optimum-intel/pr_1513/en/_app/immutable/nodes/9.b64500a8.js

import{s as It,n as Mt,o as Tt}from"../chunks/scheduler.c90a44b2.js";import{S as zt,i as xt,e as c,s as a,c as r,h as wt,a as u,d as t,b as o,f,g as l,j as pe,k as _,l as g,m as i,n as s,t as m,o as p,p as d}from"../chunks/index.66c3f415.js";import{C as Dt,H as b}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.edbd65e4.js";import{D as v}from"../chunks/Docstring.e74e35ab.js";function qt(st){let C,be,ve,Ne,w,Ce,D,ye,q,Ie,h,F,Ke,de,mt="Handle the Neural Compressor quantization process.",Xe,y,L,Ye,ce,pt="Create the calibration <code>datasets.Dataset</code> to use for the post-training static quantization calibration step.",Ze,I,P,et,ue,dt="Quantize a model given the optimization specifications defined in <code>quantization_config</code>.",Me,k,Te,$,Q,tt,ge,ct="INCTrainer enables Intel Neural Compression quantization aware training, pruning and distillation.",nt,M,S,at,fe,ut="How the distillation loss is computed given the student and teacher outputs.",ot,T,E,it,_e,gt="How the loss is computed by Trainer. By default, all models return the loss in the first element.",rt,z,O,lt,$e,ft=`Will save the model, so you can reload it using <code>from_pretrained()</code>.
Will only save from the main process.`,ze,U,xe,V,H,we,A,De,B,R,qe,W,Fe,j,G,Le,J,Pe,K,X,ke,Y,Qe,Z,ee,Se,te,Ee,ne,ae,Oe,oe,Ue,ie,re,Ve,le,He,se,me,Ae,he,Be;return w=new Dt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),D=new b({props:{title:"Reference",local:"reference",headingTag:"h1"}}),q=new b({props:{title:"INCQuantizer",local:"optimum.intel.INCQuantizer",headingTag:"h2"}}),F=new v({props:{name:"class optimum.intel.INCQuantizer",anchor:"optimum.intel.INCQuantizer",parameters:[{name:"model",val:": typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module]"},{name:"eval_fn",val:": typing.Optional[typing.Callable[[transformers.modeling_utils.PreTrainedModel], int]] = None"},{name:"calibration_fn",val:": typing.Optional[typing.Callable[[transformers.modeling_utils.PreTrainedModel], int]] = None"},{name:"task",val:": typing.Optional[str] = None"},{name:"seed",val:": int = 42"}],source:"https://github.com/huggingface/optimum-intel/blob/vr_1513/optimum/intel/neural_compressor/quantization.py#L78"}}),L=new v({props:{name:"get_calibration_dataset",anchor:"optimum.intel.INCQuantizer.get_calibration_dataset",parameters:[{name:"dataset_name",val:": str"},{name:"num_samples",val:": int = 100"},{name:"dataset_config_name",val:": typing.Optional[str] = None"},{name:"dataset_split",val:": str = 'train'"},{name:"preprocess_function",val:": typing.Optional[typing.Callable] = None"},{name:"preprocess_batch",val:": bool = True"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"token",val:": typing.Union[bool, str, NoneType] = None"}],parametersDescription:[{anchor:"optimum.intel.INCQuantizer.get_calibration_dataset.dataset_name",description:`<strong>dataset_name</strong> (<code>str</code>) &#x2014;
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<p>The calibration <code>datasets.Dataset</code> to use for the post-training static quantization calibration step.</p>
`}}),P=new v({props:{name:"quantize",anchor:"optimum.intel.INCQuantizer.quantize",parameters:[{name:"quantization_config",val:": ForwardRef('PostTrainingQuantConfig')"},{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"calibration_dataset",val:": Dataset = None"},{name:"batch_size",val:": int = 8"},{name:"data_collator",val:": typing.Optional[transformers.data.data_collator.DataCollator] = None"},{name:"remove_unused_columns",val:": bool = True"},{name:"file_name",val:": str = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"optimum.intel.INCQuantizer.quantize.quantization_config",description:`<strong>quantization_config</strong> (<code>Union[PostTrainingQuantConfig]</code>) &#x2014;
The configuration containing the parameters related to quantization.`,name:"quantization_config"},{anchor:"optimum.intel.INCQuantizer.quantize.save_directory",description:`<strong>save_directory</strong> (<code>Union[str, Path]</code>) &#x2014;
The directory where the quantized model should be saved.`,name:"save_directory"},{anchor:"optimum.intel.INCQuantizer.quantize.calibration_dataset",description:`<strong>calibration_dataset</strong> (<code>datasets.Dataset</code>, defaults to <code>None</code>) &#x2014;
The dataset to use for the calibration step, needed for post-training static quantization.`,name:"calibration_dataset"},{anchor:"optimum.intel.INCQuantizer.quantize.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, defaults to 8) &#x2014;
The number of calibration samples to load per batch.`,name:"batch_size"},{anchor:"optimum.intel.INCQuantizer.quantize.data_collator",description:`<strong>data_collator</strong> (<code>DataCollator</code>, defaults to <code>None</code>) &#x2014;
The function to use to form a batch from a list of elements of the calibration dataset.`,name:"data_collator"},{anchor:"optimum.intel.INCQuantizer.quantize.remove_unused_columns",description:`<strong>remove_unused_columns</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether or not to remove the columns unused by the model forward method.`,name:"remove_unused_columns"}],source:"https://github.com/huggingface/optimum-intel/blob/vr_1513/optimum/intel/neural_compressor/quantization.py#L120"}}),k=new b({props:{title:"INCTrainer",local:"optimum.intel.INCTrainer",headingTag:"h2"}}),Q=new v({props:{name:"class optimum.intel.INCTrainer",anchor:"optimum.intel.INCTrainer",parameters:[{name:"model",val:": typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] = None"},{name:"args",val:": TrainingArguments = None"},{name:"data_collator",val:": typing.Optional[transformers.data.data_collator.DataCollator] = None"},{name:"train_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"eval_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"processing_class",val:": typing.Union[transformers.tokenization_utils_base.PreTrainedTokenizerBase, 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