import{s as ea,n as ta,o as ra}from"../chunks/scheduler.56725da7.js";import{S as na,i as aa,e as a,s as r,c as u,h as oa,a as o,d as l,b as n,f,g as p,j as m,k as v,l as e,m as T,n as d,t as c,o as g,p as _}from"../chunks/index.18a26576.js";import{C as sa}from"../chunks/CopyLLMTxtMenu.f4001e5e.js";import{D as h}from"../chunks/Docstring.185d8bd7.js";import{H as gn}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d7e7b6b3.js";function ia(_n){let D,Nt,Tt,Ct,ee,kt,te,wt,re,fn="Training classes for AWS Trainium accelerators.",Lt,ne,zt,$,ae,rr,x,oe,nr,He,vn=`Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.`,ar,Ae,hn=`For the main process the log level defaults to the logging level set (<code>logging.WARNING</code> if you didn’t do
anything) unless overridden by <code>log_level</code> argument.`,or,Ve,bn=`For the replica processes the log level defaults to <code>logging.WARNING</code> unless overridden by <code>log_level_replica</code>
argument.`,sr,Se,$n="The choice between the main and replica process settings is made according to the return value of <code>should_log</code>.",ir,P,se,lr,Fe,xn="Get number of steps used for a linear warmup.",mr,I,ie,ur,Re,Tn=`Serializes this instance while replace <code>Enum</code> by their values (for JSON serialization support). It obfuscates
the token values by removing their value.`,pr,M,le,dr,je,yn="Serializes this instance to a JSON string.",cr,E,me,gr,qe,Nn="Sanitized serialization to use with TensorBoard’s hparams",Dt,ue,Pt,s,pe,_r,H,de,fr,Oe,Cn="Add a callback to the current list of <code>TrainerCallback</code>.",vr,A,ce,hr,We,kn=`A helper wrapper that creates an appropriate context manager for <code>autocast</code> while feeding it the desired
arguments, depending on the situation.`,br,V,ge,$r,Be,wn="Creates NeuronAccelerator instance and prepares model for distributed training.",xr,C,_e,Tr,Ge,Ln="Setup the optimizer.",yr,Ue,zn=`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
NeuronTrainer’s init through <code>optimizers</code>, or subclass and override this method in a subclass.`,Nr,k,fe,Cr,Je,Dn="Setup the optimizer and the learning rate scheduler.",kr,Ye,Pn=`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
NeuronTrainer’s init through <code>optimizers</code>, or subclass and override this method (or <code>create_optimizer</code> and/or
<code>create_scheduler</code>) in a subclass.`,wr,S,ve,Lr,Ke,In=`Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.`,zr,y,he,Dr,Qe,Mn="Get all parameter names that weight decay will be applied to.",Pr,Xe,En="This function filters out parameters in two ways:",Ir,Ze,Hn="<li>By layer type (instances of layers specified in ALL_LAYERNORM_LAYERS)</li> <li>By parameter name patterns (containing ‘bias’, ‘layernorm’, or ‘rmsnorm’)</li>",Mr,F,be,Er,et,An="Returns the learning rate of each parameter from self.optimizer.",Hr,R,$e,Ar,tt,Vn="Get the number of trainable parameters.",Vr,j,xe,Sr,rt,Sn="Returns the optimizer class and optimizer parameters based on the training arguments.",Fr,q,Te,Rr,nt,Fn="Returns optimizer group for a parameter if given, else returns all optimizer groups for params.",jr,O,ye,qr,at,Rn="Returns the training DataLoader with appropriate sampler and batch size.",Or,W,Ne,Wr,ot,jn=`Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.`,Br,B,Ce,Gr,st,qn=`Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).`,Ur,G,ke,Jr,it,On="Log training metrics to the state history and callbacks.",Yr,U,we,Kr,lt,Wn="Log training step metrics if logging is due.",Qr,J,Le,Xr,mt,Bn="Save checkpoint if saving is due.",Zr,Y,ze,en,ut,Gn=`Helper to get number of samples in a <code>~torch.utils.data.DataLoader</code> by accessing its dataset. When
dataloader.dataset does not exist or has no length, estimates as best it can`,tn,K,De,rn,pt,Un="Helper to get number of tokens in a <code>~torch.utils.data.DataLoader</code> by enumerating dataloader.",nn,w,Pe,an,dt,Jn="Remove a callback from the current list of <code>TrainerCallback</code> and returns it.",on,ct,Yn="If the callback is not found, returns <code>None</code> (and no error is raised).",sn,Q,Ie,ln,gt,Kn="Remove a callback from the current list of <code>TrainerCallback</code>.",mn,L,Me,un,_t,Qn="Calculates and returns the following values:",pn,ft,Xn="<li><code>num_train_epochs</code></li> <li><code>num_update_steps_per_epoch</code></li> <li><code>num_examples</code></li> <li><code>num_train_samples</code></li> <li><code>epoch_based</code></li> <li><code>len_dataloader</code></li> <li><code>max_steps</code></li>",dn,X,Ee,cn,vt,Zn=`Setup everything to prepare for the training loop.
This methods does not return anything but initializes many attributes of the class for training.`,It,yt,Mt;return ee=new sa({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),te=new gn({props:{title:"NeuronTrainer",local:"neurontrainer",headingTag:"h1"}}),ne=new gn({props:{title:"NeuronTrainingArguments",local:"optimum.neuron.NeuronTrainingArguments",headingTag:"h2"}}),ae=new h({props:{name:"class optimum.neuron.NeuronTrainingArguments",anchor:"optimum.neuron.NeuronTrainingArguments",parameters:[{name:"output_dir",val:": str | None = None"},{name:"overwrite_output_dir",val:": bool = False"},{name:"do_train",val:": bool = False"},{name:"do_eval",val:": bool = False"},{name:"eval_strategy",val:": transformers.trainer_utils.IntervalStrategy | str = 'no'"},{name:"per_device_train_batch_size",val:": int = 1"},{name:"per_device_eval_batch_size",val:": int = 1"},{name:"gradient_accumulation_steps",val:": int = 1"},{name:"learning_rate",val:": float = 5e-05"},{name:"weight_decay",val:": float = 0.0"},{name:"adam_beta1",val:": float = 0.9"},{name:"adam_beta2",val:": float = 0.999"},{name:"adam_epsilon",val:": float = 1e-08"},{name:"max_grad_norm",val:": float = 1.0"},{name:"num_train_epochs",val:": float = 3.0"},{name:"max_steps",val:": int = -1"},{name:"lr_scheduler_type",val:": transformers.trainer_utils.SchedulerType | str = 'linear'"},{name:"lr_scheduler_kwargs",val:": dict[str, typing.Any] | str | None = <factory>"},{name:"warmup_ratio",val:": float = 0.0"},{name:"warmup_steps",val:": int = 0"},{name:"log_level",val:": str = 'info'"},{name:"log_level_replica",val:": str = 'silent'"},{name:"logging_dir",val:": str | None = None"},{name:"logging_strategy",val:": transformers.trainer_utils.IntervalStrategy | str = 'steps'"},{name:"logging_first_step",val:": bool = False"},{name:"logging_steps",val:": float = 500"},{name:"save_strategy",val:": transformers.trainer_utils.SaveStrategy | str = 'steps'"},{name:"save_steps",val:": float = 500"},{name:"save_total_limit",val:": int | None = None"},{name:"save_only_model",val:": bool = False"},{name:"restore_callback_states_from_checkpoint",val:": bool = False"},{name:"seed",val:": int = 42"},{name:"bf16",val:": bool = False"},{name:"dataloader_drop_last",val:": bool = False"},{name:"eval_steps",val:": float | None = None"},{name:"dataloader_num_workers",val:": int = 0"},{name:"dataloader_prefetch_factor",val:": int | None = None"},{name:"run_name",val:": str | None = None"},{name:"disable_tqdm",val:": bool | None = None"},{name:"remove_unused_columns",val:": bool | None = True"},{name:"label_names",val:": list[str] | None = None"},{name:"accelerator_config",val:": dict | str | None = None"},{name:"label_smoothing_factor",val:": float = 0.0"},{name:"optim",val:": transformers.training_args.OptimizerNames | str = 'adamw_torch'"},{name:"optim_args",val:": str | None = None"},{name:"report_to",val:": None | str | list[str] = None"},{name:"resume_from_checkpoint",val:": str | None = None"},{name:"gradient_checkpointing",val:": bool = False"},{name:"gradient_checkpointing_kwargs",val:": dict[str, typing.Any] | str | None = None"},{name:"use_liger_kernel",val:": bool | None = False"},{name:"average_tokens_across_devices",val:": bool | None = False"},{name:"dataloader_prefetch_size",val:": int = None"},{name:"skip_cache_push",val:": bool = False"},{name:"use_autocast",val:": bool = False"},{name:"zero_1",val:": bool = True"},{name:"stochastic_rounding_enabled",val:": bool = True"},{name:"optimizer_use_master_weights",val:": bool = True"},{name:"optimizer_use_fp32_grad_acc",val:": bool = True"},{name:"optimizer_save_master_weights_in_ckpt",val:": bool = False"},{name:"tensor_parallel_size",val:": int = 1"},{name:"disable_sequence_parallel",val:": bool = False"},{name:"pipeline_parallel_size",val:": int = 1"},{name:"pipeline_parallel_num_microbatches",val:": int = -1"},{name:"kv_size_multiplier",val:": int | None = None"},{name:"num_local_ranks_per_step",val:": int = 8"},{name:"use_xser",val:": bool = True"},{name:"async_save",val:": bool = False"},{name:"fuse_qkv",val:": bool = False"},{name:"recompute_causal_mask",val:": bool = True"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/training_args.py#L51"}}),oe=new h({props:{name:"get_process_log_level",anchor:"optimum.neuron.NeuronTrainingArguments.get_process_log_level",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/training_args.py#L726"}}),se=new h({props:{name:"get_warmup_steps",anchor:"optimum.neuron.NeuronTrainingArguments.get_warmup_steps",parameters:[{name:"num_training_steps",val:": int"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/training_args.py#L748"}}),ie=new h({props:{name:"to_dict",anchor:"optimum.neuron.NeuronTrainingArguments.to_dict",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/training_args.py#L768"}}),le=new h({props:{name:"to_json_string",anchor:"optimum.neuron.NeuronTrainingArguments.to_json_string",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/training_args.py#L795"}}),me=new h({props:{name:"to_sanitized_dict",anchor:"optimum.neuron.NeuronTrainingArguments.to_sanitized_dict",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/training_args.py#L801"}}),ue=new gn({props:{title:"NeuronTrainer",local:"optimum.neuron.NeuronTrainer",headingTag:"h2"}}),pe=new h({props:{name:"class optimum.neuron.NeuronTrainer",anchor:"optimum.neuron.NeuronTrainer",parameters:[{name:"model",val:": transformers.modeling_utils.PreTrainedModel | torch.nn.modules.module.Module"},{name:"args",val:": NeuronTrainingArguments"},{name:"data_collator",val:": typing.Optional[transformers.data.data_collator.DataCollator] = None"},{name:"train_dataset",val:": Dataset | IterableDataset | datasets.Dataset | None = None"},{name:"eval_dataset",val:": Dataset | dict[str, Dataset] | datasets.Dataset | None = None"},{name:"processing_class",val:": transformers.tokenization_utils_base.PreTrainedTokenizerBase | transformers.image_processing_utils.BaseImageProcessor | transformers.feature_extraction_utils.FeatureExtractionMixin | transformers.processing_utils.ProcessorMixin | None = None"},{name:"callbacks",val:": list[transformers.trainer_callback.TrainerCallback] | None = None"},{name:"optimizers",val:": tuple[torch.optim.optimizer.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None)"},{name:"optimizer_cls_and_kwargs",val:": tuple[type[torch.optim.optimizer.Optimizer], dict[str, typing.Any]] | None = None"},{name:"tokenizer",val:": transformers.tokenization_utils_base.PreTrainedTokenizerBase | None = None"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L114"}}),de=new h({props:{name:"add_callback",anchor:"optimum.neuron.NeuronTrainer.add_callback",parameters:[{name:"callback",val:": typing.Union[typing.Type[transformers.trainer_callback.TrainerCallback], transformers.trainer_callback.TrainerCallback]"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.add_callback.callback",description:`<strong>callback</strong> (<code>Type[TrainerCallback] | TrainerCallback</code>) &#x2014;
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will instantiate a member of that class.`,name:"callback"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L367"}}),ce=new h({props:{name:"autocast_smart_context_manager",anchor:"optimum.neuron.NeuronTrainer.autocast_smart_context_manager",parameters:[{name:"cache_enabled",val:": bool | None = True"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L727"}}),ge=new h({props:{name:"create_accelerator_and_postprocess",anchor:"optimum.neuron.NeuronTrainer.create_accelerator_and_postprocess",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L283"}}),_e=new h({props:{name:"create_optimizer",anchor:"optimum.neuron.NeuronTrainer.create_optimizer",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L561"}}),fe=new h({props:{name:"create_optimizer_and_scheduler",anchor:"optimum.neuron.NeuronTrainer.create_optimizer_and_scheduler",parameters:[{name:"num_training_steps",val:": int"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L539"}}),ve=new h({props:{name:"create_scheduler",anchor:"optimum.neuron.NeuronTrainer.create_scheduler",parameters:[{name:"num_training_steps",val:": int"},{name:"optimizer",val:": torch.optim.optimizer.Optimizer | None = None"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.create_scheduler.num_training_steps",description:"<strong>num_training_steps</strong> (int) &#x2014; The number of training steps to do.",name:"num_training_steps"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L675"}}),he=new h({props:{name:"get_decay_parameter_names",anchor:"optimum.neuron.NeuronTrainer.get_decay_parameter_names",parameters:[{name:"model",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L550"}}),be=new h({props:{name:"get_learning_rates",anchor:"optimum.neuron.NeuronTrainer.get_learning_rates",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L618"}}),$e=new h({props:{name:"get_num_trainable_parameters",anchor:"optimum.neuron.NeuronTrainer.get_num_trainable_parameters",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L612"}}),xe=new h({props:{name:"get_optimizer_cls_and_kwargs",anchor:"optimum.neuron.NeuronTrainer.get_optimizer_cls_and_kwargs",parameters:[{name:"args",val:": TrainingArguments"},{name:"model",val:": transformers.modeling_utils.PreTrainedModel | None = None"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.get_optimizer_cls_and_kwargs.args",description:`<strong>args</strong> (<code>transformers.training_args.TrainingArguments</code>) &#x2014;
The training arguments for the training session.`,name:"args"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L642"}}),Te=new h({props:{name:"get_optimizer_group",anchor:"optimum.neuron.NeuronTrainer.get_optimizer_group",parameters:[{name:"param",val:": str | torch.nn.parameter.Parameter | None = None"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.get_optimizer_group.param",description:`<strong>param</strong> (<code>str | torch.nn.parameter.Parameter | None</code>, defaults to <code>None</code>) &#x2014;
The parameter for which optimizer group needs to be returned.`,name:"param"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L626"}}),ye=new h({props:{name:"get_train_dataloader",anchor:"optimum.neuron.NeuronTrainer.get_train_dataloader",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L520"}}),Ne=new h({props:{name:"is_local_process_zero",anchor:"optimum.neuron.NeuronTrainer.is_local_process_zero",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L1166"}}),Ce=new h({props:{name:"is_world_process_zero",anchor:"optimum.neuron.NeuronTrainer.is_world_process_zero",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L1173"}}),ke=new h({props:{name:"log",anchor:"optimum.neuron.NeuronTrainer.log",parameters:[{name:"logs",val:": dict[str, float]"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L1233"}}),we=new h({props:{name:"maybe_log_train_step_metrics",anchor:"optimum.neuron.NeuronTrainer.maybe_log_train_step_metrics",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L989"}}),Le=new h({props:{name:"maybe_save_checkpoint",anchor:"optimum.neuron.NeuronTrainer.maybe_save_checkpoint",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L1025"}}),ze=new h({props:{name:"num_examples",anchor:"optimum.neuron.NeuronTrainer.num_examples",parameters:[{name:"dataloader",val:": DataLoader"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L695"}}),De=new h({props:{name:"num_tokens",anchor:"optimum.neuron.NeuronTrainer.num_tokens",parameters:[{name:"train_dl",val:": DataLoader"},{name:"max_steps",val:": int | None = None"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L709"}}),Pe=new h({props:{name:"pop_callback",anchor:"optimum.neuron.NeuronTrainer.pop_callback",parameters:[{name:"callback",val:": typing.Union[typing.Type[transformers.trainer_callback.TrainerCallback], transformers.trainer_callback.TrainerCallback]"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.pop_callback.callback",description:`<strong>callback</strong> (<code>Type[TrainerCallback] | TrainerCallback</code>) &#x2014;
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will pop the first member of that class found in the list of callbacks.`,name:"callback"}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.4.1/optimum/neuron/trainers/transformers.py#L378",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The callback removed, if found.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>TrainerCallback | None</code></p>
`}}),Ie=new h({props:{name:"remove_callback",anchor:"optimum.neuron.NeuronTrainer.remove_callback",parameters:[{name:"callback",val:": typing.Union[typing.Type[transformers.trainer_callback.TrainerCallback], transformers.trainer_callback.TrainerCallback]"}],parametersDescription:[{anchor:"optimum.neuron.NeuronTrainer.remove_callback.callback",description:`<strong>callback</strong> (<code>Type[TrainerCallback] | TrainerCallback</code>) &#x2014;
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