bitsandbytes/pr_1532/en/_app/immutable/nodes/15.3fd2f0fd.js

import{s as Un,o as Wn}from"../chunks/scheduler.852ec091.js";import{S as jn,i as Gn,g as r,s as o,r as l,A as Jn,h as d,f as e,c as a,j as h,u,x as c,k as g,y as i,a as s,v as p,d as m,t as b,w as f,m as Kn,n as Xn}from"../chunks/index.28275fd3.js";import{T as Yn}from"../chunks/Tip.9f398c59.js";import{D as y}from"../chunks/Docstring.ee6c313e.js";import{H as Zt,E as Zn}from"../chunks/EditOnGithub.582011f0.js";function to(ee){let _;return{c(){_=Kn("This function is useful for training, but for inference it is advised to use `int8_vectorwise_quant` instead.\nThis implementation performs additional column-wise transposed calculations which are not optimized.")},l(C){_=Xn(C,"This function is useful for training, but for inference it is advised to use `int8_vectorwise_quant` instead.\nThis implementation performs additional column-wise transposed calculations which are not optimized.")},m(C,I){s(C,_,I)},d(C){C&&e(_)}}}function eo(ee){let _,C,I,ne,Q,oe,S,un="The <code>bitsandbytes.functional</code> API provides the low-level building blocks for the library’s features.",ae,F,ie,R,pn="<li>When you need direct control over quantized operations and their parameters.</li> <li>To build custom layers or operations leveraging low-bit arithmetic.</li> <li>To integrate with other ecosystem tooling.</li> <li>For experimental or research purposes requiring non-standard quantization or performance optimizations.</li>",se,B,re,v,U,Me,xt,mn="Determine the quantization statistics for input matrix <code>A</code> in accordance to the <code>LLM.int8()</code> algorithm.",Ve,$t,bn="The statistics are determined both row-wise and column-wise (transposed).",He,Tt,fn='For more information, see the <a href="https://arxiv.org/abs/2208.07339" rel="nofollow">LLM.int8() paper</a>.',Ie,E,de,q,W,Qe,qt,hn="Performs an 8-bit integer matrix multiplication.",Se,wt,gn=`A linear transformation is applied such that <code>out = A @ B.T</code>. When possible, integer tensor core hardware is
utilized to accelerate the operation.`,ce,N,j,Fe,zt,yn="Performs dequantization on the result of a quantized int8 matrix multiplication.",le,P,G,Re,At,vn="Dequantizes a tensor with dtype <code>torch.int8</code> to <code>torch.float32</code>.",ue,w,J,Be,kt,_n="Quantizes a tensor with dtype <code>torch.float16</code> to <code>torch.int8</code> in accordance to the <code>LLM.int8()</code> algorithm.",Ue,Dt,xn='For more information, see the <a href="https://arxiv.org/abs/2208.07339" rel="nofollow">LLM.int8() paper</a>.',pe,K,me,z,X,We,Lt,$n="Dequantizes a packed 4-bit quantized tensor.",je,Ct,Tn=`The input tensor is dequantized by dividing it into blocks of <code>blocksize</code> values.
The the absolute maximum value within these blocks is used for scaling
the non-linear dequantization.`,be,Y,Z,fe,tt,et,he,nt,ot,ge,A,at,Ge,Nt,qn="Quantize tensor A in blocks of 4-bit values.",Je,Pt,wn="Quantizes tensor A by dividing it into blocks which are independently quantized.",ye,it,st,ve,rt,dt,_e,x,ct,Ke,Ot,zn="container for quantization state components to work with Params4bit and similar classes",Xe,M,lt,Ye,Et,An=`returns dict of tensors and strings to use in serialization via _save_to_state_dict()
param: packed — returns dict[str, torch.Tensor] for state_dict fit for safetensors saving`,Ze,T,ut,tn,Mt,kn=`unpacks components of state_dict into QuantState
where necessary, convert into strings, torch.dtype, ints, etc.`,en,Vt,Dn="qs_dict: based on state_dict, with only relevant keys, striped of prefixes.",nn,Ht,Ln="item with key <code>quant_state.bitsandbytes__[nf4/fp4]</code> may contain minor and non-tensor quant state items.",xe,pt,$e,mt,Cn="Primitives used in the 8-bit optimizer quantization.",Te,bt,Nn='For more details see <a href="https://arxiv.org/abs/1511.04561" rel="nofollow">8-Bit Approximations for Parallelism in Deep Learning</a>',qe,k,ft,on,It,Pn="Dequantize a tensor in blocks of values.",an,Qt,On=`The input tensor is dequantized by dividing it into blocks of <code>blocksize</code> values.
The the absolute maximum value within these blocks is used for scaling
the non-linear dequantization.`,we,D,ht,sn,St,En="Quantize a tensor in blocks of values.",rn,Ft,Mn=`The input tensor is quantized by dividing it into blocks of <code>blocksize</code> values.
The the absolute maximum value within these blocks is calculated for scaling
the non-linear quantization.`,ze,gt,Ae,O,yt,dn,Rt,Vn="Gets the memory address of the first element of a tenso",ke,L,vt,cn,Bt,Hn="Verifies that the input tensors are all on the same device.",ln,Ut,In="An input tensor may also be marked as <code>paged</code>, in which case the device placement is ignored.",De,_t,Le,te,Ce;return Q=new Zt({props:{title:"Overview",local:"overview",headingTag:"h1"}}),F=new Zt({props:{title:"When to Use bitsandbytes.functional",local:"when-to-use-bitsandbytesfunctional",headingTag:"h2"}}),B=new Zt({props:{title:"LLM.int8()",local:"bitsandbytes.functional.int8_double_quant",headingTag:"h2"}}),U=new y({props:{name:"bitsandbytes.functional.int8_double_quant",anchor:"bitsandbytes.functional.int8_double_quant",parameters:[{name:"A",val:": Tensor"},{name:"col_stats",val:": typing.Optional[torch.Tensor] = None"},{name:"row_stats",val:": typing.Optional[torch.Tensor] = None"},{name:"out_col",val:": typing.Optional[torch.Tensor] = None"},{name:"out_row",val:": typing.Optional[torch.Tensor] = None"},{name:"threshold",val:" = 0.0"}],parametersDescription:[{anchor:"bitsandbytes.functional.int8_double_quant.A",description:"<strong>A</strong> (<code>torch.Tensor</code> with dtype <code>torch.float16</code>) &#x2014; The input matrix.",name:"A"},{anchor:"bitsandbytes.functional.int8_double_quant.col_stats",description:"<strong>col_stats</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A pre-allocated tensor to hold the column-wise quantization scales.",name:"col_stats"},{anchor:"bitsandbytes.functional.int8_double_quant.row_stats",description:"<strong>row_stats</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A pre-allocated tensor to hold the row-wise quantization scales.",name:"row_stats"},{anchor:"bitsandbytes.functional.int8_double_quant.out_col",description:"<strong>out_col</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A pre-allocated tensor to hold the column-wise quantized data.",name:"out_col"},{anchor:"bitsandbytes.functional.int8_double_quant.out_row",description:"<strong>out_row</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A pre-allocated tensor to hold the row-wise quantized data.",name:"out_row"},{anchor:"bitsandbytes.functional.int8_double_quant.threshold",description:`<strong>threshold</strong> (<code>float</code>, <em>optional</em>) &#x2014;
An optional threshold for sparse decomposition of outlier features.</p>
<p>No outliers are held back when 0.0. Defaults to 0.0.`,name:"threshold"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L2155",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A tuple containing the quantized tensor and relevant statistics.</p>
<ul>
<li><code>torch.Tensor</code> with dtype <code>torch.int8</code>: The row-wise quantized data.</li>
<li><code>torch.Tensor</code> with dtype <code>torch.int8</code>: The column-wise quantized data.</li>
<li><code>torch.Tensor</code> with dtype <code>torch.float32</code>: The row-wise quantization scales.</li>
<li><code>torch.Tensor</code> with dtype <code>torch.float32</code>: The column-wise quantization scales.</li>
<li><code>torch.Tensor</code> with dtype <code>torch.int32</code>, <em>optional</em>: A list of column indices which contain outlier features.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor]]</code></p>
`}}),E=new Yn({props:{$$slots:{default:[to]},$$scope:{ctx:ee}}}),W=new y({props:{name:"bitsandbytes.functional.int8_linear_matmul",anchor:"bitsandbytes.functional.int8_linear_matmul",parameters:[{name:"A",val:": Tensor"},{name:"B",val:": Tensor"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"dtype",val:" = torch.int32"}],parametersDescription:[{anchor:"bitsandbytes.functional.int8_linear_matmul.A",description:"<strong>A</strong> (<code>torch.Tensor</code>) &#x2014; The first matrix operand with the data type <code>torch.int8</code>.",name:"A"},{anchor:"bitsandbytes.functional.int8_linear_matmul.B",description:"<strong>B</strong> (<code>torch.Tensor</code>) &#x2014; The second matrix operand with the data type <code>torch.int8</code>.",name:"B"},{anchor:"bitsandbytes.functional.int8_linear_matmul.out",description:"<strong>out</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A pre-allocated tensor used to store the result.",name:"out"},{anchor:"bitsandbytes.functional.int8_linear_matmul.dtype",description:"<strong>dtype</strong> (<code>torch.dtype</code>, <em>optional</em>) &#x2014; The expected data type of the output. Defaults to <code>torch.int32</code>.",name:"dtype"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1843",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The result of the operation.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>NotImplementedError</code> — The operation is not supported in the current environment.</li>
<li><code>RuntimeError</code> — Raised when the cannot be completed for any other reason.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>NotImplementedError</code> or <code>RuntimeError</code></p>
`}}),j=new y({props:{name:"bitsandbytes.functional.int8_mm_dequant",anchor:"bitsandbytes.functional.int8_mm_dequant",parameters:[{name:"A",val:": Tensor"},{name:"row_stats",val:": Tensor"},{name:"col_stats",val:": Tensor"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"bias",val:": typing.Optional[torch.Tensor] = None"}],parametersDescription:[{anchor:"bitsandbytes.functional.int8_mm_dequant.A",description:"<strong>A</strong> (<code>torch.Tensor</code> with dtype <code>torch.int32</code>) &#x2014; The result of a quantized int8 matrix multiplication.",name:"A"},{anchor:"bitsandbytes.functional.int8_mm_dequant.row_stats",description:"<strong>row_stats</strong> (<code>torch.Tensor</code>) &#x2014; The row-wise quantization statistics for the lhs operand of the matrix multiplication.",name:"row_stats"},{anchor:"bitsandbytes.functional.int8_mm_dequant.col_stats",description:"<strong>col_stats</strong> (<code>torch.Tensor</code>) &#x2014; The column-wise quantization statistics for the rhs operand of the matrix multiplication.",name:"col_stats"},{anchor:"bitsandbytes.functional.int8_mm_dequant.out",description:"<strong>out</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A pre-allocated tensor to store the output of the operation.",name:"out"},{anchor:"bitsandbytes.functional.int8_mm_dequant.bias",description:"<strong>bias</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; An optional bias vector to add to the result.",name:"bias"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1868",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The dequantized result with an optional bias, with dtype <code>torch.float16</code>.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`}}),G=new y({props:{name:"bitsandbytes.functional.int8_vectorwise_dequant",anchor:"bitsandbytes.functional.int8_vectorwise_dequant",parameters:[{name:"A",val:": Tensor"},{name:"stats",val:": Tensor"}],parametersDescription:[{anchor:"bitsandbytes.functional.int8_vectorwise_dequant.A",description:"<strong>A</strong> (<code>torch.Tensor</code> with dtype <code>torch.int8</code>) &#x2014; The quantized int8 tensor.",name:"A"},{anchor:"bitsandbytes.functional.int8_vectorwise_dequant.stats",description:"<strong>stats</strong> (<code>torch.Tensor</code> with dtype <code>torch.float32</code>) &#x2014; The row-wise quantization statistics.",name:"stats"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L2199",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The dequantized tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code> with dtype <code>torch.float32</code></p>
`}}),J=new y({props:{name:"bitsandbytes.functional.int8_vectorwise_quant",anchor:"bitsandbytes.functional.int8_vectorwise_quant",parameters:[{name:"A",val:": Tensor"},{name:"threshold",val:" = 0.0"}],parametersDescription:[{anchor:"bitsandbytes.functional.int8_vectorwise_quant.A",description:"<strong>A</strong> (<code>torch.Tensor</code> with dtype <code>torch.float16</code>) &#x2014; The input tensor.",name:"A"},{anchor:"bitsandbytes.functional.int8_vectorwise_quant.threshold",description:`<strong>threshold</strong> (<code>float</code>, <em>optional</em>) &#x2014;
An optional threshold for sparse decomposition of outlier features.</p>
<p>No outliers are held back when 0.0. Defaults to 0.0.`,name:"threshold"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L2213",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A tuple containing the quantized tensor and relevant statistics.</p>
<ul>
<li><code>torch.Tensor</code> with dtype <code>torch.int8</code>: The quantized data.</li>
<li><code>torch.Tensor</code> with dtype <code>torch.float32</code>: The quantization scales.</li>
<li><code>torch.Tensor</code> with dtype <code>torch.int32</code>, <em>optional</em>: A list of column indices which contain outlier features.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]</code></p>
`}}),K=new Zt({props:{title:"4-bit",local:"bitsandbytes.functional.dequantize_4bit",headingTag:"h2"}}),X=new y({props:{name:"bitsandbytes.functional.dequantize_4bit",anchor:"bitsandbytes.functional.dequantize_4bit",parameters:[{name:"A",val:": Tensor"},{name:"quant_state",val:": typing.Optional[bitsandbytes.utils.QuantState] = None"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:": int = 64"},{name:"quant_type",val:" = 'fp4'"}],parametersDescription:[{anchor:"bitsandbytes.functional.dequantize_4bit.A",description:"<strong>A</strong> (<code>torch.Tensor</code>) &#x2014; The quantized input tensor.",name:"A"},{anchor:"bitsandbytes.functional.dequantize_4bit.quant_state",description:`<strong>quant_state</strong> (<code>QuantState</code>, <em>optional</em>) &#x2014;
The quantization state as returned by <code>quantize_4bit</code>.
Required if <code>absmax</code> is not provided.`,name:"quant_state"},{anchor:"bitsandbytes.functional.dequantize_4bit.absmax",description:`<strong>absmax</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
A tensor containing the scaling values.
Required if <code>quant_state</code> is not provided and ignored otherwise.`,name:"absmax"},{anchor:"bitsandbytes.functional.dequantize_4bit.out",description:"<strong>out</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A tensor to use to store the result.",name:"out"},{anchor:"bitsandbytes.functional.dequantize_4bit.blocksize",description:`<strong>blocksize</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The size of the blocks. Defaults to 64.
Valid values are 64, 128, 256, 512, 1024, 2048, and 4096.`,name:"blocksize"},{anchor:"bitsandbytes.functional.dequantize_4bit.quant_type",description:"<strong>quant_type</strong> (<code>str</code>, <em>optional</em>) &#x2014; The data type to use: <code>nf4</code> or <code>fp4</code>. Defaults to <code>fp4</code>.",name:"quant_type"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1085",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The dequantized tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>torch.Tensor</code></p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> — Raised when the input data type or blocksize is not supported.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),Z=new y({props:{name:"bitsandbytes.functional.dequantize_fp4",anchor:"bitsandbytes.functional.dequantize_fp4",parameters:[{name:"A",val:": Tensor"},{name:"quant_state",val:": typing.Optional[bitsandbytes.utils.QuantState] = None"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:": int = 64"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1065"}}),et=new y({props:{name:"bitsandbytes.functional.dequantize_nf4",anchor:"bitsandbytes.functional.dequantize_nf4",parameters:[{name:"A",val:": Tensor"},{name:"quant_state",val:": typing.Optional[bitsandbytes.utils.QuantState] = None"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:": int = 64"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1075"}}),ot=new y({props:{name:"bitsandbytes.functional.gemv_4bit",anchor:"bitsandbytes.functional.gemv_4bit",parameters:[{name:"A",val:": Tensor"},{name:"B",val:": Tensor"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"transposed_A",val:" = False"},{name:"transposed_B",val:" = False"},{name:"state",val:" = None"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1606"}}),at=new y({props:{name:"bitsandbytes.functional.quantize_4bit",anchor:"bitsandbytes.functional.quantize_4bit",parameters:[{name:"A",val:": Tensor"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:" = None"},{name:"compress_statistics",val:" = False"},{name:"quant_type",val:" = 'fp4'"},{name:"quant_storage",val:" = torch.uint8"}],parametersDescription:[{anchor:"bitsandbytes.functional.quantize_4bit.A",description:"<strong>A</strong> (<code>torch.Tensor</code>) &#x2014; The input tensor. Supports <code>float16</code>, <code>bfloat16</code>, or <code>float32</code> datatypes.",name:"A"},{anchor:"bitsandbytes.functional.quantize_4bit.absmax",description:"<strong>absmax</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A tensor to use to store the absmax values.",name:"absmax"},{anchor:"bitsandbytes.functional.quantize_4bit.out",description:"<strong>out</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014; A tensor to use to store the result.",name:"out"},{anchor:"bitsandbytes.functional.quantize_4bit.blocksize",description:`<strong>blocksize</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The size of the blocks. Defaults to 64.
Valid values are 64, 128, 256, 512, 1024, 2048, and 4096.`,name:"blocksize"},{anchor:"bitsandbytes.functional.quantize_4bit.compress_statistics",description:"<strong>compress_statistics</strong> (<code>bool</code>, <em>optional</em>) &#x2014; Whether to additionally quantize the absmax values. Defaults to False.",name:"compress_statistics"},{anchor:"bitsandbytes.functional.quantize_4bit.quant_type",description:"<strong>quant_type</strong> (<code>str</code>, <em>optional</em>) &#x2014; The data type to use: <code>nf4</code> or <code>fp4</code>. Defaults to <code>fp4</code>.",name:"quant_type"},{anchor:"bitsandbytes.functional.quantize_4bit.quant_storage",description:"<strong>quant_storage</strong> (<code>torch.dtype</code>, <em>optional</em>) &#x2014; The dtype of the tensor used to store the result. Defaults to <code>torch.uint8</code>.",name:"quant_storage"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1021",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A tuple containing the quantization results.</p>
<ul>
<li><code>torch.Tensor</code>: The quantized tensor with packed 4-bit values.</li>
<li><code>QuantState</code>: The state object used to undo the quantization.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple[<code>torch.Tensor</code>, <code>QuantState</code>]</p>
`,raiseDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><code>ValueError</code> — Raised when the input data type is not supported.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ValueError</code></p>
`}}),st=new y({props:{name:"bitsandbytes.functional.quantize_fp4",anchor:"bitsandbytes.functional.quantize_fp4",parameters:[{name:"A",val:": Tensor"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:" = None"},{name:"compress_statistics",val:" = False"},{name:"quant_storage",val:" = torch.uint8"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L991"}}),dt=new y({props:{name:"bitsandbytes.functional.quantize_nf4",anchor:"bitsandbytes.functional.quantize_nf4",parameters:[{name:"A",val:": Tensor"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:" = None"},{name:"compress_statistics",val:" = False"},{name:"quant_storage",val:" = torch.uint8"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/functional.py#L1006"}}),ct=new y({props:{name:"class bitsandbytes.utils.QuantState",anchor:"bitsandbytes.utils.QuantState",parameters:[{name:"absmax",val:""},{name:"shape",val:" = None"},{name:"code",val:" = None"},{name:"blocksize",val:" = None"},{name:"quant_type",val:" = None"},{name:"dtype",val:" = None"},{name:"offset",val:" = None"},{name:"state2",val:" = None"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/utils.py#L259"}}),lt=new y({props:{name:"as_dict",anchor:"bitsandbytes.utils.QuantState.as_dict",parameters:[{name:"packed",val:" = False"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/utils.py#L371"}}),ut=new y({props:{name:"from_dict",anchor:"bitsandbytes.utils.QuantState.from_dict",parameters:[{name:"qs_dict",val:": typing.Dict[str, typing.Any]"},{name:"device",val:": device"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1532/bitsandbytes/utils.py#L320"}}),pt=new Zt({props:{title:"Dynamic 8-bit Quantization",local:"bitsandbytes.functional.dequantize_blockwise",headingTag:"h2"}}),ft=new y({props:{name:"bitsandbytes.functional.dequantize_blockwise",anchor:"bitsandbytes.functional.dequantize_blockwise",parameters:[{name:"A",val:": Tensor"},{name:"quant_state",val:": typing.Optional[bitsandbytes.utils.QuantState] = None"},{name:"absmax",val:": typing.Optional[torch.Tensor] = None"},{name:"code",val:": typing.Optional[torch.Tensor] = None"},{name:"out",val:": typing.Optional[torch.Tensor] = None"},{name:"blocksize",val:": int = 4096"},{name:"nested",val:" = False"}],parametersDescription:[{anchor:"bitsandbytes.functional.dequantize_blockwise.A",description:"<strong>A</strong> (<code>torch.Tensor</code>) &#x2014; The quantized input tensor.",name:"A"},{anchor:"bitsandbytes.functional.dequantize_blockwise.quant_state",description:`<strong>quant_state</strong> (<code>QuantState</code>, <em>optional</em>) &#x2014;
The quantization state as returned by <code>quantize_blockwise</code>.
Required if <code>absmax</code> is not provided.`,name:"quant_state"},{anchor:"bitsandbytes.functional.dequantize_blockwise.absmax",description:`<strong>absmax</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
A tensor containing the scaling values.
Required if <code>quant_state</code> is not provided and ignored otherwise.`,name:"absmax"},{anchor:"bitsandbytes.functional.dequantize_blockwise.code",description:`<strong>code</strong> (<code>torch.Tensor</code>, <em>optional</em>) &#x2014;
A mapping describing the low-bit data type. Defaults to a signed 8-bit dynamic type.
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