import{s as za,f as or,o as Ma,n as ir}from"../chunks/scheduler.9bc65507.js";import{S as Aa,i as Ca,g as l,s as a,r as p,A as La,h as m,f as t,c as n,j as z,u as d,x as w,k as b,y as o,a as s,v as h,d as u,t as f,w as g}from"../chunks/index.707bf1b6.js";import{D as M}from"../chunks/Docstring.570ddb64.js";import{C as lr}from"../chunks/CodeBlock.54a9f38d.js";import{E as sr}from"../chunks/ExampleCodeBlock.bc2be6bc.js";import{H,E as Wa}from"../chunks/EditOnGithub.922df6ba.js";function ja(U){let c,A="Example:",x,_,v;return _=new lr({props:{code:"QWRhZmFjdG9yKG1vZGVsLnBhcmFtZXRlcnMoKSUyQyUyMHNjYWxlX3BhcmFtZXRlciUzREZhbHNlJTJDJTIwcmVsYXRpdmVfc3RlcCUzREZhbHNlJTJDJTIwd2FybXVwX2luaXQlM0RGYWxzZSUyQyUyMGxyJTNEMWUtMyk=",highlighted:'Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">False</span>, relative_step=<span class="hljs-literal">False</span>, warmup_init=<span class="hljs-literal">False</span>, lr=<span class="hljs-number">1e-3</span>)',wrap:!1}}),{c(){c=l("p"),c.textContent=A,x=a(),p(_.$$.fragment)},l(i){c=m(i,"P",{"data-svelte-h":!0}),w(c)!=="svelte-11lpom8"&&(c.textContent=A),x=n(i),d(_.$$.fragment,i)},m(i,T){s(i,c,T),s(i,x,T),h(_,i,T),v=!0},p:ir,i(i){v||(u(_.$$.fragment,i),v=!0)},o(i){f(_.$$.fragment,i),v=!1},d(i){i&&(t(c),t(x)),g(_,i)}}}function Da(U){let c,A="Others reported the following combination to work well:",x,_,v;return _=new lr({props:{code:"QWRhZmFjdG9yKG1vZGVsLnBhcmFtZXRlcnMoKSUyQyUyMHNjYWxlX3BhcmFtZXRlciUzRFRydWUlMkMlMjByZWxhdGl2ZV9zdGVwJTNEVHJ1ZSUyQyUyMHdhcm11cF9pbml0JTNEVHJ1ZSUyQyUyMGxyJTNETm9uZSk=",highlighted:'Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">True</span>, relative_step=<span class="hljs-literal">True</span>, warmup_init=<span class="hljs-literal">True</span>, lr=<span class="hljs-literal">None</span>)',wrap:!1}}),{c(){c=l("p"),c.textContent=A,x=a(),p(_.$$.fragment)},l(i){c=m(i,"P",{"data-svelte-h":!0}),w(c)!=="svelte-mxeef1"&&(c.textContent=A),x=n(i),d(_.$$.fragment,i)},m(i,T){s(i,c,T),s(i,x,T),h(_,i,T),v=!0},p:ir,i(i){v||(u(_.$$.fragment,i),v=!0)},o(i){f(_.$$.fragment,i),v=!1},d(i){i&&(t(c),t(x)),g(_,i)}}}function Ua(U){let c,A="scheduler as following:",x,_,v;return _=new lr({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycy5vcHRpbWl6YXRpb24lMjBpbXBvcnQlMjBBZGFmYWN0b3IlMkMlMjBBZGFmYWN0b3JTY2hlZHVsZSUwQSUwQW9wdGltaXplciUyMCUzRCUyMEFkYWZhY3Rvcihtb2RlbC5wYXJhbWV0ZXJzKCklMkMlMjBzY2FsZV9wYXJhbWV0ZXIlM0RUcnVlJTJDJTIwcmVsYXRpdmVfc3RlcCUzRFRydWUlMkMlMjB3YXJtdXBfaW5pdCUzRFRydWUlMkMlMjBsciUzRE5vbmUpJTBBbHJfc2NoZWR1bGVyJTIwJTNEJTIwQWRhZmFjdG9yU2NoZWR1bGUob3B0aW1pemVyKSUwQXRyYWluZXIlMjAlM0QlMjBUcmFpbmVyKC4uLiUyQyUyMG9wdGltaXplcnMlM0Qob3B0aW1pemVyJTJDJTIwbHJfc2NoZWR1bGVyKSk=",highlighted:`<span class="hljs-keyword">from</span> transformers.optimization <span class="hljs-keyword">import</span> Adafactor, AdafactorSchedule

optimizer = Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">True</span>, relative_step=<span class="hljs-literal">True</span>, warmup_init=<span class="hljs-literal">True</span>, lr=<span class="hljs-literal">None</span>)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(..., optimizers=(optimizer, lr_scheduler))`,wrap:!1}}),{c(){c=l("p"),c.textContent=A,x=a(),p(_.$$.fragment)},l(i){c=m(i,"P",{"data-svelte-h":!0}),w(c)!=="svelte-1eua222"&&(c.textContent=A),x=n(i),d(_.$$.fragment,i)},m(i,T){s(i,c,T),s(i,x,T),h(_,i,T),v=!0},p:ir,i(i){v||(u(_.$$.fragment,i),v=!0)},o(i){f(_.$$.fragment,i),v=!1},d(i){i&&(t(c),t(x)),g(_,i)}}}function Ra(U){let c,A="Usage:",x,_,v;return _=new lr({props:{code:"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",highlighted:`<span class="hljs-comment"># replace AdamW with Adafactor</span>
optimizer = Adafactor(
    model.parameters(),
    lr=<span class="hljs-number">1e-3</span>,
    eps=(<span class="hljs-number">1e-30</span>, <span class="hljs-number">1e-3</span>),
    clip_threshold=<span class="hljs-number">1.0</span>,
    decay_rate=-<span class="hljs-number">0.8</span>,
    beta1=<span class="hljs-literal">None</span>,
    weight_decay=<span class="hljs-number">0.0</span>,
    relative_step=<span class="hljs-literal">False</span>,
    scale_parameter=<span class="hljs-literal">False</span>,
    warmup_init=<span class="hljs-literal">False</span>,
)`,wrap:!1}}),{c(){c=l("p"),c.textContent=A,x=a(),p(_.$$.fragment)},l(i){c=m(i,"P",{"data-svelte-h":!0}),w(c)!=="svelte-5wyjqd"&&(c.textContent=A),x=n(i),d(_.$$.fragment,i)},m(i,T){s(i,c,T),s(i,x,T),h(_,i,T),v=!0},p:ir,i(i){v||(u(_.$$.fragment,i),v=!0)},o(i){f(_.$$.fragment,i),v=!1},d(i){i&&(t(c),t(x)),g(_,i)}}}function Ea(U){let c,A,x,_,v,i,T,Hr="The <code>.optimization</code> module provides:",ft,te,Sr="<li>an optimizer with weight decay fixed that can be used to fine-tuned models, and</li> <li>several schedules in the form of schedule objects that inherit from <code>_LRSchedule</code>:</li> <li>a gradient accumulation class to accumulate the gradients of multiple batches</li>",gt,re,_t,L,ae,mr,Ie,qr=`Implements Adam algorithm with weight decay fix as introduced in <a href="https://arxiv.org/abs/1711.05101" rel="nofollow">Decoupled Weight Decay
Regularization</a>.`,cr,S,ne,pr,Fe,Br="Performs a single optimization step.",wt,oe,bt,$,se,dr,Pe,Xr=`AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code:
<a href="https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py" rel="nofollow">https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py</a>`,hr,Ve,Or=`Paper: <em>Adafactor: Adaptive Learning Rates with Sublinear Memory Cost</em> <a href="https://arxiv.org/abs/1804.04235" rel="nofollow">https://arxiv.org/abs/1804.04235</a> Note that
this optimizer internally adjusts the learning rate depending on the <code>scale_parameter</code>, <code>relative_step</code> and
<code>warmup_init</code> options. To use a manual (external) learning rate schedule you should set <code>scale_parameter=False</code> and
<code>relative_step=False</code>.`,ur,Ge,Yr="This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.",fr,Je,Qr='Recommended T5 finetuning settings (<a href="https://discuss.huggingface.co/t/t5-finetuning-tips/684/3" rel="nofollow">https://discuss.huggingface.co/t/t5-finetuning-tips/684/3</a>):',gr,Ne,Kr='<li><p>Training without LR warmup or clip_threshold is not recommended.</p> <ul><li>use scheduled LR warm-up to fixed LR</li> <li>use clip_threshold=1.0 (<a href="https://arxiv.org/abs/1804.04235" rel="nofollow">https://arxiv.org/abs/1804.04235</a>)</li></ul></li> <li><p>Disable relative updates</p></li> <li><p>Use scale_parameter=False</p></li> <li><p>Additional optimizer operations like gradient clipping should not be used alongside Adafactor</p></li>',_r,q,wr,B,br,Ze,ea='When using <code>lr=None</code> with <a href="/docs/transformers/main/en/main_classes/trainer#transformers.Trainer">Trainer</a> you will most likely need to use <code>AdafactorSchedule</code>',$r,X,vr,O,yr,Y,ie,xr,He,ta="Performs a single optimization step",$t,le,vt,C,me,Tr,Se,ra=`Adam enables L2 weight decay and clip_by_global_norm on gradients. Just adding the square of the weights to the
loss function is <em>not</em> the correct way of using L2 regularization/weight decay with Adam, since that will interact
with the m and v parameters in strange ways as shown in <a href="https://arxiv.org/abs/1711.05101" rel="nofollow">Decoupled Weight Decay
Regularization</a>.`,zr,qe,aa=`Instead we want to decay the weights in a manner that doesn’t interact with the m/v parameters. This is equivalent
to adding the square of the weights to the loss with plain (non-momentum) SGD.`,Mr,Q,ce,Ar,Be,na="Creates an optimizer from its config with WarmUp custom object.",yt,R,pe,Cr,Xe,oa="Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay.",xt,de,Tt,he,zt,E,ue,Lr,Oe,sa="An enumeration.",Mt,k,fe,Wr,Ye,ia="Unified API to get any scheduler from its name.",At,I,ge,jr,Qe,la="Create a schedule with a constant learning rate, using the learning rate set in optimizer.",Ct,F,_e,Dr,Ke,ma=`Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.`,Lt,we,ca,Wt,P,be,Ur,et,pa=`Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.`,jt,$e,da,Dt,V,ve,Rr,tt,ha=`Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.`,Ut,ye,ua,Rt,G,xe,Er,rt,fa=`Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.`,Et,Te,ga,kt,W,ze,kr,at,_a=`Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by <em>lr_end</em>, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.`,Ir,nt,wa=`Note: <em>power</em> defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
<a href="https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37" rel="nofollow">https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37</a>`,It,J,Me,Fr,ot,ba=`Create a schedule with an inverse square-root learning rate, from the initial lr set in the optimizer, after a
warmup period which increases lr linearly from 0 to the initial lr set in the optimizer.`,Ft,j,Ae,Pr,st,$a="Create a schedule with a learning rate that has three stages:",Vr,it,va=`<li>linear increase from 0 to initial lr.</li> <li>constant lr (equal to initial lr).</li> <li>decrease following the values of the cosine function between the initial lr set in the optimizer to
a fraction of initial lr.</li>`,Pt,Ce,Vt,N,Le,Gr,lt,ya="Applies a warmup schedule on a given learning rate decay schedule.",Gt,We,Jt,je,Nt,D,De,Jr,mt,xa=`Gradient accumulation utility. When used with a distribution strategy, the accumulator should be called in a
replica context. Gradients will be accumulated locally on each replica and without synchronization. Users should
then call <code>.gradients</code>, scale the gradients if required, and pass the result to <code>apply_gradients</code>.`,Nr,K,Ue,Zr,ct,Ta="Resets the accumulated gradients on the current replica.",Zt,Re,Ht,ut,St;return v=new H({props:{title:"Optimization",local:"optimization",headingTag:"h1"}}),re=new H({props:{title:"AdamW (PyTorch)",local:"transformers.AdamW",headingTag:"h2"}}),ae=new M({props:{name:"class transformers.AdamW",anchor:"transformers.AdamW",parameters:[{name:"params",val:": Iterable"},{name:"lr",val:": float = 0.001"},{name:"betas",val:": Tuple = (0.9, 0.999)"},{name:"eps",val:": float = 1e-06"},{name:"weight_decay",val:": float = 0.0"},{name:"correct_bias",val:": bool = True"},{name:"no_deprecation_warning",val:": bool = False"}],parametersDescription:[{anchor:"transformers.AdamW.params",description:`<strong>params</strong> (<code>Iterable[nn.parameter.Parameter]</code>) &#x2014;
Iterable of parameters to optimize or dictionaries defining parameter groups.`,name:"params"},{anchor:"transformers.AdamW.lr",description:`<strong>lr</strong> (<code>float</code>, <em>optional</em>, defaults to 0.001) &#x2014;
The learning rate to use.`,name:"lr"},{anchor:"transformers.AdamW.betas",description:`<strong>betas</strong> (<code>Tuple[float,float]</code>, <em>optional</em>, defaults to <code>(0.9, 0.999)</code>) &#x2014;
Adam&#x2019;s betas parameters (b1, b2).`,name:"betas"},{anchor:"transformers.AdamW.eps",description:`<strong>eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-06) &#x2014;
Adam&#x2019;s epsilon for numerical stability.`,name:"eps"},{anchor:"transformers.AdamW.weight_decay",description:`<strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Decoupled weight decay to apply.`,name:"weight_decay"},{anchor:"transformers.AdamW.correct_bias",description:`<strong>correct_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to correct bias in Adam (for instance, in Bert TF repository they use <code>False</code>).`,name:"correct_bias"},{anchor:"transformers.AdamW.no_deprecation_warning",description:`<strong>no_deprecation_warning</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
A flag used to disable the deprecation warning (set to <code>True</code> to disable the warning).`,name:"no_deprecation_warning"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L555"}}),ne=new M({props:{name:"step",anchor:"transformers.AdamW.step",parameters:[{name:"closure",val:": Callable = None"}],parametersDescription:[{anchor:"transformers.AdamW.step.closure",description:"<strong>closure</strong> (<code>Callable</code>, <em>optional</em>) &#x2014; A closure that reevaluates the model and returns the loss.",name:"closure"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L606"}}),oe=new H({props:{title:"AdaFactor (PyTorch)",local:"transformers.Adafactor",headingTag:"h2"}}),se=new M({props:{name:"class transformers.Adafactor",anchor:"transformers.Adafactor",parameters:[{name:"params",val:""},{name:"lr",val:" = None"},{name:"eps",val:" = (1e-30, 0.001)"},{name:"clip_threshold",val:" = 1.0"},{name:"decay_rate",val:" = -0.8"},{name:"beta1",val:" = None"},{name:"weight_decay",val:" = 0.0"},{name:"scale_parameter",val:" = True"},{name:"relative_step",val:" = True"},{name:"warmup_init",val:" = False"}],parametersDescription:[{anchor:"transformers.Adafactor.params",description:`<strong>params</strong> (<code>Iterable[nn.parameter.Parameter]</code>) &#x2014;
Iterable of parameters to optimize or dictionaries defining parameter groups.`,name:"params"},{anchor:"transformers.Adafactor.lr",description:`<strong>lr</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The external learning rate.`,name:"lr"},{anchor:"transformers.Adafactor.eps",description:`<strong>eps</strong> (<code>Tuple[float, float]</code>, <em>optional</em>, defaults to <code>(1e-30, 0.001)</code>) &#x2014;
Regularization constants for square gradient and parameter scale respectively`,name:"eps"},{anchor:"transformers.Adafactor.clip_threshold",description:`<strong>clip_threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Threshold of root mean square of final gradient update`,name:"clip_threshold"},{anchor:"transformers.Adafactor.decay_rate",description:`<strong>decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to -0.8) &#x2014;
Coefficient used to compute running averages of square`,name:"decay_rate"},{anchor:"transformers.Adafactor.beta1",description:`<strong>beta1</strong> (<code>float</code>, <em>optional</em>) &#x2014;
Coefficient used for computing running averages of gradient`,name:"beta1"},{anchor:"transformers.Adafactor.weight_decay",description:`<strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Weight decay (L2 penalty)`,name:"weight_decay"},{anchor:"transformers.Adafactor.scale_parameter",description:`<strong>scale_parameter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If True, learning rate is scaled by root mean square`,name:"scale_parameter"},{anchor:"transformers.Adafactor.relative_step",description:`<strong>relative_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If True, time-dependent learning rate is computed instead of external learning rate`,name:"relative_step"},{anchor:"transformers.Adafactor.warmup_init",description:`<strong>warmup_init</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Time-dependent learning rate computation depends on whether warm-up initialization is being used`,name:"warmup_init"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L669"}}),q=new sr({props:{anchor:"transformers.Adafactor.example",$$slots:{default:[ja]},$$scope:{ctx:U}}}),B=new sr({props:{anchor:"transformers.Adafactor.example-2",$$slots:{default:[Da]},$$scope:{ctx:U}}}),X=new sr({props:{anchor:"transformers.Adafactor.example-3",$$slots:{default:[Ua]},$$scope:{ctx:U}}}),O=new sr({props:{anchor:"transformers.Adafactor.example-4",$$slots:{default:[Ra]},$$scope:{ctx:U}}}),ie=new M({props:{name:"step",anchor:"transformers.Adafactor.step",parameters:[{name:"closure",val:" = None"}],parametersDescription:[{anchor:"transformers.Adafactor.step.closure",description:`<strong>closure</strong> (callable, optional) &#x2014; A closure that reevaluates the model
and returns the loss.`,name:"closure"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L815"}}),le=new H({props:{title:"AdamWeightDecay (TensorFlow)",local:"transformers.AdamWeightDecay",headingTag:"h2"}}),me=new M({props:{name:"class transformers.AdamWeightDecay",anchor:"transformers.AdamWeightDecay",parameters:[{name:"learning_rate",val:": Union = 0.001"},{name:"beta_1",val:": float = 0.9"},{name:"beta_2",val:": float = 0.999"},{name:"epsilon",val:": float = 1e-07"},{name:"amsgrad",val:": bool = False"},{name:"weight_decay_rate",val:": float = 0.0"},{name:"include_in_weight_decay",val:": Optional = None"},{name:"exclude_from_weight_decay",val:": Optional = None"},{name:"name",val:": str = 'AdamWeightDecay'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AdamWeightDecay.learning_rate",description:`<strong>learning_rate</strong> (<code>Union[float, LearningRateSchedule]</code>, <em>optional</em>, defaults to 0.001) &#x2014;
The learning rate to use or a schedule.`,name:"learning_rate"},{anchor:"transformers.AdamWeightDecay.beta_1",description:`<strong>beta_1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) &#x2014;
The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates.`,name:"beta_1"},{anchor:"transformers.AdamWeightDecay.beta_2",description:`<strong>beta_2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) &#x2014;
The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates.`,name:"beta_2"},{anchor:"transformers.AdamWeightDecay.epsilon",description:`<strong>epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-07) &#x2014;
The epsilon parameter in Adam, which is a small constant for numerical stability.`,name:"epsilon"},{anchor:"transformers.AdamWeightDecay.amsgrad",description:`<strong>amsgrad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to apply AMSGrad variant of this algorithm or not, see <a href="https://arxiv.org/abs/1904.09237" rel="nofollow">On the Convergence of Adam and
Beyond</a>.`,name:"amsgrad"},{anchor:"transformers.AdamWeightDecay.weight_decay_rate",description:`<strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The weight decay to apply.`,name:"weight_decay_rate"},{anchor:"transformers.AdamWeightDecay.include_in_weight_decay",description:`<strong>include_in_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
applied to all parameters by default (unless they are in <code>exclude_from_weight_decay</code>).`,name:"include_in_weight_decay"},{anchor:"transformers.AdamWeightDecay.exclude_from_weight_decay",description:`<strong>exclude_from_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
List of the parameter names (or re patterns) to exclude from applying weight decay to. If a
<code>include_in_weight_decay</code> is passed, the names in it will supersede this list.`,name:"exclude_from_weight_decay"},{anchor:"transformers.AdamWeightDecay.name",description:`<strong>name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;AdamWeightDecay&quot;</code>) &#x2014;
Optional name for the operations created when applying gradients.`,name:"name"},{anchor:"transformers.AdamWeightDecay.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Keyword arguments. Allowed to be {<code>clipnorm</code>, <code>clipvalue</code>, <code>lr</code>, <code>decay</code>}. <code>clipnorm</code> is clip gradients by
norm; <code>clipvalue</code> is clip gradients by value, <code>decay</code> is included for backward compatibility to allow time
inverse decay of learning rate. <code>lr</code> is included for backward compatibility, recommended to use
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weight tensors, as if they were concatenated into a single vector.`,name:"adam_global_clipnorm"},{anchor:"transformers.create_optimizer.weight_decay_rate",description:`<strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
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List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
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The optimizer that will be used during training.`,name:"optimizer"},{anchor:"transformers.get_scheduler.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014;
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The index of the last epoch when resuming training.`,name:"last_epoch"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L40",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),_e=new M({props:{name:"transformers.get_constant_schedule_with_warmup",anchor:"transformers.get_constant_schedule_with_warmup",parameters:[{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": int"},{name:"last_epoch",val:": int = -1"}],parametersDescription:[{anchor:"transformers.get_constant_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>~torch.optim.Optimizer</code>) &#x2014;
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),xe=new M({props:{name:"transformers.get_linear_schedule_with_warmup",anchor:"transformers.get_linear_schedule_with_warmup",parameters:[{name:"optimizer",val:""},{name:"num_warmup_steps",val:""},{name:"num_training_steps",val:""},{name:"last_epoch",val:" = -1"}],parametersDescription:[{anchor:"transformers.get_linear_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>~torch.optim.Optimizer</code>) &#x2014;
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),ze=new M({props:{name:"transformers.get_polynomial_decay_schedule_with_warmup",anchor:"transformers.get_polynomial_decay_schedule_with_warmup",parameters:[{name:"optimizer",val:""},{name:"num_warmup_steps",val:""},{name:"num_training_steps",val:""},{name:"lr_end",val:" = 1e-07"},{name:"power",val:" = 1.0"},{name:"last_epoch",val:" = -1"}],parametersDescription:[{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>~torch.optim.Optimizer</code>) &#x2014;
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Power factor.`,name:"power"},{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The index of the last epoch when resuming training.`,name:"last_epoch"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L243",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),Me=new M({props:{name:"transformers.get_inverse_sqrt_schedule",anchor:"transformers.get_inverse_sqrt_schedule",parameters:[{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": int"},{name:"timescale",val:": int = None"},{name:"last_epoch",val:": int = -1"}],parametersDescription:[{anchor:"transformers.get_inverse_sqrt_schedule.optimizer",description:`<strong>optimizer</strong> (<code>~torch.optim.Optimizer</code>) &#x2014;
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Time scale.`,name:"timescale"},{anchor:"transformers.get_inverse_sqrt_schedule.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),Ae=new M({props:{name:"transformers.get_wsd_schedule",anchor:"transformers.get_wsd_schedule",parameters:[{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": int"},{name:"num_stable_steps",val:": int"},{name:"num_decay_steps",val:": int"},{name:"min_lr_ratio",val:": float = 0"},{name:"num_cycles",val:": float = 0.5"},{name:"last_epoch",val:": int = -1"}],parametersDescription:[{anchor:"transformers.get_wsd_schedule.optimizer",description:`<strong>optimizer</strong> (<code>~torch.optim.Optimizer</code>) &#x2014;
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The number of steps for the warmup phase.`,name:"num_warmup_steps"},{anchor:"transformers.get_wsd_schedule.num_stable_steps",description:`<strong>num_stable_steps</strong> (<code>int</code>) &#x2014;
The number of steps for the stable phase.`,name:"num_stable_steps"},{anchor:"transformers.get_wsd_schedule.num_decay_steps",description:`<strong>num_decay_steps</strong> (<code>int</code>) &#x2014;
The number of steps for the cosine annealing phase.`,name:"num_decay_steps"},{anchor:"transformers.get_wsd_schedule.min_lr_ratio",description:`<strong>min_lr_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The minimum learning rate as a ratio of the initial learning rate.`,name:"min_lr_ratio"},{anchor:"transformers.get_wsd_schedule.num_cycles",description:`<strong>num_cycles</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) &#x2014;
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).`,name:"num_cycles"},{anchor:"transformers.get_wsd_schedule.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) &#x2014;
The index of the last epoch when resuming training.`,name:"last_epoch"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/optimization.py#L410",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
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The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end
of the warmup).`,name:"initial_learning_rate"},{anchor:"transformers.WarmUp.decay_schedule_fn",description:`<strong>decay_schedule_fn</strong> (<code>Callable</code>) &#x2014;
The schedule function to apply after the warmup for the rest of training.`,name:"decay_schedule_fn"},{anchor:"transformers.WarmUp.warmup_steps",description:`<strong>warmup_steps</strong> (<code>int</code>) &#x2014;
The number of steps for the warmup part of training.`,name:"warmup_steps"},{anchor:"transformers.WarmUp.power",description:`<strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
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