bitsandbytes/pr_1255/en/_app/immutable/nodes/23.faee9e43.js

import{s as Xe,o as Le,n as Re}from"../chunks/scheduler.852ec091.js";import{S as He,i as Ae,g as s,s as a,r as d,A as Ne,h as m,f as t,c as o,j as w,u as c,x as k,k as x,y as n,a as r,v as b,d as _,t as g,w as h}from"../chunks/index.28275fd3.js";import{D as E}from"../chunks/Docstring.395987e7.js";import{C as Pe}from"../chunks/CodeBlock.c3366071.js";import{E as Ye}from"../chunks/ExampleCodeBlock.452ced84.js";import{H as re,E as Qe}from"../chunks/EditOnGithub.582011f0.js";function qe(ne){let l,W="Example:",$,u,f;return u=new Pe({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> bitsandbytes <span class="hljs-keyword">as</span> bnb

mng = bnb.optim.GlobalOptimManager.get_instance()

model = MyModel()
mng.register_parameters(model.parameters()) <span class="hljs-comment"># 1. register parameters while still on CPU</span>

model = model.cuda()
<span class="hljs-comment"># use 8-bit optimizer states for all parameters</span>
adam = bnb.optim.Adam(model.parameters(), lr=<span class="hljs-number">0.001</span>, optim_bits=<span class="hljs-number">8</span>)

<span class="hljs-comment"># 2. override: the parameter model.fc1.weight now uses 32-bit Adam</span>
mng.override_config(model.fc1.weight, <span class="hljs-string">&#x27;optim_bits&#x27;</span>, <span class="hljs-number">32</span>)`,wrap:!1}}),{c(){l=s("p"),l.textContent=W,$=a(),d(u.$$.fragment)},l(p){l=m(p,"P",{"data-svelte-h":!0}),k(l)!=="svelte-11lpom8"&&(l.textContent=W),$=o(p),c(u.$$.fragment,p)},m(p,v){r(p,l,v),r(p,$,v),b(u,p,v),f=!0},p:Re,i(p){f||(_(u.$$.fragment,p),f=!0)},o(p){g(u.$$.fragment,p),f=!1},d(p){p&&(t(l),t($)),h(u,p)}}}function Ke(ne){let l,W,$,u,f,p,v,We='<a href="https://hf.co/papers/2110.02861" rel="nofollow">8-bit optimizers</a> reduce the memory footprint of 32-bit optimizers without any performance degradation which means you can train large models with many parameters faster. At the core of 8-bit optimizers is block-wise quantization which enables quantization accuracy, computational efficiency, and stability.',se,U,Ue="bitsandbytes provides 8-bit optimizers through the base <code>Optimizer8bit</code> class, and additionally provides <code>Optimizer2State</code> and <code>Optimizer1State</code> for 2-state (for example, <code>Adam</code>) and 1-state (for example, <code>Adagrad</code>) optimizers respectively. To provide custom optimizer hyperparameters, use the <code>GlobalOptimManager</code> class to configure the optimizer.",me,Z,pe,T,B,xe,S,D,Te,Q,Ze="Base 8-bit optimizer class.",le,F,de,O,I,Oe,C,V,Me,q,Be="Base 2-state update optimizer class.",ce,X,be,M,L,ke,G,R,Se,K,De="Base 1-state update optimizer class.",_e,H,ge,y,A,Ce,ee,Fe="A global optimizer manager for enabling custom optimizer configs.",Ge,z,N,Je,te,Ie="Override initial optimizer config with specific hyperparameters.",je,ie,Ve=`The key-values of the optimizer config for the input parameters are overridden
This can be both, optimizer parameters like <code>betas</code> or <code>lr</code>, or it can be
8-bit specific parameters like <code>optim_bits</code> or <code>percentile_clipping</code>.`,Ee,J,he,P,ue,oe,fe;return f=new re({props:{title:"Overview",local:"overview",headingTag:"h1"}}),Z=new re({props:{title:"Optimizer8bit",local:"bitsandbytes.optim.optimizer.Optimizer8bit",headingTag:"h2"}}),B=new E({props:{name:"class bitsandbytes.optim.optimizer.Optimizer8bit",anchor:"bitsandbytes.optim.optimizer.Optimizer8bit",parameters:[{name:"params",val:""},{name:"defaults",val:""},{name:"optim_bits",val:" = 32"},{name:"is_paged",val:" = False"}],source:"https://github.com/TimDettmers/bitsandbytes/blob/vr_1255/src/bitsandbytes/optim/optimizer.py#L111"}}),D=new E({props:{name:"__init__",anchor:"bitsandbytes.optim.optimizer.Optimizer8bit.__init__",parameters:[{name:"params",val:""},{name:"defaults",val:""},{name:"optim_bits",val:" = 32"},{name:"is_paged",val:" = False"}],parametersDescription:[{anchor:"bitsandbytes.optim.optimizer.Optimizer8bit.__init__.params",description:`<strong>params</strong> (<code>torch.tensor</code>) &#x2014;
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