,c,y),f(a,j,y),v(u,a,y),g=!0},p:wt,i(a){g||($(u.$$.fragment,a),g=!0)},o(a){w(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(j)),x(u,a)}}}function sa(D){let c,T="By default the last 21 frames are printed. You can change the default to adjust for your needs. For example :",j,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,j=n(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-jxu20j"&&(c.textContent=T),j=s(a),_(u.$$.fragment,a)},m(a,y){f(a,c,y),f(a,j,y),v(u,a,y),g=!0},p:wt,i(a){g||($(u.$$.fragment,a),g=!0)},o(a){w(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(j)),x(u,a)}}}function aa(D){let c,T="given batch, and only do that for batches 1 and 3. Then you instantiate this class as :",j,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,j=n(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-1009pyu"&&(c.textContent=T),j=s(a),_(u.$$.fragment,a)},m(a,y){f(a,c,y),f(a,j,y),v(u,a,y),g=!0},p:wt,i(a){g||($(u.$$.fragment,a),g=!0)},o(a){w(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(j)),x(u,a)}}}function ra(D){let c,T="You can also specify the batch number after which to stop the training, with :",j,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,j=n(),b(u.$$.fragment)},l(a){c=l(a,"P",{"data-svelte-h":!0}),p(c)!=="svelte-psjyqa"&&(c.textContent=T),j=s(a),_(u.$$.fragment,a)},m(a,y){f(a,c,y),f(a,j,y),v(u,a,y),g=!0},p:wt,i(a){g||($(u.$$.fragment,a),g=!0)},o(a){w(u.$$.fragment,a),g=!1},d(a){a&&(o(c),o(j)),x(u,a)}}}function la(D){let c,T,j,u,g,a,y,Kn='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。',jt,X,es="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。",Mt,q,yt,A,K,Rt,je,ts="Evaluation output (always contains labels), to be used to compute metrics.",Tt,H,ee,Yt,Me,ns="An enumeration.",Ct,I,te,St,ye,ss="Helper function for reproducible behavior during distributed training. See",Ot,Te,as='<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>',Ut,E,ne,Xt,Ce,rs="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).",kt,Z,se,qt,Ue,ls="Decorator to make all processes in distributed training wait for each local_master to do something.",Lt,ae,Jt,V,re,Kt,ke,os="Internal class that just calls the list of callbacks in order.",Dt,le,It,h,oe,en,Le,is="A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks.",tn,Je,ps=`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:`,nn,De,ms="<code>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 0, 1]</code>",sn,Ie,ds=`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:`,an,Pe,cs="<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>",rn,Ae,us="The first batch treated on each process will be",ln,He,fs="<li>P0: <code>[0, 1]</code></li> <li>P1: <code>[6, 7]</code></li> <li>P2: <code>[12, 13]</code></li>",on,Ee,hs=`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:`,pn,Ze,gs="<code>[0, 1, 6, 7, 12, 13]</code>",mn,Ve,bs=`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:`,dn,Ge,_s="<code>[0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1]</code>",cn,ze,vs="For some reason, that’s not going to roll their boat. This class is there to solve that problem.",un,z,ie,fn,Ne,$s=`Add <code>arrays</code> to the internal storage, Will initialize the storage to the full size at the first arrays passed
so that if we’re bound to get an OOM, it happens at the beginning.`,hn,N,pe,gn,We,ws=`Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras
to get each process a dataset of the same length).`,Pt,me,At,C,de,bn,Be,xs="This subclass of <code>argparse.ArgumentParser</code> uses type hints on dataclasses to generate arguments.",_n,Qe,js=`The class is designed to play well with the native argparse. In particular, you can add more (non-dataclass backed)
arguments to the parser after initialization and you’ll get the output back after parsing as an additional
namespace. Optional: To create sub argument groups use the <code>_argument_group_name</code> attribute in the dataclass.`,vn,P,ce,$n,Fe,Ms="Parse command-line args into instances of the specified dataclass types.",wn,Re,ys=`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`,xn,W,ue,jn,Ye,Ts=`Alternative helper method that does not use <code>argparse</code> at all, instead uses a dict and populating the dataclass
types.`,Mn,B,fe,yn,Se,Cs=`Alternative helper method that does not use <code>argparse</code> at all, instead loading a json file and populating the
dataclass types.`,Tn,Q,he,Cn,Oe,Us=`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,Un,Xe,ks=`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.`,kn,qe,Ls="There are 2 working modes:",Ln,Ke,Js="<li>Underflow/overflow detection (default)</li> <li>Specific batch absolute min/max tracing without detection</li>",Jn,et,Ds="Mode 1: Underflow/overflow detection",Dn,F,In,tt,Is=`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`,Pn,nt,Ps="<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>",An,st,As="For example, here is the header and the last few frames in detection report for <code>google/mt5-small</code> run in fp16",Hn,R,En,at,Hs=`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 overlow.`,Zn,rt,Es=`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.`,Vn,lt,Zs="The tracking is done in a forward hook, which gets invoked immediately after <code>forward</code> has completed.",Gn,Y,zn,ot,Vs=`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.`,Nn,it,Gs="Mode 2. Specific batch absolute min/max tracing without detection",Wn,pt,zs="The second work mode is per-batch tracing with the underflow/overflow detection feature turned off.",Bn,mt,Ns="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",Qn,S,Fn,dt,Ws="And now full batches 1 and 3 will be traced using the same format as explained above. Batches are 0-indexed.",Rn,ct,Bs=`This is helpful if you know that the program starts misbehaving after a certain batch number, so you can
fast-forward right to that area.`,Yn,ut,Qs="Early stopping:",Sn,O,On,ft,Fs="This feature is mainly useful in the tracing mode, but you can use it for any mode.",Xn,ht,Rs="<strong>Performance</strong>:",qn,gt,Ys="As this module measures absolute <code>min</code>/`<code>max</code> of each weight of the model on every forward it’ll slow the training\ndown. Therefore remember to turn it off once the debugging needs have been met.",Zt,_e,Vt,vt,Gt;return g=new _t({props:{title:"トレーナー用ユーティリティ",local:"トレーナー用ユーティリティ",headingTag:"h1"}}),q=new _t({props:{title:"Utilities",local:"transformers.EvalPrediction",headingTag:"h2"}}),K=new L({props:{name:"class transformers.EvalPrediction",anchor:"transformers.EvalPrediction",parameters:[{name:"predictions",val:": Union"},{name:"label_ids",val:": Union"},{name:"inputs",val:": Union = None"}],parametersDescription:[{anchor:"transformers.EvalPrediction.predictions",description:"<strong>predictions</strong> (<code>np.ndarray</code>) &#x2014; Predictions of the model.",name:"predictions"},{anchor:"transformers.EvalPrediction.label_ids",description:"<strong>label_ids</strong> (<code>np.ndarray</code>) &#x2014; Targets to be matched.",name:"label_ids"},{anchor:"transformers.EvalPrediction.inputs",description:"<strong>inputs</strong> (<code>np.ndarray</code>, <em>optional</em>) &#x2014;",name:"inputs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_utils.py#L144"}}),ee=new L({props:{name:"class transformers.IntervalStrategy",anchor:"transformers.IntervalStrategy",parameters:[{name:"value",val:""},{name:"names",val:" = None"},{name:"module",val:" = None"},{name:"qualname",val:" = None"},{name:"type",val:" = None"},{name:"start",val:" = 1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_utils.py#L218"}}),te=new L({props:{name:"transformers.enable_full_determinism",anchor:"transformers.enable_full_determinism",parameters:[{name:"seed",val:": int"},{name:"warn_only",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/trainer_utils.py#L59"}}),ne=new L({props:{name:"transformers.set_seed",anchor:"transformers.set_seed",parameters:[{name:"seed",val:": int"},{name:"deterministic",val:": bool = False"}],parametersDescription:[{anchor:"transformers.set_seed.seed",description:`<strong>seed</strong> (<code>int</code>) &#x2014;
The seed to set.`,name:"seed"},{anchor:"transformers.set_seed.deterministic",description:`<strong>deterministic</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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<ul>
<li>the dataclass instances in the same order as they were passed to the initializer.abspath</li>
<li>if applicable, an additional namespace for more (non-dataclass backed) arguments added to the parser
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<li>The potential list of remaining argument strings. (same as argparse.ArgumentParser.parse_known_args)</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple consisting of</p>
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<ul>
<li>the dataclass instances in the same order as they were passed to the initializer.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple consisting of</p>
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<ul>
<li>the dataclass instances in the same order as they were passed to the initializer.</li>
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`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple consisting of</p>
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<ul>
<li>the dataclass instances in the same order as they were passed to the initializer.</li>
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`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple consisting of</p>
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