import{s as At,o as Kt,n as Ot}from"../chunks/scheduler.8a2cc2fa.js";import{S as te,i as ee,e as o,s as a,c as p,h as ne,a as l,d as e,b as s,f as N,g as b,j as x,k as F,l as i,m as r,n as c,t as f,o as _,p as h}from"../chunks/index.7079e750.js";import{C as ae,H as lt,E as se}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.ac36d8ed.js";import{D as J}from"../chunks/Docstring.c96e8772.js";import{C as ie}from"../chunks/CodeBlock.90bbfb62.js";import{E as re}from"../chunks/ExampleCodeBlock.c12e279f.js";function oe(mt){let d,z="Example:",M,$,v;return $=new ie({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn

<span class="hljs-keyword">import</span> bitsandbytes <span class="hljs-keyword">as</span> bnb
<span class="hljs-keyword">from</span> bnb.nn <span class="hljs-keyword">import</span> Linear4bit

fp16_model = nn.Sequential(
    nn.Linear(<span class="hljs-number">64</span>, <span class="hljs-number">64</span>),
    nn.Linear(<span class="hljs-number">64</span>, <span class="hljs-number">64</span>)
)

quantized_model = nn.Sequential(
    Linear4bit(<span class="hljs-number">64</span>, <span class="hljs-number">64</span>),
    Linear4bit(<span class="hljs-number">64</span>, <span class="hljs-number">64</span>)
)

quantized_model.load_state_dict(fp16_model.state_dict())
quantized_model = quantized_model.to(<span class="hljs-number">0</span>) <span class="hljs-comment"># Quantization happens here</span>`,wrap:!1}}),{c(){d=o("p"),d.textContent=z,M=a(),p($.$$.fragment)},l(m){d=l(m,"P",{"data-svelte-h":!0}),x(d)!=="svelte-11lpom8"&&(d.textContent=z),M=s(m),b($.$$.fragment,m)},m(m,L){r(m,d,L),r(m,M,L),c($,m,L),v=!0},p:Ot,i(m){v||(f($.$$.fragment,m),v=!0)},o(m){_($.$$.fragment,m),v=!1},d(m){m&&(e(d),e(M)),h($,m)}}}function le(mt){let d,z,M,$,v,m,L,dt,I,Rt='<a href="https://hf.co/papers/2305.14314" rel="nofollow">QLoRA</a> is a finetuning method that quantizes a model to 4-bits and adds a set of low-rank adaptation (LoRA) weights to the model and tuning them through the quantized weights. This method also introduces a new data type, 4-bit NormalFloat (<code>LinearNF4</code>) in addition to the standard Float4 data type (<code>LinearFP4</code>). <code>LinearNF4</code> is a quantization data type for normally distributed data and can improve performance.',ut,B,pt,y,k,xt,S,Wt=`This class is the base module for the 4-bit quantization algorithm presented in <a href="https://arxiv.org/abs/2305.14314" rel="nofollow">QLoRA</a>.
QLoRA 4-bit linear layers uses blockwise k-bit quantization under the hood, with the possibility of selecting various
compute datatypes such as FP4 and NF4.`,Mt,H,Qt=`In order to quantize a linear layer one should first load the original fp16 / bf16 weights into
the Linear4bit module, then call <code>quantized_module.to(&quot;cuda&quot;)</code> to quantize the fp16 / bf16 weights.`,Nt,q,Ft,j,E,Ct,A,Dt="Initialize Linear4bit class.",bt,R,ct,T,W,qt,K,Ut="Implements the FP4 data type.",jt,O,Q,ft,D,_t,u,U,Pt,tt,Gt="Implements the NF4 data type.",Jt,et,Xt=`Constructs a quantization data type where each bin has equal area under a standard normal distribution N(0, 1) that
is normalized into the range [-1, 1].`,zt,nt,Vt='For more information read the paper: QLoRA: Efficient Finetuning of Quantized LLMs (<a href="https://arxiv.org/abs/2305.14314" rel="nofollow">https://arxiv.org/abs/2305.14314</a>)',It,at,Yt=`Implementation of the NF4 data type in bitsandbytes can be found in the <code>create_normal_map</code> function in
the <code>functional.py</code> file: <a href="https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236" rel="nofollow">https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236</a>.`,Bt,st,G,ht,X,yt,C,V,kt,P,Y,Et,it,Zt="Initialize self.  See help(type(self)) for accurate signature.",gt,Z,$t,ot,vt;return v=new ae({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),L=new lt({props:{title:"4-bit quantization",local:"4-bit-quantization",headingTag:"h1"}}),B=new lt({props:{title:"Linear4bit",local:"bitsandbytes.nn.Linear4bit",headingTag:"h2"}}),k=new J({props:{name:"class bitsandbytes.nn.Linear4bit",anchor:"bitsandbytes.nn.Linear4bit",parameters:[{name:"input_features",val:""},{name:"output_features",val:""},{name:"bias",val:" = True"},{name:"compute_dtype",val:" = None"},{name:"compress_statistics",val:" = True"},{name:"quant_type",val:" = 'fp4'"},{name:"quant_storage",val:" = torch.uint8"},{name:"device",val:" = None"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/nn/modules.py#L413"}}),q=new re({props:{anchor:"bitsandbytes.nn.Linear4bit.example",$$slots:{default:[oe]},$$scope:{ctx:mt}}}),E=new J({props:{name:"__init__",anchor:"bitsandbytes.nn.Linear4bit.__init__",parameters:[{name:"input_features",val:""},{name:"output_features",val:""},{name:"bias",val:" = True"},{name:"compute_dtype",val:" = None"},{name:"compress_statistics",val:" = True"},{name:"quant_type",val:" = 'fp4'"},{name:"quant_storage",val:" = torch.uint8"},{name:"device",val:" = None"}],parametersDescription:[{anchor:"bitsandbytes.nn.Linear4bit.__init__.input_features",description:`<strong>input_features</strong> (<code>str</code>) &#x2014;
Number of input features of the linear layer.`,name:"input_features"},{anchor:"bitsandbytes.nn.Linear4bit.__init__.output_features",description:`<strong>output_features</strong> (<code>str</code>) &#x2014;
Number of output features of the linear layer.`,name:"output_features"},{anchor:"bitsandbytes.nn.Linear4bit.__init__.bias",description:`<strong>bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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Number of input features of the linear layer.`,name:"input_features"},{anchor:"bitsandbytes.nn.LinearFP4.__init__.output_features",description:`<strong>output_features</strong> (<code>str</code>) &#x2014;
Number of output features of the linear layer.`,name:"output_features"},{anchor:"bitsandbytes.nn.LinearFP4.__init__.bias",description:`<strong>bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether the linear class uses the bias term as well.`,name:"bias"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/nn/modules.py#L540"}}),D=new lt({props:{title:"LinearNF4",local:"bitsandbytes.nn.LinearNF4",headingTag:"h2"}}),U=new J({props:{name:"class bitsandbytes.nn.LinearNF4",anchor:"bitsandbytes.nn.LinearNF4",parameters:[{name:"input_features",val:""},{name:"output_features",val:""},{name:"bias",val:" = True"},{name:"compute_dtype",val:" = None"},{name:"compress_statistics",val:" = True"},{name:"quant_storage",val:" = torch.uint8"},{name:"device",val:" = None"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/nn/modules.py#L571"}}),G=new J({props:{name:"__init__",anchor:"bitsandbytes.nn.LinearNF4.__init__",parameters:[{name:"input_features",val:""},{name:"output_features",val:""},{name:"bias",val:" = True"},{name:"compute_dtype",val:" = None"},{name:"compress_statistics",val:" = True"},{name:"quant_storage",val:" = torch.uint8"},{name:"device",val:" = None"}],parametersDescription:[{anchor:"bitsandbytes.nn.LinearNF4.__init__.input_features",description:`<strong>input_features</strong> (<code>str</code>) &#x2014;
Number of input features of the linear layer.`,name:"input_features"},{anchor:"bitsandbytes.nn.LinearNF4.__init__.output_features",description:`<strong>output_features</strong> (<code>str</code>) &#x2014;
Number of output features of the linear layer.`,name:"output_features"},{anchor:"bitsandbytes.nn.LinearNF4.__init__.bias",description:`<strong>bias</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether the linear class uses the bias term as well.`,name:"bias"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/nn/modules.py#L583"}}),X=new lt({props:{title:"Params4bit",local:"bitsandbytes.nn.Params4bit",headingTag:"h2"}}),V=new J({props:{name:"class bitsandbytes.nn.Params4bit",anchor:"bitsandbytes.nn.Params4bit",parameters:[{name:"data",val:": typing.Optional[torch.Tensor] = None"},{name:"requires_grad",val:" = False"},{name:"quant_state",val:": typing.Optional[bitsandbytes.functional.QuantState] = None"},{name:"blocksize",val:": typing.Optional[int] = None"},{name:"compress_statistics",val:": bool = True"},{name:"quant_type",val:": str = 'fp4'"},{name:"quant_storage",val:": dtype = torch.uint8"},{name:"module",val:": typing.Optional[ForwardRef('Linear4bit')] = None"},{name:"bnb_quantized",val:": bool = False"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/nn/modules.py#L207"}}),Y=new J({props:{name:"__init__",anchor:"bitsandbytes.nn.Params4bit.__init__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}]}}),Z=new 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