import{S as Fi,i as Oi,s as Ri,e as a,k as l,w as g,t as s,M as ji,c as n,d as r,m,a as o,x as _,h as i,b as c,N as Ca,G as t,g as h,y as w,q as v,o as b,B as y,v as qi,L as Oa}from"../../chunks/vendor-hf-doc-builder.js";import{D as L}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ra}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ne}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Fa}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ui(C){let f,T,x,u,z;return u=new Ra({props:{code:"Adafactor(model.parameters(), scale_parameter=False, relative_step=False, warmup_init=False, lr=1e-3)",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>)'}}),{c(){f=a("p"),T=s("Example:"),x=l(),g(u.$$.fragment)},l(p){f=n(p,"P",{});var $=o(f);T=i($,"Example:"),$.forEach(r),x=m(p),_(u.$$.fragment,p)},m(p,$){h(p,f,$),t(f,T),h(p,x,$),w(u,p,$),z=!0},p:Oa,i(p){z||(v(u.$$.fragment,p),z=!0)},o(p){b(u.$$.fragment,p),z=!1},d(p){p&&r(f),p&&r(x),y(u,p)}}}function Gi(C){let f,T,x,u,z;return u=new Ra({props:{code:"Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)",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>)'}}),{c(){f=a("p"),T=s("Others reported the following combination to work well:"),x=l(),g(u.$$.fragment)},l(p){f=n(p,"P",{});var $=o(f);T=i($,"Others reported the following combination to work well:"),$.forEach(r),x=m(p),_(u.$$.fragment,p)},m(p,$){h(p,f,$),t(f,T),h(p,x,$),w(u,p,$),z=!0},p:Oa,i(p){z||(v(u.$$.fragment,p),z=!0)},o(p){b(u.$$.fragment,p),z=!1},d(p){p&&r(f),p&&r(x),y(u,p)}}}function Vi(C){let f,T,x,u,z;return u=new Ra({props:{code:`from transformers.optimization import Adafactor, AdafactorSchedule

optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(..., optimizers=(optimizer, lr_scheduler))`,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))`}}),{c(){f=a("p"),T=s("scheduler as following:"),x=l(),g(u.$$.fragment)},l(p){f=n(p,"P",{});var $=o(f);T=i($,"scheduler as following:"),$.forEach(r),x=m(p),_(u.$$.fragment,p)},m(p,$){h(p,f,$),t(f,T),h(p,x,$),w(u,p,$),z=!0},p:Oa,i(p){z||(v(u.$$.fragment,p),z=!0)},o(p){b(u.$$.fragment,p),z=!1},d(p){p&&r(f),p&&r(x),y(u,p)}}}function Mi(C){let f,T,x,u,z;return u=new Ra({props:{code:`# replace AdamW with Adafactor
optimizer = Adafactor(
    model.parameters(),
    lr=1e-3,
    eps=(1e-30, 1e-3),
    clip_threshold=1.0,
    decay_rate=-0.8,
    beta1=None,
    weight_decay=0.0,
    relative_step=False,
    scale_parameter=False,
    warmup_init=False,
)`,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>,
)`}}),{c(){f=a("p"),T=s("Usage:"),x=l(),g(u.$$.fragment)},l(p){f=n(p,"P",{});var $=o(f);T=i($,"Usage:"),$.forEach(r),x=m(p),_(u.$$.fragment,p)},m(p,$){h(p,f,$),t(f,T),h(p,x,$),w(u,p,$),z=!0},p:Oa,i(p){z||(v(u.$$.fragment,p),z=!0)},o(p){b(u.$$.fragment,p),z=!1},d(p){p&&r(f),p&&r(x),y(u,p)}}}function Hi(C){let f,T,x,u,z,p,$,zt,ja,kr,oe,qa,Et,Ua,Ga,Sr,F,Tt,Va,Ma,ze,Ha,Dt,Ba,Ja,Ka,Lt,Qa,Nr,R,se,Pt,Ee,Xa,Wt,Ya,Ir,k,Te,Za,De,en,Le,tn,rn,an,ie,Pe,nn,kt,on,Cr,j,le,St,We,sn,Nt,ln,Fr,A,ke,mn,_t,cn,Se,pn,dn,D,hn,It,un,fn,Ne,gn,_n,Ct,wn,vn,Ft,bn,yn,Ot,$n,xn,Rt,An,zn,jt,En,Tn,Dn,qt,Ln,Pn,Ie,Wn,Ce,kn,Sn,Nn,S,Fe,Ut,In,Cn,Oe,Gt,Fn,On,Re,Rn,je,jn,qn,Un,Vt,Mt,Gn,Vn,Ht,Bt,Mn,Hn,Jt,Kt,Bn,Jn,me,Kn,ce,Qn,O,Xn,Qt,Yn,Zn,wt,eo,to,Xt,ro,ao,pe,no,de,oo,he,qe,so,Yt,io,Or,q,ue,Zt,Ue,lo,er,mo,Rr,W,Ge,co,U,po,tr,ho,uo,Ve,fo,go,_o,rr,wo,vo,fe,Me,bo,ar,yo,jr,G,He,$o,nr,xo,qr,V,ge,or,Be,Ao,sr,zo,Ur,M,_e,ir,Je,Eo,lr,To,Gr,H,Ke,Do,mr,Lo,Vr,B,Qe,Po,cr,Wo,Mr,J,Xe,ko,pr,So,Hr,K,Ye,No,dr,Io,Br,Ze,fs,Jr,Q,et,Co,hr,Fo,Kr,tt,gs,Qr,X,rt,Oo,ur,Ro,Xr,at,_s,Yr,Y,nt,jo,fr,qo,Zr,ot,ws,ea,N,st,Uo,it,Go,gr,Vo,Mo,Ho,we,Bo,_r,Jo,Ko,lt,Qo,ta,Z,ve,wr,mt,Xo,vr,Yo,ra,ee,ct,Zo,br,es,aa,te,be,yr,pt,ts,$r,rs,na,re,ye,xr,dt,as,Ar,ns,oa,I,ht,os,ae,ss,zr,is,ls,Er,ms,cs,ps,$e,ut,ds,Tr,hs,sa;return p=new ne({}),Ee=new ne({}),Te=new L({props:{name:"class transformers.AdamW",anchor:"transformers.AdamW",parameters:[{name:"params",val:": typing.Iterable[torch.nn.parameter.Parameter]"},{name:"lr",val:": float = 0.001"},{name:"betas",val:": typing.Tuple[float, float] = (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 1e-3) &#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 (0.9, 0.999)) &#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-6) &#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) &#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/v4.25.1/src/transformers/optimization.py#L273"}}),Pe=new L({props:{name:"step",anchor:"transformers.AdamW.step",parameters:[{name:"closure",val:": typing.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/v4.25.1/src/transformers/optimization.py#L324"}}),We=new ne({}),ke=new L({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 (1e-30, 1e-3)) &#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 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) &#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/v4.25.1/src/transformers/optimization.py#L386"}}),me=new Fa({props:{anchor:"transformers.Adafactor.example",$$slots:{default:[Ui]},$$scope:{ctx:C}}}),ce=new Fa({props:{anchor:"transformers.Adafactor.example-2",$$slots:{default:[Gi]},$$scope:{ctx:C}}}),pe=new Fa({props:{anchor:"transformers.Adafactor.example-3",$$slots:{default:[Vi]},$$scope:{ctx:C}}}),de=new Fa({props:{anchor:"transformers.Adafactor.example-4",$$slots:{default:[Mi]},$$scope:{ctx:C}}}),qe=new L({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/v4.25.1/src/transformers/optimization.py#L532"}}),Ue=new ne({}),Ge=new L({props:{name:"class transformers.AdamWeightDecay",anchor:"transformers.AdamWeightDecay",parameters:[{name:"learning_rate",val:": typing.Union[float, keras.optimizers.schedules.learning_rate_schedule.LearningRateSchedule] = 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:": typing.Optional[typing.List[str]] = None"},{name:"exclude_from_weight_decay",val:": typing.Optional[typing.List[str]] = None"},{name:"name",val:": str = 'AdamWeightDecay'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AdamWeightDecay.learning_rate",description:`<strong>learning_rate</strong> (<code>Union[float, tf.keras.optimizers.schedules.LearningRateSchedule]</code>, <em>optional</em>, defaults to 1e-3) &#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-7) &#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>, default 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) &#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 &#x2018;AdamWeightDecay&#x2019;) &#x2014;
Optional name for the operations created when applying gradients.
kwargs &#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
<code>learning_rate</code> instead.`,name:"name"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/optimization_tf.py#L166"}}),Me=new L({props:{name:"from_config",anchor:"transformers.AdamWeightDecay.from_config",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/optimization_tf.py#L223"}}),He=new L({props:{name:"transformers.create_optimizer",anchor:"transformers.create_optimizer",parameters:[{name:"init_lr",val:": float"},{name:"num_train_steps",val:": int"},{name:"num_warmup_steps",val:": int"},{name:"min_lr_ratio",val:": float = 0.0"},{name:"adam_beta1",val:": float = 0.9"},{name:"adam_beta2",val:": float = 0.999"},{name:"adam_epsilon",val:": float = 1e-08"},{name:"adam_clipnorm",val:": typing.Optional[float] = None"},{name:"adam_global_clipnorm",val:": typing.Optional[float] = None"},{name:"weight_decay_rate",val:": float = 0.0"},{name:"power",val:": float = 1.0"},{name:"include_in_weight_decay",val:": typing.Optional[typing.List[str]] = None"}],parametersDescription:[{anchor:"transformers.create_optimizer.init_lr",description:`<strong>init_lr</strong> (<code>float</code>) &#x2014;
The desired learning rate at the end of the warmup phase.`,name:"init_lr"},{anchor:"transformers.create_optimizer.num_train_steps",description:`<strong>num_train_steps</strong> (<code>int</code>) &#x2014;
The total number of training steps.`,name:"num_train_steps"},{anchor:"transformers.create_optimizer.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) &#x2014;
The number of warmup steps.`,name:"num_warmup_steps"},{anchor:"transformers.create_optimizer.min_lr_ratio",description:`<strong>min_lr_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The final learning rate at the end of the linear decay will be <code>init_lr * min_lr_ratio</code>.`,name:"min_lr_ratio"},{anchor:"transformers.create_optimizer.adam_beta1",description:`<strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) &#x2014;
The beta1 to use in Adam.`,name:"adam_beta1"},{anchor:"transformers.create_optimizer.adam_beta2",description:`<strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) &#x2014;
The beta2 to use in Adam.`,name:"adam_beta2"},{anchor:"transformers.create_optimizer.adam_epsilon",description:`<strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) &#x2014;
The epsilon to use in Adam.
adam_clipnorm &#x2014; (<code>float</code>, <em>optional</em>, defaults to <code>None</code>):
If not <code>None</code>, clip the gradient norm for each weight tensor to this value.
adam_global_clipnorm &#x2014; (<code>float</code>, <em>optional</em>, defaults to <code>None</code>)
If not <code>None</code>, clip gradient norm to this value. When using this argument, the norm is computed over all
weight tensors, as if they were concatenated into a single vector.`,name:"adam_epsilon"},{anchor:"transformers.create_optimizer.weight_decay_rate",description:`<strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The weight decay to use.`,name:"weight_decay_rate"},{anchor:"transformers.create_optimizer.power",description:`<strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
The power to use for PolynomialDecay.`,name:"power"},{anchor:"transformers.create_optimizer.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 except bias and layer norm parameters.`,name:"include_in_weight_decay"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/optimization_tf.py#L82"}}),Be=new ne({}),Je=new ne({}),Ke=new L({props:{name:"class transformers.SchedulerType",anchor:"transformers.SchedulerType",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/v4.25.1/src/transformers/trainer_utils.py#L355"}}),Qe=new L({props:{name:"transformers.get_scheduler",anchor:"transformers.get_scheduler",parameters:[{name:"name",val:": typing.Union[str, transformers.trainer_utils.SchedulerType]"},{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": typing.Optional[int] = None"},{name:"num_training_steps",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"transformers.get_scheduler.name",description:`<strong>name</strong> (<code>str</code> or <code>SchedulerType</code>) &#x2014;
The name of the scheduler to use.`,name:"name"},{anchor:"transformers.get_scheduler.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) &#x2014;
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;
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it&#x2019;s unset and the scheduler type requires it.`,name:"num_warmup_steps"},{anchor:"transformers.get_scheduler.num_training_steps",description:`<strong>num_training_steps</strong> (\`int&#x201C;, <em>optional</em>) &#x2014;
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it&#x2019;s unset and the scheduler type requires it.`,name:"num_training_steps"}],source:"https://github.com/huggingface/transformers/blob/v4.25.1/src/transformers/optimization.py#L233"}}),Xe=new L({props:{name:"transformers.get_constant_schedule",anchor:"transformers.get_constant_schedule",parameters:[{name:"optimizer",val:": Optimizer"},{name:"last_epoch",val:": int = -1"}],parametersDescription:[{anchor:"transformers.get_constant_schedule.optimizer",description:`<strong>optimizer</strong> (<code>~torch.optim.Optimizer</code>) &#x2014;
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_constant_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/v4.25.1/src/transformers/optimization.py#L34",returnDescription:`
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
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`),jt=n(P,"CODE",{});var Os=o(jt);En=i(Os,"relative_step=False"),Os.forEach(r),Tn=i(P,"."),P.forEach(r),Dn=m(E),qt=n(E,"P",{});var Rs=o(qt);Ln=i(Rs,"This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested."),Rs.forEach(r),Pn=m(E),Ie=n(E,"P",{});var ha=o(Ie);Wn=i(ha,"Recommended T5 finetuning settings ("),Ce=n(ha,"A",{href:!0,rel:!0});var js=o(Ce);kn=i(js,"https://discuss.huggingface.co/t/t5-finetuning-tips/684/3"),js.forEach(r),Sn=i(ha,"):"),ha.forEach(r),Nn=m(E),S=n(E,"UL",{});var xe=o(S);Fe=n(xe,"LI",{});var ua=o(Fe);Ut=n(ua,"P",{});var qs=o(Ut);In=i(qs,"Training without LR warmup or clip_threshold is not recommended."),qs.forEach(r),Cn=m(ua),Oe=n(ua,"UL",{});var fa=o(Oe);Gt=n(fa,"LI",{});var Us=o(Gt);Fn=i(Us,"use scheduled LR warm-up to fixed LR"),Us.forEach(r),On=m(fa),Re=n(fa,"LI",{});var ga=o(Re);Rn=i(ga,"use clip_threshold=1.0 ("),je=n(ga,"A",{href:!0,rel:!0});var Gs=o(je);jn=i(Gs,"https://arxiv.org/abs/1804.04235"),Gs.forEach(r),qn=i(ga,")"),ga.forEach(r),fa.forEach(r),ua.forEach(r),Un=m(xe),Vt=n(xe,"LI",{});var Vs=o(Vt);Mt=n(Vs,"P",{});var Ms=o(Mt);Gn=i(Ms,"Disable relative updates"),Ms.forEach(r),Vs.forEach(r),Vn=m(xe),Ht=n(xe,"LI",{});var Hs=o(Ht);Bt=n(Hs,"P",{});var Bs=o(Bt);Mn=i(Bs,"Use scale_parameter=False"),Bs.forEach(r),Hs.forEach(r),Hn=m(xe),Jt=n(xe,"LI",{});var Js=o(Jt);Kt=n(Js,"P",{});var Ks=o(Kt);Bn=i(Ks,"Additional optimizer operations like gradient clipping should not be used alongside Adafactor"),Ks.forEach(r),Js.forEach(r),xe.forEach(r),Jn=m(E),_(me.$$.fragment,E),Kn=m(E),_(ce.$$.fragment,E),Qn=m(E),O=n(E,"P",{});var gt=o(O);Xn=i(gt,"When using "),Qt=n(gt,"CODE",{});var Qs=o(Qt);Yn=i(Qs,"lr=None"),Qs.forEach(r),Zn=i(gt," with "),wt=n(gt,"A",{href:!0});var Xs=o(wt);eo=i(Xs,"Trainer"),Xs.forEach(r),to=i(gt," you will most likely need to use "),Xt=n(gt,"CODE",{});var Ys=o(Xt);ro=i(Ys,"AdafactorSchedule"),Ys.forEach(r),gt.forEach(r),ao=m(E),_(pe.$$.fragment,E),no=m(E),_(de.$$.fragment,E),oo=m(E),he=n(E,"DIV",{class:!0});var _a=o(he);_(qe.$$.fragment,_a),so=m(_a),Yt=n(_a,"P",{});var Zs=o(Yt);io=i(Zs,"Performs a single optimization step"),Zs.forEach(r),_a.forEach(r),E.forEach(r),Or=m(e),q=n(e,"H2",{class:!0});var wa=o(q);ue=n(wa,"A",{id:!0,class:!0,href:!0});var ei=o(ue);Zt=n(ei,"SPAN",{});var ti=o(Zt);_(Ue.$$.fragment,ti),ti.forEach(r),ei.forEach(r),lo=m(wa),er=n(wa,"SPAN",{});var ri=o(er);mo=i(ri,"AdamWeightDecay (TensorFlow)"),ri.forEach(r),wa.forEach(r),Rr=m(e),W=n(e,"DIV",{class:!0});var Ae=o(W);_(Ge.$$.fragment,Ae),co=m(Ae),U=n(Ae,"P",{});var yt=o(U);po=i(yt,`Adam enables L2 weight decay and clip_by_global_norm on gradients. Just adding the square of the weights to the
loss function is `),tr=n(yt,"EM",{});var ai=o(tr);ho=i(ai,"not"),ai.forEach(r),uo=i(yt,` the correct way of using L2 regularization/weight decay with Adam, since that will interact
with the m and v parameters in strange ways as shown in `),Ve=n(yt,"A",{href:!0,rel:!0});var ni=o(Ve);fo=i(ni,`Decoupled Weight Decay
Regularization`),ni.forEach(r),go=i(yt,"."),yt.forEach(r),_o=m(Ae),rr=n(Ae,"P",{});var oi=o(rr);wo=i(oi,`Instead we want ot decay the weights in a manner that doesn\u2019t interact with the m/v parameters. This is equivalent
to adding the square of the weights to the loss with plain (non-momentum) SGD.`),oi.forEach(r),vo=m(Ae),fe=n(Ae,"DIV",{class:!0});var va=o(fe);_(Me.$$.fragment,va),bo=m(va),ar=n(va,"P",{});var si=o(ar);yo=i(si,"Creates an optimizer from its config with WarmUp custom object."),si.forEach(r),va.forEach(r),Ae.forEach(r),jr=m(e),G=n(e,"DIV",{class:!0});var ba=o(G);_(He.$$.fragment,ba),$o=m(ba),nr=n(ba,"P",{});var ii=o(nr);xo=i(ii,"Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay."),ii.forEach(r),ba.forEach(r),qr=m(e),V=n(e,"H2",{class:!0});var ya=o(V);ge=n(ya,"A",{id:!0,class:!0,href:!0});var li=o(ge);or=n(li,"SPAN",{});var mi=o(or);_(Be.$$.fragment,mi),mi.forEach(r),li.forEach(r),Ao=m(ya),sr=n(ya,"SPAN",{});var ci=o(sr);zo=i(ci,"Schedules"),ci.forEach(r),ya.forEach(r),Ur=m(e),M=n(e,"H3",{class:!0});var $a=o(M);_e=n($a,"A",{id:!0,class:!0,href:!0});var pi=o(_e);ir=n(pi,"SPAN",{});var di=o(ir);_(Je.$$.fragment,di),di.forEach(r),pi.forEach(r),Eo=m($a),lr=n($a,"SPAN",{});var hi=o(lr);To=i(hi,"Learning Rate Schedules (Pytorch)"),hi.forEach(r),$a.forEach(r),Gr=m(e),H=n(e,"DIV",{class:!0});var xa=o(H);_(Ke.$$.fragment,xa),Do=m(xa),mr=n(xa,"P",{});var ui=o(mr);Lo=i(ui,"An enumeration."),ui.forEach(r),xa.forEach(r),Vr=m(e),B=n(e,"DIV",{class:!0});var Aa=o(B);_(Qe.$$.fragment,Aa),Po=m(Aa),cr=n(Aa,"P",{});var fi=o(cr);Wo=i(fi,"Unified API to get any scheduler from its name."),fi.forEach(r),Aa.forEach(r),Mr=m(e),J=n(e,"DIV",{class:!0});var za=o(J);_(Xe.$$.fragment,za),ko=m(za),pr=n(za,"P",{});var gi=o(pr);So=i(gi,"Create a schedule with a constant learning rate, using the learning rate set in optimizer."),gi.forEach(r),za.forEach(r),Hr=m(e),K=n(e,"DIV",{class:!0});var Ea=o(K);_(Ye.$$.fragment,Ea),No=m(Ea),dr=n(Ea,"P",{});var _i=o(dr);Io=i(_i,`Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.`),_i.forEach(r),Ea.forEach(r),Br=m(e),Ze=n(e,"IMG",{alt:!0,src:!0}),Jr=m(e),Q=n(e,"DIV",{class:!0});var Ta=o(Q);_(et.$$.fragment,Ta),Co=m(Ta),hr=n(Ta,"P",{});var wi=o(hr);Fo=i(wi,`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.`),wi.forEach(r),Ta.forEach(r),Kr=m(e),tt=n(e,"IMG",{alt:!0,src:!0}),Qr=m(e),X=n(e,"DIV",{class:!0});var Da=o(X);_(rt.$$.fragment,Da),Oo=m(Da),ur=n(Da,"P",{});var vi=o(ur);Ro=i(vi,`Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.`),vi.forEach(r),Da.forEach(r),Xr=m(e),at=n(e,"IMG",{alt:!0,src:!0}),Yr=m(e),Y=n(e,"DIV",{class:!0});var La=o(Y);_(nt.$$.fragment,La),jo=m(La),fr=n(La,"P",{});var bi=o(fr);qo=i(bi,`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.`),bi.forEach(r),La.forEach(r),Zr=m(e),ot=n(e,"IMG",{alt:!0,src:!0}),ea=m(e),N=n(e,"DIV",{class:!0});var $t=o(N);_(st.$$.fragment,$t),Uo=m($t),it=n($t,"P",{});var Pa=o(it);Go=i(Pa,`Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by `),gr=n(Pa,"EM",{});var yi=o(gr);Vo=i(yi,"lr_end"),yi.forEach(r),Mo=i(Pa,`, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.`),Pa.forEach(r),Ho=m($t),we=n($t,"P",{});var Wr=o(we);Bo=i(Wr,"Note: "),_r=n(Wr,"EM",{});var $i=o(_r);Jo=i($i,"power"),$i.forEach(r),Ko=i(Wr,` defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
`),lt=n(Wr,"A",{href:!0,rel:!0});var xi=o(lt);Qo=i(xi,"https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37"),xi.forEach(r),Wr.forEach(r),$t.forEach(r),ta=m(e),Z=n(e,"H3",{class:!0});var Wa=o(Z);ve=n(Wa,"A",{id:!0,class:!0,href:!0});var Ai=o(ve);wr=n(Ai,"SPAN",{});var zi=o(wr);_(mt.$$.fragment,zi),zi.forEach(r),Ai.forEach(r),Xo=m(Wa),vr=n(Wa,"SPAN",{});var Ei=o(vr);Yo=i(Ei,"Warmup (TensorFlow)"),Ei.forEach(r),Wa.forEach(r),ra=m(e),ee=n(e,"DIV",{class:!0});var ka=o(ee);_(ct.$$.fragment,ka),Zo=m(ka),br=n(ka,"P",{});var Ti=o(br);es=i(Ti,"Applies a warmup schedule on a given learning rate decay schedule."),Ti.forEach(r),ka.forEach(r),aa=m(e),te=n(e,"H2",{class:!0});var Sa=o(te);be=n(Sa,"A",{id:!0,class:!0,href:!0});var Di=o(be);yr=n(Di,"SPAN",{});var Li=o(yr);_(pt.$$.fragment,Li),Li.forEach(r),Di.forEach(r),ts=m(Sa),$r=n(Sa,"SPAN",{});var Pi=o($r);rs=i(Pi,"Gradient Strategies"),Pi.forEach(r),Sa.forEach(r),na=m(e),re=n(e,"H3",{class:!0});var Na=o(re);ye=n(Na,"A",{id:!0,class:!0,href:!0});var Wi=o(ye);xr=n(Wi,"SPAN",{});var ki=o(xr);_(dt.$$.fragment,ki),ki.forEach(r),Wi.forEach(r),as=m(Na),Ar=n(Na,"SPAN",{});var Si=o(Ar);ns=i(Si,"GradientAccumulator (TensorFlow)"),Si.forEach(r),Na.forEach(r),oa=m(e),I=n(e,"DIV",{class:!0});var xt=o(I);_(ht.$$.fragment,xt),os=m(xt),ae=n(xt,"P",{});var At=o(ae);ss=i(At,`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 `),zr=n(At,"CODE",{});var Ni=o(zr);is=i(Ni,".gradients"),Ni.forEach(r),ls=i(At,", scale the gradients if required, and pass the result to "),Er=n(At,"CODE",{});var Ii=o(Er);ms=i(Ii,"apply_gradients"),Ii.forEach(r),cs=i(At,"."),At.forEach(r),ps=m(xt),$e=n(xt,"DIV",{class:!0});var Ia=o($e);_(ut.$$.fragment,Ia),ds=m(Ia),Tr=n(Ia,"P",{});var Ci=o(Tr);hs=i(Ci,"Resets the accumulated gradients on the current replica."),Ci.forEach(r),Ia.forEach(r),xt.forEach(r),this.h()},h(){c(f,"name","hf:doc:metadata"),c(f,"content",JSON.stringify(Bi)),c(u,"id","optimization"),c(u,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(u,"href","#optimization"),c(x,"class","relative group"),c(se,"id","transformers.AdamW"),c(se,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 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