bitsandbytes/pr_1908/en/_app/immutable/nodes/24.61a9fb7d.js

import{s as Rt,o as At,n as Ht}from"../chunks/scheduler.8a2cc2fa.js";import{S as Dt,i as Pt,e as s,s as a,c as d,h as Yt,a as m,d as e,b as o,f as x,g as b,j as S,k as w,l as r,m as n,n as c,t as _,o as g,p as h}from"../chunks/index.7079e750.js";import{C as Qt,H as rt,E as Kt}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.2b7ce466.js";import{D as j}from"../chunks/Docstring.8c9a5003.js";import{C as qt}from"../chunks/CodeBlock.a326412a.js";import{E as te}from"../chunks/ExampleCodeBlock.7664d7e9.js";function ee(st){let l,W="Example:",$,f,u;return f=new qt({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(f.$$.fragment)},l(p){l=m(p,"P",{"data-svelte-h":!0}),S(l)!=="svelte-11lpom8"&&(l.textContent=W),$=o(p),b(f.$$.fragment,p)},m(p,v){n(p,l,v),n(p,$,v),c(f,p,v),u=!0},p:Ht,i(p){u||(_(f.$$.fragment,p),u=!0)},o(p){g(f.$$.fragment,p),u=!1},d(p){p&&(e(l),e($)),h(f,p)}}}function ie(st){let l,W,$,f,u,p,v,mt,U,Zt='<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.',pt,Z,Bt="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.",lt,B,dt,M,F,Ot,k,I,Tt,K,Ft="Base 8-bit optimizer class.",bt,N,ct,O,L,St,C,V,kt,q,It="Base 2-state update optimizer class.",_t,X,gt,T,R,Ct,G,A,Gt,tt,Nt="Base 1-state update optimizer class.",ht,H,ft,y,D,Jt,et,Lt="A global optimizer manager for enabling custom optimizer configs.",Et,z,P,jt,it,Vt="Override initial optimizer config with specific hyperparameters.",Wt,at,Xt=`The key-values of the optimizer config for the input parameters are overridden
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