o  hQ@sdZddlZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlZddlZddlZddlZddlmZddlmZddlmZmZmZmZmZddlmZddlmZ ddl!Z"ddl#Z#ddl$m%Z&ddl'm(Z(m)Z)m*Z*dd l+m,Z,dd l#m-Z-dd l.m/Z/m0Z0m1Z1m2Z2m3Z3dd l4m5Z5dd l6m7Z7ddl8m9Z9m:Z:m;Z;ddlZ>ddl?m@Z@ddlAmBZBddlCmDZDmEZEddlFmGZGddlHmIZImJZJmKZKddlLmMZMddlNmOZOddlPmQZQmRZRmSZSddlTmUZUmVZVddlWmXZXmYZYddlZm[Z[ddl\m]Z]m^Z^ddl_m`Z`ddlambZbmcZcmdZdmeZemfZfmgZgmhZhmiZiddljmkZkmlZlmmZmmnZnmoZompZpmqZqmrZrmsZsmtZtmuZumvZvmwZwmxZxmyZymzZzm{Z{m|Z|m}Z}m~Z~ddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZdd lmZmZmZdd!lmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZdd"lmZdd#lmZecgZefZerdd$lmZeZerdd%lmZerddlZerPddlmmZddlmmZdd lm5Ze,eءe,ekZeڐrOddlm%mZddlmZnd&Zer|ddlmm#Zdd lm5Ze,ee,d'kZdd(ljmZmZmZmZnd&ZerddlZerdd)lmZerdd*lmZmZdd lm5Zdd+lmZdd,lmZmZmZmZmZmZmZe2gZe,ee,d-krdd.lmZe,ee,d/krdd0lmZeeg7ZeKrdd1lmZed2rdd3lmZd4d5Zd6d7Zd8d9Zer ddlZer ddlZeeZ d:Z d;Z d<Z d=Z d>Zd?Zd@ZGdAdBdBZdS)Cuc The Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task. N)Mapping)Path) TYPE_CHECKINGAnyCallableOptionalUnion)#get_reporting_integration_callbacks) ModelCard create_repo upload_folder)version)nn) DataLoaderDatasetIterableDataset RandomSamplerSequentialSampler) __version__)PretrainedConfig) DataCollatorDataCollatorWithPaddingdefault_data_collator) DebugOptionDebugUnderflowOverflow)SequenceFeatureExtractor)FeatureExtractionMixin)"ALL_HYPERPARAMETER_SEARCH_BACKENDSdefault_hp_search_backend)BaseImageProcessor)deepspeed_initdeepspeed_load_checkpointis_deepspeed_available)tpu_spmd_dataloader)TrainingSummary)PreTrainedModelload_sharded_checkpoint unwrap_model)!MODEL_FOR_CAUSAL_LM_MAPPING_NAMESMODEL_MAPPING_NAMES) Adafactor get_scheduler)ProcessorMixin)ALL_LAYERNORM_LAYERS"is_torch_greater_or_equal_than_2_3)PreTrainedTokenizerBase)CallbackHandlerDefaultFlowCallbackExportableStatePrinterCallbackProgressCallbackTrainerCallbackTrainerControl TrainerState)DistributedTensorGathererEvalLoopContainerIterableDatasetShard LabelSmootherLayerWiseDummyOptimizerLengthGroupedSamplerSequentialDistributedSamplerdistributed_broadcast_scalarsdistributed_concatfind_batch_sizeget_model_param_countget_module_class_from_nameget_parameter_names nested_concat nested_detachnested_numpifynested_xla_mesh_reducereissue_pt_warningsremove_dummy_checkpointset_rng_state_for_device)PREFIX_CHECKPOINT_DIRBestRunEvalLoopOutputEvalPredictionHPSearchBackend HubStrategyPredictionOutputRemoveColumnsCollator SaveStrategyTrainerMemoryTracker TrainOutputcheck_target_module_existsdefault_compute_objectivedenumpify_detensorizeenable_full_determinismfind_executable_batch_sizeget_last_checkpoint has_lengthneftune_post_forward_hooknumber_of_arguments seed_workerset_seed speed_metrics)OptimizerNames ParallelModeTrainingArguments))ADAPTER_CONFIG_NAMEADAPTER_SAFE_WEIGHTS_NAMEADAPTER_WEIGHTS_NAME CONFIG_NAMESAFE_WEIGHTS_INDEX_NAMESAFE_WEIGHTS_NAMEWEIGHTS_INDEX_NAME WEIGHTS_NAMEXLA_FSDPV2_MIN_VERSIONPushInProgressPushToHubMixincan_return_loss find_labelsis_accelerate_availableis_apex_availableis_apollo_torch_availableis_bitsandbytes_availableis_datasets_availableis_galore_torch_availableis_grokadamw_availableis_in_notebookis_ipex_availableis_liger_kernel_availableis_lomo_availableis_peft_availableis_safetensors_availableis_sagemaker_dp_enabledis_sagemaker_mp_enabledis_schedulefree_availableis_torch_compile_availableis_torch_hpu_availableis_torch_mlu_availableis_torch_mps_availableis_torch_musa_availableis_torch_neuroncore_availableis_torch_npu_availableis_torch_xla_availableis_torch_xpu_availableis_torchao_availablelogging strtobool)deprecate_kwarg)QuantizationMethod)NotebookProgressCallback)ampFz1.10)smp_forward_backwardsmp_forward_only smp_gathersmp_nested_concat) PeftModel) Acceleratorskip_first_batches)AcceleratorState)AutocastKwargsDistributedDataParallelKwargsDistributedTypeload_fsdp_modelload_fsdp_optimizersave_fsdp_modelsave_fsdp_optimizer1.3.0)TorchTensorParallelPluginz0.23.0)SeedableRandomSampler)DeepSpeedSchedulerWrapper0.28.0)DataLoaderConfigurationcCsZtr+tr tfnd}ttjdtdkr&ddlm}g||R}t||SdS)Npeftz0.7.0r)PeftMixedModelF) rrrparse importlibmetadatarr isinstance)modelclasses_to_checkrrrh/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/transformers/trainer.py_is_peft_models  rcCs&trdtttjvrddiSiS)N adapter_onlyT)rtlistinspect signaturer parametersrrrr_get_fsdp_ckpt_kwargssrcCsxttjjtdjkrtSttjtdkrtjntj }|j j tj tj g}|tt tjg7}tj|S)Nz2.62.0.0)rrtorchrrelease contextlib nullcontextnp_corecore multiarray _reconstructndarraydtypetypeuint32 serialization safe_globals)np_core allowlistrrrrs " rztraining_args.binztrainer_state.jsonz optimizer.ptz scaler.ptz optimizer.binz scheduler.ptpytorch_model_fsdpc@s" eZdZdZddlmZmZmZmZm Z e ddddd dd e e e jd fd ed eedee eedfdee eeeefdfdee eeeefdeege fdeedeeegefdeeedeeej j!eej j"j#fdeee$ej j!eee%ffdeeej&ej&gej&ffddZ'e(deefddZ)e)j*dddZ)ddZ+d d!Z,d"d#Z-d$d%Z.d&d'Z/d(d)Z0d*d+Z1dd,dd-eefd.d/Z2 dd ed-eedefd0d1Z3deej4j5j6fd2d3Z7de8fd4d5Z9dedeej4j5j6fd6d7Z:ddee eefde8fd8d9Z;d:ede8fd;d<Zd?Z>deefd@dAZ?dBdCZ@dDdEZAdFdGZBddHee eej jCjDffdIdJZEeF dd ed ee dee%e%ffdKdLZGdd=e=dMej j!fdNdOZHdPe8de=fdQdRZIeFddSe8dTee=de=fdUdVZJdWe dXeee%fffdYdZZKdWe dXeee%ffd[e=d\eeeLffd]d^ZMd_efd`daZNddbdcZOddedfZPddejQfdgdhZRdidjZSddkdlZT ddmee eeUfdWe dXeee%fd fdneeefdodpZV ddqdrZWdsdtZXddudvZYdwdxZZdydzZ[dd{d|Z\ dd}d~Z]ddZ^ddZ_ddZ`ddZaddZbddZcddZdddZeddZf ddeedXgeeeLffdeeeeeLfgeLfde=de eeefdee degfdeedXgefde eheehffddZiddeeeLfdeeLdd fddZjde ej&e%fde ej&e%ffddZkdeee ej&e%ffdeee ej&e%fffddZlddZmddeeUfddZn dd e jdeee ej&e%ffdej&fddZodddZpdeUfddZqdeUfddZrddeedeUfddZsddeefddZtd deefddZuddZvd ewddfdeefddZxddddZy ddee eeeeffdeeededeeeLffddńZz Ɛdd:edeeedede{fddȄZ| ddPe8d-edeeUdeeedede}f dd˄Z~ddd̈́Z dd e jdeee ej&e%ffdeUdeeedeeej&eej&eej&ff ddτZdeee ej&e%fffddфZddeefddԄZ ddeedeede eeed fdeedeede eeed fde eeed fd,e eeed fde eeed ffddބZddZddZ  ddeedeUdeedeedef ddZ ddPe8d-edeeUdeeedede}f ddZddZdddZddZdddZddZddZd edPe8de=fddZd S( TraineruA Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Args: model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*): The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed. [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers models. args ([`TrainingArguments`], *optional*): The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided. data_collator (`DataCollator`, *optional*): The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will default to [`default_data_collator`] if no `processing_class` is provided, an instance of [`DataCollatorWithPadding`] otherwise if the processing_class is a feature extractor or tokenizer. train_dataset (Union[`torch.utils.data.Dataset`, `torch.utils.data.IterableDataset`, `datasets.Dataset`], *optional*): The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally sets the seed of the RNGs used. eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`, `datasets.Dataset`]), *optional*): The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each dataset prepending the dictionary key to the metric name. processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*): Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model. This supercedes the `tokenizer` argument, which is now deprecated. model_init (`Callable[[], PreTrainedModel]`, *optional*): A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start from a new instance of the model as given by this function. The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to be able to choose different architectures according to hyper parameters (such as layer count, sizes of inner layers, dropout probabilities etc). compute_loss_func (`Callable`, *optional*): A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default [loss function](https://github.com/huggingface/transformers/blob/052e652d6d53c2b26ffde87e039b723949a53493/src/transformers/trainer.py#L3618) used by [`Trainer`]. compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*): The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return a dictionary string to metric values. *Note* When passing TrainingArgs with `batch_eval_metrics` set to `True`, your compute_metrics function must take a boolean `compute_result` argument. This will be triggered after the last eval batch to signal that the function needs to calculate and return the global summary statistics rather than accumulating the batch-level statistics callbacks (List of [`TrainerCallback`], *optional*): A list of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in [here](callback). If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method. optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*, defaults to `(None, None)`): A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. optimizer_cls_and_kwargs (`Tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*): A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in `args`. Incompatible with the `optimizers` argument. Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer. preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*): A function that preprocess the logits right before caching them at each evaluation step. Must take two tensors, the logits and the labels, and return the logits once processed as desired. The modifications made by this function will be reflected in the predictions received by `compute_metrics`. Note that the labels (second parameter) will be `None` if the dataset does not have them. Important attributes: - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`] subclass. - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`, the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`. - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from data parallelism, this means some of the model layers are split on different GPUs). - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set to `False` if model parallel or deepspeed is used, or if the default `TrainingArguments.place_model_on_device` is overridden to return `False` . - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while in `train`) r )_get_learning_rate log_metricsmetrics_format save_metrics save_state tokenizerprocessing_classz5.0.0T)new_namerraise_if_both_namesNNNrargs data_collator train_datasetzdatasets.Dataset eval_dataset model_initcompute_loss_funccompute_metrics callbacks optimizersoptimizer_cls_and_kwargspreprocess_logits_for_metricsc Cs |durd}td|dt|d}|jr)| dur)dt| jvr)td|j dur@|j dkr@|dur@td|j d |j t j ksI|j rR|jdurRtd ||_||_|jjrbt|jjnt|jjd|_d|_d |_|t|jj|_|j|}t||j |dur|dur||_!|"}nt#d |durt$%d t&||_!|j'j(t)vrtd|j'j(dt*|dd rt*|dd rd|_+nd |_+t*|dddurddt,|j-.D}t/|dkrd|_+nt/|dkr|jj0t10|dk|_+nd |_+|j+rtd|jj2rKt3rGddl4m5}t6|t7r+||dn t8|drAt6|9t7rA||9dn t:dnt;dt*|dd oXt*|dd  }t*|d dduoe|j|s|std$|r|std%|j|sI|j]n|9j]}t|j}t8|d+r_|j^|__n t`d,d-|.D|__|ja|_a| |_b| |_c| \|_d|_e| |_f|jfdur|jddurt#d.|dur|jddus|jedurt#d/tgr|jddur|jZD]}|j0}|jdjhD]}t/|d0dkr|d0dj0}nq||krtd1|jBs|jLr|jddus|jedurt#d2titj|jjk}| dur|n|| } tl| |jZ|jT|jd|je|_m|n|jjortpntqd |_rd|_s|jjtr.|u|jjvr|js|jr |jd?kr |j0t10d@kr|jrtstdAndB|_tdC|jdD|js|jr;|jDs;ts;|jdBkr*d|_t1j|_n|jdEkr;ts8t;dFd|_|jjdkrKt|jjdG|_nd|_t|_t||dHd|jmj|jgDdI|_d|_d|_t>|jZr|jjdurt:dJ|jZj'j(dKt|jZj'}|jjdur|n|jj|_t|jZj'|_|jm|j|j|j|_|j|_d |_|j|jrtst#dL|jAdMd |_|jrtstdNt}ttjtt||dfdOdP|jBo|j |_dS)QN tmp_trainerz1No `TrainingArguments` passed, using `output_dir=z`.) output_dircompute_resultzWhen using `batch_eval_metrics`, your `compute_metrics` function must take a `compute_result` boolean argument which will be triggered after the last batch of the eval set to signal that the summary statistics should be returned by the function.noz%You have set `args.eval_strategy` to zx but you didn't pass an `eval_dataset` to `Trainer`. Either set `args.eval_strategy` to `no` or pass an `eval_dataset`. z`args.metric_for_best_model` must be provided when using 'best' save_strategy or if `args.load_best_model_at_end` is set to `True`.Fz<`Trainer` requires either a `model` or `model_init` argumentz`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will overwrite your model when calling the `train` method. This will become a fatal error in the next release.zThe model you have picked (a) cannot be used as is for training: it only computes hidden states and does not accept any labels. You should choose a model with a head suitable for your task like any of the `AutoModelForXxx` listed at https://huggingface.co/docs/transformers/model_doc/autois_parallelizablemodel_parallelT hf_device_mapcSsg|]}|dvr|qS))cpudiskr).0devicerrr z$Trainer.__init__..r rzYou have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.)_apply_liger_kernel_to_instancerget_base_modelzRThe model is not an instance of PreTrainedModel. No liger kernels will be applied.zYou have set `use_liger_kernel` to `True` but liger-kernel >= 0.3.0 is not available. Please install it with `pip install liger-kernel` is_quantized_hf_peft_config_loaded hf_quantizeris_qat_trainable _orig_modzYou cannot fine-tune quantized model with `torch.compile()` make sure to pass a non-compiled model when fine-tuning a quantized model with PEFTzYou cannot perform fine-tuning on purely quantized models. Please attach trainable adapters on top of the quantized model to correctly perform fine-tuning. Please see: https://huggingface.co/docs/transformers/peft for more detailsz8The model you are trying to fine-tune is quantized with z but that quantization method do not support training. Please open an issue on GitHub: https://github.com/huggingface/transformers to request the support for training support for xlazZUsing --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags.z.Using fsdp only works in distributed training.quantization_methodaccepts_loss_kwargscss|] }|jtjjkVqdSN)kindr Parameter VAR_KEYWORDrkrrr ~s z#Trainer.__init__..zSPassing both `optimizers` and `optimizer_cls_and_kwargs` arguments is incompatible.zPassing a `model_init` is incompatible with providing the `optimizers` argument. You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method.paramsa[The model and the optimizer parameters are not on the same device, which probably means you created an optimizer around your model **before** putting on the device and passing it to the `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and `model.to(xm.xla_device())` is performed before the optimizer creation in your script.zPassing `optimizers` is not allowed if PyTorch FSDP is enabled. You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method.exist_ok collate_batchzRThe `data_collator` should be a simple callable (function, class with `__call__`).zHmax_steps is given, it will override any value given in num_train_epochszThe train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.zTthe `--group_by_length` option is only available for `Dataset`, not `IterableDatasetzOSageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead z(FP16 provided in SM_HP_MP_PARAMETERS is z+, but FP16 provided in trainer argument is z , setting to fp16zJ, but SageMaker Model Parallelism < 1.10 does not support FP16 in trainer.autorz2Tried to use `fp16` but it is not supported on cpucpu_ampzUsing z half precision backendapexzcUsing FP16 with APEX but APEX is not installed, please refer to https://www.github.com/nvidia/apex.)epsiloncSg|] }t|tr|qSrrr3rcbrrrr  )is_local_process_zerois_world_process_zerostateful_callbacksz)No label_names provided for model class `z`. Since `PeftModel` hides base models input arguments, if label_names is not given, label_names can't be set automatically within `Trainer`. Note that empty label_names list will be used instead.z3Using torch.compile requires PyTorch 2.0 or higher. xla_fsdp_v2z*FSDPv2 requires `torch_xla` 2.2 or higher.)fsdptensor) axis_names)loggerinforfbatch_eval_metricsrrrkeys ValueError eval_strategy save_strategyrUBESTload_best_model_at_endmetric_for_best_modelrrfull_determinismr[seedrbhp_name deepspeed is_in_train"create_accelerator_and_postprocessrVskip_memory_metrics_memory_trackerstartget_process_log_levelr set_verbosity_setup_devicesrcall_model_init RuntimeErrorwarningswarn FutureWarning __class____name__r*getattris_model_parallelsetrvalueslenrruse_liger_kernelr}liger_kernel.transformersrrr&hasattrrwarning ImportErrorr is_trainablerquantization_config quant_method fsdp_configis_fsdp_xla_enabledris_deepspeed_enabled parallel_modere DISTRIBUTEDplace_model_on_devicefp16_full_evalbf16_full_evaldo_trainis_fsdp_enabledr0rrrrrrrrBITS_AND_BYTES_move_model_to_device_n_gpu model_wrappedr acceleratorr(forwardrmodel_accepts_loss_kwargsanyneftune_noise_alpharr optimizer lr_schedulerrr param_groupsDEFAULT_CALLBACKSr report_tor1callback_handler add_callback disable_tqdmr4DEFAULT_PROGRESS_CALLBACK_loggers_initialized hub_model_id push_to_hub init_hf_repo should_saveosmakedirsrcallable max_stepsnum_train_epochsr^utilsdatargroup_by_length_signature_columnsuse_apex use_cpu_amprbf16IS_SAGEMAKER_MP_POST_1_10rsmpstatecfghalf_precision_backendr/bfloat16 amp_dtyperulabel_smoothing_factorr<label_smootherr7controlr8rrr current_floshp_search_backend label_namesrsrr on_init_endtrain_batch_size_train_batch_size_created_lr_schedulerstop_and_update_metrics torch_compilergetis_fsdp_xla_v2_enabledIS_XLA_FSDPV2_POST_2_2xrglobal_runtime_device_countxsset_global_meshMeshrarrayrangeis_fsdp_xla_v1_enabled) selfrrrrrrrrrrrrrr log_leveldevicesr_is_quantized_and_base_model&_quantization_method_supports_training%_is_model_quantized_and_qat_trainabledefault_collatorunwrapped_model model_forwardforward_paramsparam model_device param_groupoptimizer_devicedefault_callbacksdefault_label_names num_devicesrrr__init__s&                        "                $zTrainer.__init__returncCstd|jS)NzUTrainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.rr<rrrrrr.s zTrainer.tokenizercCstd||_dS)NzjTrainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead.r)rrrrrr3s cCsH|j|}t|r|jj}n|}~|j|_|t}||_ |S)z Activates the neftune as presented in this code: https://github.com/neelsjain/NEFTune and paper: https://arxiv.org/abs/2310.05914 ) rOr(r base_modelrget_input_embeddingsrSregister_forward_hookr_neftune_hook_handle)rrr embeddings hook_handlerrr_activate_neftune:s  zTrainer._activate_neftunecCsPt|ds td|j|}t|r|jj}n|}|j |` ~dS)z^ Deactivates the neftune method. Make sure to call `_activate_neftune` first. rzNNeftune is not activated make sure to call `trainer._activate_neftune()` firstN) r;rrOr(rrrrrremoverS)rrrrrrr_deactivate_neftuneMs    zTrainer._deactivate_neftunecC|j|dS)ag Add a callback to the current list of [`~transformers.TrainerCallback`]. Args: callback (`type` or [`~transformers.TrainerCallback]`): A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the first case, will instantiate a member of that class. N)rYrZrcallbackrrrrZ^ zTrainer.add_callbackcCs |j|S)aK Remove a callback from the current list of [`~transformers.TrainerCallback`] and returns it. If the callback is not found, returns `None` (and no error is raised). Args: callback (`type` or [`~transformers.TrainerCallback]`): A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the first case, will pop the first member of that class found in the list of callbacks. Returns: [`~transformers.TrainerCallback`]: The callback removed, if found. )rY pop_callbackrrrrris zTrainer.pop_callbackcCr)a Remove a callback from the current list of [`~transformers.TrainerCallback`]. Args: callback (`type` or [`~transformers.TrainerCallback]`): A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the first case, will remove the first member of that class found in the list of callbacks. N)rYremove_callbackrrrrryrzTrainer.remove_callbackcCs6||}|jjtjkrt|dr|dSdSdS)N tie_weights)torrDreTPUr;r)rrrrrrrLs  zTrainer._move_model_to_devicecCs|jdur>|j}t|jrt|jdr|j}n|jjj}t|j}t |j |_|jt t ddg|j 7_dSdS)Nrlabel label_ids)rjrrr;rrrrrPrrrr6rz)rmodel_to_inspectrrrr _set_signature_columns_if_neededs      $z(Trainer._set_signature_columns_if_neededdataset descriptionc s |jjsS||j}ttjt|}t|dkrK|dur$dnd|d}t d|d|j j j dd |d d |d |j j j d fd d |D}t|dkretdd |dttjtdkrjjd|jddS|S)Nrzin the z setzThe following columns z) don't have a corresponding argument in `z!.forward` and have been ignored: , z. If z are not expected by `z/.forward`, you can safely ignore this message.csg|] }|jvr|qSr) column_namesrrrrrz2Trainer._remove_unused_columns..zpNo columns in the dataset match the model's forward method signature. The following columns have been ignored: [zp]. Please check the dataset and model. You may need to set `remove_unused_columns=False` in `TrainingArguments`.1.4.0r format_kwargs)rcolumnsr)rremove_unused_columnsrrjrr6rr8rrrr2r3joinrrrdatasetsr set_formatformatremove_columns)rrrsignature_columnsignored_columnsdset_descriptionrrrr_remove_unused_columnss>   zTrainer._remove_unused_columnscCs6|jjs|S||j}t||t||jjjd}|S)z=Wrap the data collator in a callable removing unused columns.)rrrr model_name) rrrrjrTrrr2r3)rrrrremove_columns_collatorrrr"_get_collator_with_removed_columnssz*Trainer._get_collator_with_removed_columnscCs|jdus t|js dS|jjrJtr,t|jtjr,|jj|jj vr)|j|jjnd}nd}|j dur9|j j dnd}t |jj |jj|j||dSt|jS)Nrrlengthsmodel_input_name)rr^rrirxrrrlength_column_namerrmodel_input_namesr>r|gradient_accumulation_stepsr)rrrrrr_get_train_samplers$ zTrainer._get_train_samplercCs|jdur td|j}|j}tr t|tjr |j|dd}n|j|dd}|j ||j j |j j |j j d}t|tjjjsT||d<|j j|d<t|d<|j j|d <|jt|fi|S) a@ Returns the training [`~torch.utils.data.DataLoader`]. Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed training if necessary) otherwise. Subclass and override this method if you want to inject some custom behavior. Nz+Trainer: training requires a train_dataset.trainingr batch_size collate_fn num_workers pin_memorypersistent_workerssampler drop_lastworker_init_fnprefetch_factor)rrrrxrrrrrr}rdataloader_num_workersdataloader_pin_memorydataloader_persistent_workersrrgrhrrdataloader_drop_lastradataloader_prefetch_factorrOpreparer)rrrdataloader_paramsrrrget_train_dataloaders&    zTrainer.get_train_dataloadercCs|dust|s dS|jjr1trt|ttdStr-t|t t |jj dSt |S|jjrgtrNt|tjrN|jj|jvrK||jjnd}nd}|jdur[|jjdnd}t|jj|||dS|jjdkrqt |SdS)N) num_replicasrank)rrrrrr )r^ruse_legacy_prediction_looprr?xmxrt_world_size get_ordinalrrodp_sizedp_rankper_device_eval_batch_sizerrirxrrrrrrrr>eval_batch_size world_size)rrrrrrr_get_eval_sampler sB  zTrainer._get_eval_samplercCsP|dur |jdur tdt|tr|nd}t|dr-||jvr-|jjr-|j |j|St|tr7|j|n|dur=|n|j}|j }t rTt|t j rT|j|dd}n|j|dd}|jj||jj|jj|jjd}t|tjjjs|||d<|jj|d <|jj|d <t|fi|}|jjrt|dr||j|<n||i|_|j |S) a  Returns the evaluation [`~torch.utils.data.DataLoader`]. Subclass and override this method if you want to inject some custom behavior. Args: eval_dataset (`str` or `torch.utils.data.Dataset`, *optional*): If a `str`, will use `self.eval_dataset[eval_dataset]` as the evaluation dataset. If a `Dataset`, will override `self.eval_dataset` and must implement `__len__`. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. Nz-Trainer: evaluation requires an eval_dataset.eval_eval_dataloaders evaluationrrrrr)rrrstrr;rrrrOrrrxrrrrrrrrrgrhrrrrr)rrdataloader_keyrreval_dataloaderrrrget_eval_dataloader6sF         zTrainer.get_eval_dataloader test_datasetcCs|j}trt|tjr|j|dd}n|j|dd}|jj||jj |jj |jj d}t|t j jjsF|||d<|jj|d<|jj|d<|jt|fi|S)a Returns the test [`~torch.utils.data.DataLoader`]. Subclass and override this method if you want to inject some custom behavior. Args: test_dataset (`torch.utils.data.Dataset`, *optional*): The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. It must implement `__len__`. testrrrrr)rrxrrrrrrrrrrrrgrhrrrrrOrr)rrrrrrrget_test_dataloaderss   zTrainer.get_test_dataloadernum_training_stepscCs8|trtjjjr|jj}n|j}|j||ddS)aZ Setup the optimizer and the learning rate scheduler. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or `create_scheduler`) in a subclass. )rrTN)create_optimizerrnrorprqrrTcreate_schedulerrrrTrrrcreate_optimizer_and_schedulers  z&Trainer.create_optimizer_and_schedulercCst|tgd}|S)a0 Get all parameter names that weight decay will be applied to. This function filters out parameters in two ways: 1. By layer type (instances of layers specified in ALL_LAYERNORM_LAYERS) 2. By parameter name patterns (containing 'bias', 'layernorm', or 'rmsnorm') )bias layernormrmsnorm)rEr.)rrdecay_parametersrrrget_decay_parameter_namessz!Trainer.get_decay_parameter_namesc str|jn|j}|jdur||fdd|D|jjdfdd|Dddg}|jdur<|j\}}n | |j|\}}d|vrN| d}d|vrW| d}d |vr`| d }||fi||_|j d krd dl }|j j}d }|D]7}t|tjr|td d |D7}td|d|dd||ddditd|dq~td|ddtrt|j|_|jS)a Setup the optimizer. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the Trainer's init through `optimizers`, or subclass and override this method in a subclass. Ncs"g|] \}}|vr|jr|qSr requires_gradrnprrrrz,Trainer.create_optimizer..)r weight_decaycs"g|] \}}|vr|jr|qSrr r rrrrrrroptimizer_dictAdam8bitrcSsi|] }||qSr)data_ptrnumelrr rrr rz,Trainer.create_optimizer..zskipped : izM paramsweight optim_bits zbitsandbytes: will optimize z in fp32z skipped: )rrNrrTrnamed_parametersrrrget_optimizer_cls_and_kwargspopr3 bitsandbytesoptimGlobalOptimManager get_instancemodulesrr Embeddingsumrr7rrregister_module_overridedebugroDistributedOptimizer) r opt_modeloptimizer_grouped_parameters optimizer_clsoptimizer_kwargsrmanagerskippedmodulerrrrsN             zTrainer.create_optimizercCstdd|jDS)z9 Get the number of trainable parameters. css|] }|jr|VqdSr)r rrrrrrz7Trainer.get_num_trainable_parameters..)r%rrrrrrget_num_trainable_parameterssz$Trainer.get_num_trainable_parameterscCs$|jdur tddd|jjDS)zR Returns the learning rate of each parameter from self.optimizer. NPTrainer optimizer is None, please make sure you have setup the optimizer before.cSg|]}|dqS)lrrrgrouprrrrz.Trainer.get_learning_rates..rTrrVrrrrget_learning_ratess zTrainer.get_learning_ratesrcCsN|jdur td|dur|jjD] }||dvr|Sqdd|jjDS)a Returns optimizer group for a parameter if given, else returns all optimizer groups for params. Args: param (`str` or `torch.nn.parameter.Parameter`, *optional*): The parameter for which optimizer group needs to be returned. Nr2rcSr3)rrr5rrrrr7z/Trainer.get_optimizer_group..r8)rrr6rrrget_optimizer_groups   zTrainer.get_optimizer_groupc#s| ijrjdddD] }|d\}}||<qdjijjfjd} d|dtd ttt fd ttt fd t d t t t ff fd d }j t jkrbt}ddd|fSj t jt jfvrddlm}|}|j t jkrddi|fSj t jkrzddlm}|}|W|fStytdwj t jkrzddlm} | }|W|fStytdwj t jkrzddlm} | }|W|fStytdwj t jt j t j!t j"t j#t j$t j%t j&t j't j(t j)t j*t j+t j,t j-fvr zddl.m}m/} m0} d} d}d}|}dj vr4d} dj vr}|}|t?8d8d9t@tA8d:d;t@tA8dd?W|fStyVtd@wj t jBkrftAj jB}|fSj t jCkrutAj jD}|fSj t jEkrtAj j0}|fSj t jFt jGt jHt jIt jJt jKfvrtLstdAddBlMmN}mO}mP}t jF|t jG|t jH|t jI|t jJ|t jK|i}t9QdCdDt9QdEdFt7QdGdHQdIdJdK}|j ||\}j t jHkrddd|fSj t jRt jSfvrItTstdLddMlUmV}t jR|t jS|i}t9QdCdDQdNdOQdPdQt9QdEdFt7QdGdRQdIdJdS}|j ||\}|fSj t jWt jXfvrtYs[tdTtZdUsdtdVdurmtdWddXl[m\}m]}dYj vr~|}n|}dZi|fSj t j^krt_std[dd\l`ma}|}t78d]d^t78d_d`t78dadbt78dcdbt78dddRde|fSj t jbt jcfvr,tdrt23t4j52dft23dgkrtdht23t4j52dit23djkrtdkddllemf}mg}j t jbkr|}nj t jckr|}ntdm||fSj t jht jit jjfvrtks@tdntZdUsItdoddpllmm}mn} i}d}!j t jhkrrtkdqsetdrddsllmo}"|"}|}d}!nj t jikr~|}|}nj t jjkr| }ntdtjp|du<|!rjq|dv<|t78dwd`t78dxdydz||fStd{j )}z Returns the optimizer class and optimizer parameters based on the training arguments. Args: args (`transformers.training_args.TrainingArguments`): The training arguments for the training session.  r,=r4)betasepsToptimizer_nameoptimizer_mapping optim_kwargsis_layerwise_supportedrcsJ|d}|rjtjkr|rtd|d||}jdur*td|dtjt t fs:tdjdurFtd|d tjt oTj d d d k}g}g D]6\}} t j|d d\} } t| tjs| r| st|d|dq]| s|sq]|| j|dq]t|dkrtd|djdfddD} |d| id|i|g} |rjdkrtd|di| D]}|d|gigfi|<q|D]}|d|gi|gfi|<qfdd}D] }|jr||qt}did| i|fS)a Helper function to set up low-rank optimizers like GaLore and Apollo. Args: optimizer_name (str): Name of the optimizer. optimizer_mapping (dict): Mapping of optimizer names to their classes. optim_kwargs (dict): Keyword arguments for the optimizer. is_layerwise_supported (bool): Whether layerwise optimization is supported. Returns: Tuple[Any, Any]: Optimizer class and updated optimizer kwargs. layerwisez Layer-wise z" does not support DDP at this timeNz1You need to define `optim_target_modules` to use z optimizerszX`optim_target_modules` must be a list of strings, a regex string, or 'all-linear'. Got: z'You need to pass a model to initialize z optimizer._-z all-linearT)return_is_regexz matched but ignored. z only supports linear layers.z.weightrzNo target modules found for z ().csg|] \}}|vr|qSrrr )target_params_namesrrrkzZTrainer.get_optimizer_cls_and_kwargs..setup_low_rank_optimizer..rr z Layerwise z( does not support gradient accumulation!cs*|jdur||dSdSr)gradstep zero_grad)r)rrroptimizer_hook}s  z^Trainer.get_optimizer_cls_and_kwargs..setup_low_rank_optimizer..optimizer_hookr)lowerendswithrDrerENotImplementedErroroptim_target_modulesrrrrreplace named_modulesrXrLinearrr<appendrr8rupdaterrr "register_post_accumulate_grad_hookr=)r@rArBrC is_layerwiser+ all_linear target_params module_namer/target_module_existsis_regexnon_target_paramsrVrrNrr optim_argsr,)rrIrsetup_low_rank_optimizer,sn         "   zFTrainer.get_optimizer_cls_and_kwargs..setup_low_rank_optimizerF)scale_parameter relative_stepr)AdamWfusedz7Trainer failed to import syncfree AdamW from torch_xla.) NpuFusedAdamWz3Trainer failed to import FusedAdamW from torch_npu.) FusedAdamzFTrainer tried to instantiate apex FusedAdam but apex is not installed!)reLionRMSproprNpaged8bitadamlionr>rmspropademamixrz0.44.0z{The AdEMAMix optimizer is not supported by your current version of `bitsandbytes`. Please install `bitsandbytes` >= 0.44.0.)AdEMAMixbeta1beta2beta3gH.?alphag@r?)r>rvr?t_alphat_beta3ris_pagedzOTrainer tried to instantiate bnb optimizer but `bitsandbytes` is not installed!z0.41.1zYou are using 8-bit optimizers with a version of `bitsandbytes` < 0.41.1. It is recommended to update your version as a major bug has been fixed in 8-bit optimizers.)AnyPrecisionAdamWuse_kahan_summationFalsemomentum_dtypefloat32variance_dtypecompensation_buffer_dtypers)r{r}rrz4Please install https://github.com/pytorch/torchdistxzYou need to install `galore_torch` in order to use GaLore optimizers install it with `pip install git+https://github.com/jiaweizzhao/GaLore`)GaLoreAdafactor GaLoreAdamWGaLoreAdamW8bitrupdate_proj_gapscaleg? proj_typestd)rrrrzYou need to install `apollo_torch` in order to use APOLLO optimizers install it with `pip install git+https://github.com/zhuhanqing/APOLLO`) APOLLOAdamWprojrandom scale_typechannelg?)rrrrrrziYou need to install `lomo_optim` in order to use LOMO optimizers install it with `pip install lomo-optim`0.30.0zGYou need to have `accelerate>=0.30.0` to be able to use LOMO optimizerszMYou need to pass a `model` in order to correctly initialize a LOMO optimizer.)AdaLomoLomoadarz5Please install grokadamw with `pip install grokadamw`) GrokAdamW alpha_initg\(\?lambg@gammag?grokking_signal_decay_rategradient_clipping)rrrrrtorchaoz0.4.0zYou need to have `torchao>=0.4.0` in order to use torch 4-bit optimizers.Install it with `pip install torchao` or follow the instructions here: https://github.com/pytorch/aorz2.4zYou need to have `torch>2.4` in order to use torch 4-bit optimizers. Install it with `pip install --upgrade torch` it is available on pipy. Otherwise, you need to install torch nightly.) AdamW4bit AdamW8bitzInvalid optimizerzwYou need to install `schedulefree` in order to use schedulefree optimizers. Install it with `pip install schedulefree.`zOYou need to have `accelerate>=0.30.0` to be able to use schedulefree optimizers)AdamWScheduleFreeSGDScheduleFreerzYou need to install `schedulefree>=1.4.0` in order to use RAdamScheduleFree optimizer. Install it with `pip install schedulefree.`)RAdamScheduleFreezInvalid schedulefree optimizerr warmup_stepsweight_lr_powerrr)rrz2Trainer cannot instantiate unsupported optimizer: T)rrarSsplit learning_rate adam_beta1 adam_beta2 adam_epsilonrdictrbooltupler rd ADAFACTORr+rW ADAMW_TORCHADAMW_TORCH_FUSED torch.optimreADAMW_TORCH_XLAtorch_xla.amp.syncfreer=rADAMW_TORCH_NPU_FUSEDtorch_npu.optimrgADAMW_APEX_FUSEDapex.optimizersrh ADAMW_BNB ADAMW_8BIT PAGED_ADAMWPAGED_ADAMW_8BITADEMAMIX ADEMAMIX_8BITPAGED_ADEMAMIXPAGED_ADEMAMIX_8BITLION LION_8BIT PAGED_LIONPAGED_LION_8BIT RMSPROP_BNB RMSPROP_8BIT RMSPROP_32BITbitsandbytes.optimrirjrwrrrrrrfloatrintrr<ADAMW_ANYPRECISIONtorchdistx.optimizersrzrr4rSGDADAGRADAdagradRMSPROP GALORE_ADAMWGALORE_ADAMW_8BITGALORE_ADAFACTORGALORE_ADAMW_LAYERWISEGALORE_ADAMW_8BIT_LAYERWISEGALORE_ADAFACTOR_LAYERWISEry galore_torchrrrr APOLLO_ADAMWAPOLLO_ADAMW_LAYERWISErv apollo_torchrLOMOADALOMOr~rt lomo_optimrr GROKADAMWrz grokadamwrADAMW_TORCH_4BITADAMW_TORCH_8BITrtorchao.prototype.low_bit_optimrrSCHEDULE_FREE_RADAMSCHEDULE_FREE_ADAMWSCHEDULE_FREE_SGDr schedulefreerrrrr)#rrmappingkeyvalue adam_kwargsrbr+rergrhrirjryradditional_optim_kwargsrr bnb_kwargsrzrrrrAgalore_optim_kwargsrapollo_optim_kwargsrrrrrrrrequire_warmuprrr`rrs        `:   3   -    %                    L   @31/        p   Z R 1      z$Trainer.get_optimizer_cls_and_kwargsrTcCsH|jdur!t|jj|dur|jn||j|||jjd|_d|_|jS)z Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument. Args: num_training_steps (int): The number of training steps to do. N)rTnum_warmup_stepsrscheduler_specific_kwargsT)rUr,rlr_scheduler_typerTget_warmup_stepslr_scheduler_kwargsr~rrrrrs  zTrainer.create_scheduler dataloaderc CsTz|j}t|trt|jjWSt|jWStttfy)t||jjYSw)z Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When dataloader.dataset does not exist or has no length, estimates as best it can ) rrr;r8 NameErrorAttributeError TypeErrorrper_device_train_batch_size)rrrrrr num_exampless  zTrainer.num_examplestrain_dlrecCs^d}z|D]}|d}|dur||WS||7}qW|Sty.tdY|Sw)zq Helper to get number of tokens in a [`~torch.utils.data.DataLoader`] by enumerating dataloader. r input_idsNz%Cannot get num_tokens from dataloader)rKeyErrorrr<)rre train_tokensbatchtokensrrr num_tokenss   zTrainer.num_tokenstrialz optuna.TrialcCs||_|jdus |durdS|jtjkr||}n(|jtjkr)|}|ddn|jtjkr:dd|j D}n|jtj krB|}| D],\}}t |j |sZt d|dqFt|j |d}|durkt||}t|j ||qF|jtjkrt d|j|jtjkrt d|j|jtj krt d ||jr|j jdurtd |jd d lm}d d lm}||j j|j _|j j|j ||j jd|j _t !|"dS)zHP search setup codeNwandbcSs(i|]\}}|t|trt|n|qSr)rrrrrvrrrrs(z,Trainer._hp_search_setup..zTrying to set zY in the hyperparameter search but there is no corresponding field in `TrainingArguments`.zTrial: zSigOpt Assignments: zW&B Sweep parameters: z7For sweeps with deepspeed, `args.deepspeed` must be setr)DeepSpeedPluginHfTrainerDeepSpeedConfig) hf_ds_config)#_trialryrQOPTUNAhp_spaceRAYrSIGOPT assignmentsitemsWANDBr;rrr<r4rsetattrrrrCr$rrO free_memoryaccelerate.utilsr#transformers.integrations.deepspeedrhf_deepspeed_configtrainer_config_processdeepspeed_pluginr _reset_stater&)rrrrrold_attrrrrrr_hp_search_setupsN                 zTrainer._hp_search_setuprLmetricscCs|jdus |dur dS|}|||_|jtjkrIddl}t|drC|j sE| |j|| rG|j |j|j|j|dSdSdS|jtjkrddl}t)}d}|jjrk|j|d|jj|}|j|d<|jj ||dWddS1swYdSdS)Nrstudy)checkpoint_dir objective checkpoint)rycopycompute_objectiverrQroptunar;r_is_multi_objectivereport should_prunerY on_train_endrrprw TrialPrunedr  ray.traintempfileTemporaryDirectoryra_tune_save_checkpointtrain Checkpointfrom_directory)rrrLrr raytemp_checkpoint_dirrrrr_report_to_hp_search1s0      "zTrainer._report_to_hp_searchrcCstj|td|jj}|j|dd|jjrK|j |jj d<|j tj|t t |jtj|tt |jtj|tdSdS)NrFT_internal_callr7)rbpathrrMrp global_step save_modelrrarwr save_to_jsonTRAINER_STATE_NAMErsaverT state_dictOPTIMIZER_NAMErUSCHEDULER_NAME)rrrrrrr)Is zTrainer._tune_save_checkpointcCsLt|j}|dkr|}n|dkr||}ntd|dur$td|S)Nrr z'model_init should have 0 or 1 argument.z"model_init should not return None.)r`rr.)rrmodel_init_argcountrrrrr-Ss   zTrainer.call_model_initFc s|s|dur td|Stt||zt|}||jdd}|r.||_ t dd}|j j |dkt Wttt jjtdkrottr^t jj|dd}n1t jj|fdd Ddd}n g}D]}t |} || qst|}t jj||dd }Wdn1swYWdn1swYt j|}t |d i|d iWdn1swY|}d|_W|Sttttt fy} ztd | d WYd} ~ |Sd} ~ ww|S)NzAfailed to use PyTorch jit mode due to current dataloader is none._original_forwardF) cache_enabled)autocast_handlerr)example_kwarg_inputsstrictcsi|]}||qSrr)rr example_batchrrrwsz0Trainer.torch_jit_model_eval..r@z'failed to use PyTorch jit mode due to: .r)!rr<nextiter_prepare_inputsrr__dict__rrPrrOautocastrno_gradrrr base_versionrrjittrace ones_likerVrfreezerlr.rrr IndexError) rrrr jit_modeloriginal_forwardr> jit_inputsrexample_tensorerrArtorch_jit_model_evalas\         zTrainer.torch_jit_model_evalcCststdddl}|s+||js|jjrtjn|}|j ||dd|j d}|S|j s2| |j |||j ddd\}|_ |S)NzUsing IPEX but IPEX is not installed or IPEX's version does not match current PyTorch, please refer to https://github.com/intel/intel-extension-for-pytorch.rO1F)rlevelconv_bn_foldinginplaceT)rrTrZrX) r|r=intel_extension_for_pytorchrr%rrHrrsoptimizerr*rT)rrrripexrrripex_optimize_models   zTrainer.ipex_optimize_modelc Csdddd}d}d}|D],\}}t||d}t||d} |dur:| dur:|| kr:|d|d|d | d 7}d }q|j} |jtd |j} | | krX|d | d | d 7}d }|rat|dSdS)N logging_steps eval_steps save_steps)r_r`raFztWarning: The following arguments do not match the ones in the `trainer_state.json` within the checkpoint directory: z rz (from args) != z (from trainer_state.json)Tr z per_device_train_batch_size: )r r4rr|maxn_gpur warning_once) r training_args trainer_stateattributes_map has_warning warning_strarg_attr state_attr arg_value state_value train_bs_argstrain_bs_staterrr#compare_trainer_and_checkpoint_argss*  z+Trainer.compare_trainer_and_checkpoint_argscsRjjrjr tjntj}j|||d}tr,tj t j j r#j St j |jj dSj||ur6|SjrI|rItj|jjjd\}_jjdkrZt|ddsZt|}jjrst}|||}tt|d_|sw|SjrAzdd lm dd lm!dd l"m#}m$}j%rdd l&m'Wn t(yt(d wd}d} t|dd} jj)*d| } jj)ddkrt+j,|jj)dd}n%| durt-} | D]} t.|| }|durt/d| 0|qt+j,|| d}jj1}jj)dr|j2j3r t45dd|j2_3fdd} j%r'dd}|||| d_ }n|f|| d|_ }difdd}|t6_7|St8rUtj9j:|t;t<=dgd}|Sjj>t?j@krtArc|Si}jjBdursjjB|d <nt|tCr|jD |d <nd!|d <jjEdurjjE|d"<jjFdurjjF|d#<tGd$i|j_H|S)%N)r)backward_passes_per_step) opt_levelr is_loaded_in_8bitFr)XlaFullyShardedDataParallel)checkpoint_module)size_based_auto_wrap_policytransformer_auto_wrap_policy)SpmdFullyShardedDataParallelzJMissing XLA FSDP related module; please make sure to use torch-xla >= 2.0._no_split_modulestransformer_layer_cls_to_wrapmin_num_params)r|z@Could not find the transformer layer class to wrap in the model.)transformer_layer_clsxla_fsdp_grad_ckptzX`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.cs(jsn}||g|Ri|Sr)r)mrkwargs target_clsFSDPFSDPv2rvrrrauto_wrapper_callablesz2Trainer._wrap_model..auto_wrapper_callablecSshddlm}d}t|tjr|}nt|tr|d}nt||r#|j}|dur+tdt ||ddS)Nr )CausalLMOutputWithPastrzASomething went wrong, the output of the model shouldn't be `None`)rNN) modeling_outputsrrrTensorrlogitsrr mark_sharding)outputmeshr real_outputrrr shard_outputs     z)Trainer._wrap_model..shard_output)rauto_wrap_policyr)rrcSs |jdi|}|rt|S)Nr)rLr mark_step)rTbarrieroptimizer_argslossrrrpatched_optimizer_step9sz3Trainer._wrap_model..patched_optimizer_stepSMDATAPARALLEL_LOCAL_RANK) device_idsfind_unused_parametersT bucket_cap_mbbroadcast_buffersr)Iruse_ipexrlrrsr~r^rrrNrorDistributedModelrrOr(rkr initializerTfp16_opt_levelrcr4r DataParallel jit_mode_evaltimerVroundjit_compilation_timerBtorch_xla.distributed.fsdprurvtorch_xla.distributed.fsdp.wraprwrxr7torch_xla.experimental.spmd_fully_sharded_data_parallelryr=rAr functoolspartialr6rD Exceptionaddxla_fsdp_configconfig use_cacherrdroptimizer_steprparallelDistributedDataParallelrrbgetenvrDrerErddp_find_unused_parametersr&is_gradient_checkpointingddp_bucket_cap_mbddp_broadcast_buffersr ddp_handler)rrrrr start_timerwrxrr%default_transformer_cls_names_to_wrap"fsdp_transformer_layer_cls_to_wraptransformer_cls_to_wrap layer_classtransformer_cls fsdp_kwargsrrrrrr _wrap_models                zTrainer._wrap_modelresume_from_checkpointignore_keys_for_evalc Ks|durd}|j|j}d|_|jdur||j|_|js#|jr6|j s6|j s6|j dur6| |j|j d|vrE|d}tdtt|dkrZtddt|d |||jj|_d}|j dur|jjrut|jjnt|jj|||_d}d \|_|_ t!|t"r|rt#|j$}|durt%d |j$d |durt&s|j's|j(s|)|t*+t,j-|t.}|jdur|j|_|r|j/r| |j|j |j|_0t1|j2|j|j3}|j4rzt56|||||d Wt57St57w|||||d S)a Main training entry point. Args: resume_from_checkpoint (`str` or `bool`, *optional*): If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here. trial (`optuna.Trial` or `Dict[str, Any]`, *optional*): The trial run or the hyperparameter dictionary for hyperparameter search. ignore_keys_for_eval (`List[str]`, *optional*) A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions for evaluation during the training. kwargs (`Dict[str, Any]`, *optional*): Additional keyword arguments used to hide deprecated arguments FNT model_pathzi`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` instead.rz*train() got unexpected keyword arguments: rrDrz/No valid checkpoint found in output directory ())rrrr)8r(r)rr%rSrrrGrHrIr5rrLrrr/r0r1r8rrrrrr|r}r!r[r"rbr-rTrUrrr]rrrrCrJ_load_from_checkpointr8load_from_jsonrbr2r6rFrNr\_inner_training_loopauto_find_batch_sizer_ hf_hub_utilsdisable_progress_barsenable_progress_bars) rrrrrrmodel_reloadedrpinner_training_looprrrr*[s              z Trainer.trainc; Cs |j||_|jjrE|jj|jkr@ddlm}||j \|_ |j |_ |j r@|jj }|jt d|jj|j_ |d||j_ |j|j_td|j|}|jrYt|}|j|j|j} |||| \} } } } }}}d}|jjr|||r}dn|}|dur|r||j9}n||j9}tj|jjvr|jjdkrtdt|j }tp|j p|j!}|j!ot"|jjj#dddk}|rd }|j$rd|_%d |_$|j rt&||d \|_'|_%|s|j(|d t)d d |j*j+|j,gDd |_|du|j_-|j|j_|j.|||j/r|j j0|j1d|2|j }||j urdnd }|r.|j!r.t3|j dd|_ |rM|rG|4|jj5dkrG|j6|j |_ |j(|d |r|j 7t8|j%dru|j9rh|j6|j }n1|j6|j |j'\}|_'n$|j6|j |j'|j%\}|_'|_%n|jj:t;j|dur|j rt?|j |t@|j  dnts|j!r|A||j |B||C|tDdtDd| dtDd| dtDd|jj d|jj |jkrtDd|jdtDd| dtDd|jtDd|dtDdtE|dddd|j_FtGG}d}d}d}|durtHjIJtHjIK|tLrt)MtHjIK|tL|_|N|j|j|OtP|jjQ| }|jRs|jjQ| }||j9}nd}tDdtDd|tDd |jjQ|jRstDd!|d"|d#d$D] }tS|j*|t"||q||j*_T|jU||| |tVjWd%|jXd&}d%|_Y|jjQ|_Z|[d}d}|j*\||j|j,|_,|j]r|j^||dd't_|| D] }|} t8| d(r| `||jadkr d|_b|dur)tc| n|jd|j}!|j*e||j|j,|_,||krN|durN|dkrN|f|d }"d}#|dkrbtg| |} |}#d}d}"d)}$th| }%| |j}&|&dkru|j}&d)}'|!|jd}(|jdkr|(d8}(t_|(D]4})|'d7}'|'|(dkr|jn|&}*|i|%|*|jX\}+},tj|+D]\}-}.|$d7}$|$d|jdkp|$d|!k}/|jjkl|/|jjmrt"|j d*d+}0|0|.vrtnd,n!|.|0o}1tVjW|1|jjXtVjpd-}1|jjq|jr|1st7_q|"r|f|d }"|dkr.|d8}|dur"|ud|dkr,|f|q|dur9|vd}|$|jdkrL|j*w||j|j,|_,|-tc|+dkrf|jjxtyjzkrft{j||jj}|d.nt~j}2|2|||.|,}3Wdn 1swY|jrtstV|3stV|3r||d|jjQ|jZ}n|jX|3jXkrtd/|jXd0|3jX||3}|jt||.7_|/r|jjkld|jdur'|jdkr'tr|jr|j'|j}4n|j9rtjt|j'|j}4n |j||j}4tr%|jjxtyjzkr%|}t8|d1r$|t}n|4}|j*||j|j,|_,|j'|j*||j|j,|_,|}|jjsZt|j%tVj:j%jsZ|j%|[|jjQd7_Q||$d|#|!|j_F|j*||j|j,|_,|j||||||||d2n |j*||j|j,|_,|j,js|j,jrtrtnq|j,js|j,jrtrtnq|$dkrtnd3|jjQd4|d5d|j,_|j*||j|j,|_,|j||||||||d2tj|jjvr trttntnd6|j,jrnq|jar"t8|d7r"t|d7tDd8|jrT|jjdurTtr<d |_|j|7||7||}8|jd |8d?}9|jjr|jjdur|jjdkr|9D]}:tHjI|:|jjstDd@|:dAtj|:ddBq|j*||j|j,|_,||jdur||j t|jjQ|6|7S)CNr)release_memoryr Tz)Currently training with a batch size of: zCurrently --debug underflow_overflow is not supported under DP. Please use DDP (torchrun or torch.distributed.launch (deprecated)). fsdp_versionF)rcSr rr r rrrr rz0Trainer._inner_training_loop..)r)gradient_checkpointing_kwargs) recursivefp8rLload_module_strictz***** Running training ***** Num examples = r<z Num Epochs = z( Instantaneous batch size per device = zA Training with DataParallel so batch size has been adjusted to: zE Total train batch size (w. parallel, distributed & accumulation) = z Gradient Accumulation steps = z Total optimization steps = z# Number of trainable parameters = )trainable_onlyzE Continuing training from checkpoint, will skip to saved global_stepz! Continuing training from epoch z' Continuing training from global step z Will skip the first z epochs then the first z batches in the first epoch.)rrTrUrr)skip_scheduler set_epochmain_input_namerzTried to track the number of tokens seen, however the current model is not configured properly to know what item is the input. To fix this, add a `main_input_name` attribute to the model class you are using.)rrrz0Calculated loss must be on the original device: z but device in use is item)rzXThere seems not to be a single sample in your epoch_iterator, stopping training at step zI! This is expected if you're using an IterableDataset and set num_steps (z.) higher than the number of available samples.zYou enabled PyTorch/XLA debug metrics but you don't have a TPU configured. Check your training configuration if this is unexpected._pastzU Training completed. Do not forget to share your model on huggingface.co/models =) rgMbP?r*) num_samples num_stepsr total_flos train_loss use_mtimerDeleting older checkpoint [] due to args.save_total_limit ignore_errors)rOrr}rrrpr|rrrNrrCrrbrcpropagate_args_to_deepspeedrr'rrr$rrset_initial_training_valuesinclude_tokens_per_secondrrfrUNDERFLOW_OVERFLOWrrrrBrJr4 fsdp_pluginr~rUr!rTrr8rYrrwis_hyper_param_search compute_stepsgradient_checkpointinggradient_checkpointing_enablerrr(_fsdp_qlora_plugin_updatesmixed_precisionrr*r;rkr rdrrr$r"rr_load_optimizer_and_scheduler _load_scalerrrCepochrrbr2isfilerr6rrp_load_callback_staterr3ignore_data_skiprtrain_dataloaderinit_training_referencesrrr_total_loss_scalar_globalstep_last_loggedrMon_train_begin eval_on_start _evaluaterr past_indexrr8reon_epoch_begin_load_rng_staterrFget_batch_samples enumerategradient_state_set_sync_gradientsinclude_num_input_tokens_seenr<rint64num_input_tokens_seengatherr%rrWclose on_step_begindistributed_typer DEEPSPEEDrrno_syncrr training_steplogging_nan_inf_filterrisnanisinfrxrfloating_point_ops max_grad_normrclip_master_gradsrrgclip_grad_norm_r master_paramsrrtget_global_grad_normon_pre_optimizer_steprLon_optimizer_steproptimizer_step_was_skippedrReduceLROnPlateau on_step_end_maybe_log_save_evaluateon_substep_endshould_epoch_stopshould_training_stoprr on_epoch_endTPU_METRICS_DEBUG master_printmetmetrics_reportdelattrrbest_model_checkpoint rendezvousrDrerEdistrro_load_best_modelrc store_flosrr%r(rlog_get_output_dir_sorted_checkpointsrasave_total_limitsamefileshutilrmtreer$_finish_current_pushrSrrW);rrrrrrr original_bsrtotal_train_batch_sizerfnum_update_steps_per_epochrnum_train_samples epoch_basedlen_dataloaderrenum_train_tokensdebug_overflowdelay_optimizer_creationis_fsdp2ruse_accelerator_preparerepochs_trainedsteps_trained_in_current_epochsteps_trained_progress_barattrtr_loss grad_normrrepoch_dataloadersteps_in_epoch rng_to_sync steps_skippedrLepoch_iterator remainder update_step total_updatesrE num_batches batch_samplesnum_items_in_batchiinputs do_sync_stepr input_tokenscontext tr_loss_step _grad_normeffective_global_steprrrun_dircheckpoints_sortedrrrrrs                                                                    &  zTrainer._inner_training_loopcCs|jdurW|durW|jtjkr|j}n*|jtjkr%ddl}|j}n|jtj kr/|j }n|jtj kr=ddl }|j j }|jdurG||nd|}tj|jj|}|S|jj}|S)Nrzrun-)ryrQrnumberr r&r* get_context get_trial_idr idr rrunr#rbr2rrr)rrrun_idr-rrun_namerXrrrr- s     zTrainer._get_output_dircs|dur|j}tjt}tjt}tjt}tjt}tjt}tjt }tjt } tj oYt fddt DpYtjtjtd} tj rlfddt Dng} | r{|js{tddt dd|||| ||fDs| s| std td d tj|rt|} | j} | dur| tkrtd | d tdtj|stj|s| rQtrtjtjdrtjtddddSt|jdr|jjdurtdtj |ddd}d|d<|j!|dd}~dS|jr&t"|j#j$j%|j#|fit&dS|jj'r;tj|r;t(jj)|dd}ntj |ddd}|!|d}~|*|dSt+|rt|dsbt|drt|drtj,rt|dr|j-}t.|dkrtd|d}n|j/}| r| D]}tj|}|j0||||kd q|1|dS|j0|dd dStd!td"dStd#dSt2|t|jj'd$}ts|*|dSdS)%Nc3s.|]}tjtj|rt|vVqdSr)rbr2isdirrFSDP_MODEL_NAMEr folder_namerrrr s z0Trainer._load_from_checkpoint..z.binc sVg|]'}tjtj|rtjtj|ts'tjtj|tr|qSr)rbr2rarrrirhrcrerrr sz1Trainer._load_from_checkpoint..zCheckpoint found at z* is only supported when using PyTorch FSDPcss|] }tj|VqdSr)rbr2r)rfrrrr s  z!Can't find a valid checkpoint at zLoading model from rDz9You are resuming training from a checkpoint trained with z- of Transformers but your current version is zJ. This is not recommended and could yield to errors or unwanted behaviors.user_content.ptFr2tagrload_optimizerrTzOEnabling FP16 and loading from smp < 1.10 checkpoint together is not supported.r map_location weights_only_smp_is_partialrCractive_adapteractive_adapters load_adapterr zFMultiple active adapters detected will only consider the first adapterr)r>jThe intermediate checkpoints of PEFT may not be saved correctly, consider using a custom callback to save i in corresponding saving folders. Check some examples here: https://github.com/huggingface/peft/issues/96GCould not load adapter model, make sure to have `peft>=0.3.0` installed)r@ prefer_safe)3rrbr2rrjrirhrnrmrlrkrarRlistdirrrbrJrrrrfrom_json_filetransformers_versionrr<rrorr;rrrloadload_state_dictrrOrprrsave_safetensors safetensors load_file_issue_warnings_after_loadrexistsrpr8rorq set_adapterr')rrr config_fileadapter_weights_fileadapter_safe_weights_file weights_fileweights_index_filesafe_weights_filesafe_weights_index_file is_fsdp_ckptadapter_subdirsrcheckpoint_versionr8 load_resultrpro subdir_namepeft_idrrerr s                  zTrainer._load_from_checkpointc Cstd|jjd|jjdtj|jjt}tj|jjt }tj|jjt }tj|jjt }t r:|j n|j}|jrPt|j |jjt|j ddS|jrgt|jjj|j||jjfit}dStj|stj|stj|stj|rd}t rtjtj|jjdrtj|jjtddddS|jjrtj|rtjj|d d }ntj |d dd }d|d <|j!|dd }dSt|rUt"|dst"|drMt"|drMt"|dr|j#d} t$|j#dkrt%dn|j&} tj|stj|rAz |'|jj| Wn%t(y4} z|j)| j*r/d|j)| j+j,d} t(| | d} ~ wwddl-m.} | gg}n7t%dt dd}n+t%dd}n#|jjrjtj|rjtjj|d d }ntj |d dd }|!|d}t s|r|/|dSdSdStjtj|jjt0stjtj|jjt1rt2||jjt d }t s|/|dSdSt%d|ddS)NzLoading best model from z (score: rHrTrgFrhrrrkrnrCrorprqrr zCDetected multiple active adapters, will only consider the first onez0When using prompt learning PEFT methods such as z, setting load_best_model_at_end=True can lead to errors, it is recommended to set this to False and to load the model manually from the checkpoint directory using PeftModel.from_pretrained(base_model, ) after training has finished.)_IncompatibleKeysrrrsrtz#Could not locate the best model at zi, if you are running a distributed training on multiple nodes, you should activate `--save_on_each_node`.)3rrrpr' best_metricrbr2rrnrlrirhrrNrrCr"rrJrrOrrrrrorrr{r|rr}ryrzr;rpr8r<rorqr. peft_configis_prompt_learning peft_typertorch.nn.modules.modulerr~rkrmr') rbest_model_pathbest_safe_model_pathbest_adapter_model_pathbest_safe_adapter_model_pathrrhas_been_loadedr8roexcmsgrrrrr*] s                   "  zTrainer._load_best_modelcCszt|jdkr(|jjdurt|jt|jjkr|jn td|jdt|jdkr;td|jddSdS)Nrz8There were missing keys in the checkpoint model loaded: rDz;There were unexpected keys in the checkpoint model loaded: ) r8 missing_keysr_keys_to_ignore_on_saver6rrr<unexpected_keys)rrrrrr~ s  z"Trainer._issue_warnings_after_loadc Cs|j|d}|||jj|t|jtjjjrS|sS|j j }| ds(d|}z |j ||W|St yR}zt d|dt|d|d|d}~ww|S)N ignore_keyseval_9The `metric_for_best_model` training argument is set to 'W', which is not found in the evaluation metrics. The available evaluation metrics are: zX. Please ensure that the `compute_metrics` function returns a dictionary that includes 'zM' or consider changing the `metric_for_best_model` via the TrainingArguments.)evaluater/rpr3rrUrr rrr  startswithrLrrr)rrrrrmetric_to_checkrrrrr s,     zTrainer._evaluatec Cs<|jjrf|jj|jkrftrti} || } ||8}t | |jj|jd| d<|durAt |t jr=| n|| d<|durJ|| d<n|| d<|j| 7_|jj|_||| |d} |jjr|||} |j| |d} |jjtjkr| |j_|jjr||||j|j|j|j|_dSdS)NrtrrDr)rr)rw should_logrpr3rrrr_nested_gathermeanrrrrrrrr+r,should_evaluater_determine_best_metricrrrUrra_save_checkpointrYon_save) rrCrDrrrrrrlogstr_loss_scalarris_new_best_metricrrrr s4      z Trainer._maybe_log_save_evaluatecCs|durdS|jjdkr-|jj}tj|d|d}tj|s,td|ddSntj|d}tj|sAtddSt t j |dd }Wdn1sVwYt |d tj |d t j |d tr|t|d |jjtjk}t jrtdt j||trtdt j||trtdt j||trtdt j||trtdt j ||dSdS)Nr rng_state_.pthz$Didn't find an RNG file for process zr, if you are resuming a training that wasn't launched in a distributed fashion, reproducibility is not guaranteed. rng_state.pthzDidn't find an RNG file, if you are resuming a training that was launched in a distributed fashion, reproducibility is not guaranteed.TrmpythonnumpyrrCUDANPUHPUMLUMUSA)!rr process_indexrbr2rrrrrrryrsetstater set_state set_rng_staterrrDrerEcuda is_availablerLrnpurhpurmlurmusa)rrrrng_filecheckpoint_rng_stateis_distributedrrrr" sJ     zTrainer._load_rng_statec Csd}|jjdurt|jj}|dsd|}z||}Wnty8}ztd|dt|d|d}~ww|jjr@tjntj }|j j durW|jjrQt dnt d|j _ |||j j rt||j _ |jj tjtjfvrr|j j|j _d }|S) z Determine if the model should be saved based on the evaluation metrics. Returns: bool: True if a new best metric was found, else False FNrrrzJ. Consider changing the `metric_for_best_model` via the TrainingArguments.z-infinfT)rr rrrrgreater_is_betterrgreaterlessrprrrrUSTEPSEPOCHr3best_global_step)rrrrr metric_valueroperatorrrrrM s4       zTrainer._determine_best_metricc Csztd|jj}|jdur|dur||j|d}tj||}|j |dd|j j t j t jfvrR|jjrRtd|jj}tj||}tj|rR||j_|j jse|||||||j jrdd|jj|jgDD]#}|jj} |} t|jj| tr|jj| | qv| |jj| <qv|j tj|t!|j j"r|#||j jr|j$d|ddSdS) NrF)rTr0cSr rr r rrrr rz,Trainer._save_checkpoint..Fr)%rMrpr3ryr+r-rbr2rr4rrrUrrrrr'save_only_model_save_optimizer_and_scheduler _save_scaler_save_rng_staterarYrrwr2r3rrrrVr5r6r__push_from_checkpoint_rotate_checkpoints) rrrcheckpoint_folderrXrbest_checkpoint_folderbest_checkpoint_dirrcb_namecb_staterrrrs s<       zTrainer._save_checkpointc Csttjtjd}tjr+|jj t j kr#tjj |d<ntjj|d<t r4t|d<trO|jj t j krGtjj |d<ntjj|d<trj|jj t j krbtjj |d<ntjj|d<tr|jj t j kr}tjj |d<ntjj|d<tr|jj t j krtj |d<ntj|d<tj|dd |jjd krt|tj|d dSt|tj|d |jjd dS)N)rrrrrrrrrTrr rrr)rgetstater get_stater get_rng_staterrrrDrerEget_rng_state_allrrrrrrrrrrrbrcrr7r2rr)rr rng_statesrrrr s<   &zTrainer._save_rng_statec Csvtrhtd|jr1|j|jd}tj|t j |d|j j d|j jdtddnt|jt j |ttjdd }t|jt j |tt|Wdn1sbwYntr|jjdd }ttd kstjjjrtj|t j |tdtjjjd n]|jrd tt |j!j"j#$v}|rt%|jr|j!j"|ddn<|j!"|n5|j&rt'|j(jj)|j(|j|fit*t+|j(jj)|j(|j|j|n|j j,rt-|jt j |t|jot.|jt/ }|j j,r5|jr|r9ts7tjdd }t-|jt j |tWdn 1s*wYt|dSdSdSdS)Nsaving_optimizer_states)rTshard_metadatar-of-rFF master_onlyTrecord)gather_if_shardr)rv3exclude_frozen_parameters)r)0rrr(rrTr8rget_shard_metadatar7rbr2rrrrr9r/catch_warningsrUr:rJrlocal_state_dictrorrdp_rankrprqshard_optimizer_staterCr6rrrNsave_checkpointrrrrJrrOrrrrarrr)rroptmcaught_warningsopt_state_dict accept_exclude_frozen_parametersis_deepspeed_custom_schedulerrrrr s     z%Trainer._save_optimizer_and_schedulerc s:durdS|jr;t|jts9tjdd}|jtjt j t ddWdn1s0wYt |dStrJtt j tdn)t j t j tpst j t j tpst j ostfddt D}|jrtt j d|jjd tn|}|rt j t j t rtr|jrtjt j d |jjd |jjd td dd }|d}n tjt j td dd }tjdd}tjt j t d dd }Wdn1swYt |t||jjt||jj|j||j|dStr6t j t j dr)fdd}nfdd}|j |n6|jjdkrA|jjnd }|j!r[t"|j#j$j%|j#|j|j&fit'n|jtjt j t|dd tjdd}|jtjt j t ddWdn 1swYt |dSdSdS)z3If optimizer and scheduler states exist, load them.NTrr_*c3s8|]}tjtj|rtdd|vVqdS)rDrN)rbr2rarOPTIMIZER_NAME_BINrrcrrrr1 s z8Trainer._load_optimizer_and_scheduler..z rank*-of-rFrrrrkrTrgcs"|tjtjtdddS)NTr)rzroryrbr2rr9modoptrrr opt_load_hook_ s"z.opt_load_hookcsJtr|tjtjtddddS|tjtjtdddS)NT)r back_compatr)rnrzroryrbr2rr9rrrrrd s "r )(rCrrUrr/rrzrryrbr2rr:rJrglobr9rrrarRrvrrrrrrsend_cpu_data_to_devicerrTrNregister_post_step_hookrJrrOrprrr)rrrcheckpoint_file_existsoptimizer_statelr_scheduler_staterrlrrrr s   &     z%Trainer._load_optimizer_and_schedulercCsz|jj}Wn tyYdSw|durdStrHtdtjdd}t|jj t j |t t|Wdn1sCwY|jjrzts|tjdd}t|jj t j |t Wdn1sowYt|dSdSdS)Nsaving_scaler_stateTr)rOscalerrrrr(r/rr7r8rbr2r SCALER_NAMErJrrar)rrrrrrrr s&      zTrainer._save_scalercCs|durdStjtj|t}|r{trNtjdd}tj tj|tddd}Wdn1s4wYt |t ||j j|jj|dStjdd}|jjtj tj|tddWdn1spwYt |dSdS)z If scaler state exists, load it.NTrrrkr)rbr2rrrrr/rrryrJrrrrrOrrz)rrrr scaler_staterrrr s( zTrainer._load_scalerc sN|jjsdSg}g}|jj|jg}|jjD]o\}t|t s$|g}t fdd|Drfdd|D}t ||D]>\}}| di}| di} t |d i|} | D] \} } t| | | qZt|trn| |_n|| |jt | q=tdq|qt|d krtd d |d |D]}|j|qdS)zLIf callback states exist and were passed in, restore their states if enabledNc3s|] }|jjkVqdSrr2r3rrstored_callbackrrr sz/Trainer._load_callback_state..csg|] }|jjkr|qSrrr r rrr sz0Trainer._load_callback_state..r attributeszPContinuing training from checkpoint, restoring any callbacks that were passed inrzPCheckpoint included callbacks not included in current configuration. Ignoring. (rrr)r'restore_callback_states_from_checkpointrYrrwrprr rrrRziprrrr7rVrrrr8r<rrZ) r not_found new_callbacksoriginal_callbacksrh duplicatesr callback_datarr  new_callback attributerrr rr s>         zTrainer._load_callback_stateminimizerrn_trials directionbackendrr#c Ks|durt}t|}t|}|||_|jdur td|dur'|jn||_||_ |dur3t n||_ |j |||fi|} d|_| S)az Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided, the sum of all metrics otherwise. To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom optimizer/scheduler. Args: hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*): A function that defines the hyperparameter search space. Will default to [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or [`~trainer_utils.default_hp_space_sigopt`] depending on your backend. compute_objective (`Callable[[Dict[str, float]], float]`, *optional*): A function computing the objective to minimize or maximize from the metrics returned by the `evaluate` method. Will default to [`~trainer_utils.default_compute_objective`]. n_trials (`int`, *optional*, defaults to 100): The number of trial runs to test. direction (`str` or `List[str]`, *optional*, defaults to `"minimize"`): If it's single objective optimization, direction is `str`, can be `"minimize"` or `"maximize"`, you should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics. If it's multi objectives optimization, direction is `List[str]`, can be List of `"minimize"` and `"maximize"`, you should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics. backend (`str` or [`~training_utils.HPSearchBackend`], *optional*): The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending on which one is installed. If all are installed, will default to optuna. hp_name (`Callable[["optuna.Trial"], str]]`, *optional*): A function that defines the trial/run name. Will default to None. kwargs (`Dict[str, Any]`, *optional*): Additional keyword arguments for each backend: - `optuna`: parameters from [optuna.study.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html) and also the parameters `timeout`, `n_jobs` and `gc_after_trial` from [optuna.study.Study.optimize](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.Study.html#optuna.study.Study.optimize) - `ray`: parameters from [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run). If `resources_per_trial` is not set in the `kwargs`, it defaults to 1 CPU core and 1 GPU (if available). If `progress_reporter` is not set in the `kwargs`, [ray.tune.CLIReporter](https://docs.ray.io/en/latest/tune/api/doc/ray.tune.CLIReporter.html) is used. - `sigopt`: the parameter `proxies` from [sigopt.Connection.set_proxies](https://docs.sigopt.com/support/faq#how-do-i-use-sigopt-with-a-proxy). Returns: [`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]: All the information about the best run or best runs for multi-objective optimization. Experiment summary can be found in `run_summary` attribute for Ray backend. NzXTo use hyperparameter search, you need to pass your model through a model_init function.) rrQrensure_availableryrr.default_hp_spacerr#rYrr^) rrrrrrr#r backend_objbest_runrrrhyperparameter_search s @  zTrainer.hyperparameter_searchrrcCs|jjdur |jj|d<|jjr#|jj|d<|dur#td||jjdi|d|jji}|jj||j |j|j|j ||_ dS)a8 Log `logs` on the various objects watching training. Subclass and override this method to inject custom behavior. Args: logs (`Dict[str, float]`): The values to log. start_time (`Optional[float]`): The start of training. Nrrr*)rrL) rprrrrrcr3 log_historyrVrYon_logrw)rrrrrrrr,/s  z Trainer.logrhcst|trt|fdd|DSt|ttfr(t|fdd|DSt|tjrVdjj i}j rNt |sAt |rN| djjjji|jdi|S|S) z| Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors. csi|] \}}||qSr_prepare_inputrrrrrKrJz*Trainer._prepare_input..c3|]}|VqdSrr")rrrrrrMz)Trainer._prepare_input..rrNr)rrrr rrrrrrrCis_floating_point is_complexrWrOrprrrr)rrhrrrrr#Fs   zTrainer._prepare_inputrQcCsR||}t|dkrtdd|jd|jjdkr'|jdur'|j|d<|S)z Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and handling potential state. rzThe batch received was empty, your model won't be able to train on it. Double-check that your training dataset contains keys expected by the model: r<rDNmems)r#r8rrrjrrrrrQrrrrGXs    zTrainer._prepare_inputscCs|S)zF A helper wrapper to group together context managers. )autocast_smart_context_managerrrrrcompute_loss_context_managerhsz$Trainer.compute_loss_context_managerr=cCs*|jrtjjj||jd}|St}|S)z A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired arguments, depending on the situation. )r=r)rlrrrrIrtrr)rr= ctx_managerrrrr*ns z&Trainer.autocast_smart_context_managercCs|t|jdrt|jjr|j||}tr0t|||jj}| |jj S| |j|||d}Wdn1sGwY~|jjdur|jj|jjdkrtrftjn4trotjn+trxtjn"trtjntddrtjntrt dntj!i}|jj"t#j$t#j%fvr|&|d<|jj'd kr|(}|j)rt*+||j }|,WddS1swYdS|j-s|j.dur||jj}|j/j0t1j2krd |d <|j/j,|fi|| S) aq Perform a training step on a batch of inputs. Subclass and override to inject custom behavior. Args: model (`nn.Module`): The model to train. inputs (`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument `labels`. Check your model's documentation for all accepted arguments. Return: `torch.Tensor`: The tensor with training loss on this batch. r*rONrz2.0) min_versionzW`torch_empty_cache_steps` is set but HPU device/backend does not support empty_cache().rr F scale_wrt_gas)3r*r;rTrdrGrrrr reduce_meandetachrrr+ compute_losstorch_empty_cache_stepsrpr3rrxpu empty_cacherrrrrrrmpsrrr<rr rdrrrrcrrkr scale_lossbackwardrQrrOr rr )rrrQrOloss_mbrr scaled_lossrrrrzsV              " zTrainer.training_stepc Cs|jdus |jdurd|vr|d}nd}|jr)i}|dur#||d<i||}|d i|}|jjdkr=||jj|_|durz|j|}t |rR|j j } n| } |jdurd|j|||d} nA| t vrs|j||dd} n2|||} n+t|trd|vrtd d |d d |d t|tr|dn|d} |jjr|js|jr|dur| |jj9} |r| |fS| S)z How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. NlabelsrOrr-T) shift_labelsrzJThe model did not return a loss from the inputs, only the following keys: r<z,. For reference, the inputs it received are rDr)rvrrrQrrrrOr(rrr _get_namer)r7rrrrraverage_tokens_across_devices num_processes) rrrQreturn_outputsrOr; loss_kwargsoutputsrrrrrrr2sN         zTrainer.compute_losscCs |jjdkS)z Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several machines) main process. r)rlocal_process_indexrrrrrs zTrainer.is_local_process_zerocCstr tdkS|jjdkS)z 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 `True` for one process). r)rrorrrrrrrrs  zTrainer.is_world_process_zerorr1cCs~|dur|jj}tr||ntr9tj|dd|j}|jj r+|j ||dt r8t tj |dnt|jrcdt|jjjjvrbtttdkrb|j|j}|jj rb|j ||dnJ|jrz|j|j}|jj ry|j ||dWn2tytd|jj r|j |idt |jj |t!t"g|j#|Yn w|jj r| ||jj$r|s|j$d d dSdSdS) z Will save the model, so you can reload it using `from_pretrained()`. Will only save from the main process. NTr)r8rgFULL_STATE_DICTz0.24.1z| stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use zero_to_fp32.py to recover weightsz Model save)commit_message)%rrr _save_tpurrbrcrNr8ra_savernrr2rtouchrJrrOrprstate_dict_typerraccelerate_versionget_state_dictrrCr$rrr<rKrnrlrr_)rrr1r8rrrr4sN     zTrainer.save_modelc Cs2|dur|n|jj}td||j}ttjddr2tj |ddt |jtj |ttf}td|jr||d}tj |d|jjd |jjd t}tj ||dd td |jjrd dlm}|tj |ddtdd\}}|jj}|j|} t| |r| j||tj |jjdndtdt |tj |tnSt||st|j||r|j|j||jjt |tj |jjdn,tdt |} t | tj |tn|j||jjtj |jjt |d|j!dur|jjr|j!|dSdSdS)NSaving model checkpoint to F)localTrsaving_checkpoint)rrrrrFrsave_full_checkpointsr)%consolidate_sharded_model_checkpointsrz rank*-of-*-) ckpt_prefix ckpt_suffixr4)r8 save_functionsafe_serializationETrainer.model is not a `PreTrainedModel`, only saving its state dict.)is_main_processr8rSrT)rVrSrTr8)"rrrrrrris_master_ordinalrbrcrr7r2rTRAINING_ARGS_NAMErqr(rr8rrrrnrarrPr/rOr(rsave_pretrainedr{_maybe_convert_to_cpur) rrrsupported_classesckpt ckpt_pathrPfull_state_dictrErr8rrrrFDsv              zTrainer._save_tpucCsh|dur|n|jj}tj|ddtd|tstfnttf}t |j |sr|dur2|j }t |j |j |rL|j |j j|||jjdn1td|jjrftjj|tj|tddidnt|tj|tn |j j|||jjd|jdur|j|n|jdurt|jd r|jjdurtd |jj|t|jtj|tdS) NTrrL)r8rTrUrpt)rrzWSaving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`)rrrbrcrrrr&rrrr8rOr(rYr{r|r save_filer2rrlr7rnrrr;rrX)rrr8r[rrrrGs:          z Trainer._savecCs\|jjtjkr |jjt|jg|jjd 7_d|_dS|jj|j7_d|_dS)Nrr) rrDrerErprr@rxrr%rrrrrr+s  zTrainer.store_flosc Csg}ddt||dD}|D]1}|r#|tj||fqtd|d|}|durD|durD|t |d|fqt |}dd|D}|j j durt t|j j |vr|t t|j j } t| t|dD]} || d || || <|| d <qt|S) NcSs g|] }tj|rt|qSr)rbr2rar)rxrrrrs z/Trainer._sorted_checkpoints..-*z.*z -([0-9]+)rcSr3)r r)rrrrrrr7rr )rrrVrbr2getmtimerematchgroupsrsortedrpr'rindexrr8) rrcheckpoint_prefixrordering_and_checkpoint_pathglob_checkpointsr2 regex_matchrYbest_model_indexrPrrrr.s" $zTrainer._sorted_checkpointscCs|jjdus |jjdkrdS|j||d}t||jjkrdS|jj}|jjdur9|jjdkr9|d|jjkr9d}tdt||}|d|}|D]}td|dt j |dd qJdS) Nrrr rrrrTr) rr/r.r8rpr'rbrrr1r2)rrrrYr/number_of_checkpoints_to_deletecheckpoints_to_be_deletedrrrrrs    zTrainer._rotate_checkpointsrrmetric_key_prefixc Cs|du}|r|n|j}t|tr3i}|D]\}}|j|r |n|||d|d}||q|S|j||} |j rDt | } t } |j j rO|jn|j} | | d|jdur\dnd||d} |j j|j j} |d| jvr|| | j|d7} |d| jvr| | j|d7} | jt|| | jt| j| d || jtj|j jvrtt|j !|j |j"|j#| j|_#|j$| j| jS) a Run evaluation and returns metrics. The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init `compute_metrics` argument). You can also subclass and override this method to inject custom behavior. Args: eval_dataset (Union[`Dataset`, Dict[str, `Dataset`]), *optional*): Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each dataset, prepending the dictionary key to the metric name. Datasets must implement the `__len__` method. If you pass a dictionary with names of datasets as keys and datasets as values, evaluate will run separate evaluations on each dataset. This can be useful to monitor how training affects other datasets or simply to get a more fine-grained evaluation. When used with `load_best_model_at_end`, make sure `metric_for_best_model` references exactly one of the datasets. If you, for example, pass in `{"data1": data1, "data2": data2}` for two datasets `data1` and `data2`, you could specify `metric_for_best_model="eval_data1_loss"` for using the loss on `data1` and `metric_for_best_model="eval_data2_loss"` for the loss on `data2`. ignore_keys (`List[str]`, *optional*): A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions. metric_key_prefix (`str`, *optional*, defaults to `"eval"`): An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "eval_bleu" if the prefix is "eval" (default) Returns: A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The dictionary also contains the epoch number which comes from the training state. NrE)rrrp EvaluationT)rprediction_loss_onlyrrp_jit_compilation_time_model_preparation_timerr)%rrrr rrWr(r)rrr$rrrprediction_loopevaluation_looprrrrrcrmathceilr,rr"r'rr#r$r%rY on_evaluaterprwr)rrrrpoverridereval_dataset_name _eval_datasetdataset_metricsrr eval_looprtotal_batch_sizerrrrsX-        zTrainer.evaluaterc Cs|j||}t}|jjr|jn|j}||d||d}|jj|jj }|d|j vr:||j |d7}|d|j vrL||j |d7}|j t |||j t|j |d|j|j|j|j|j |_|j|j t|j|j|j dS)a Run prediction and returns predictions and potential metrics. Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in `evaluate()`. Args: test_dataset (`Dataset`): Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the `model.forward()` method are automatically removed. Has to implement the method `__len__` ignore_keys (`List[str]`, *optional*): A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions. metric_key_prefix (`str`, *optional*, defaults to `"test"`): An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named "test_bleu" if the prefix is "test" (default) If your predictions or labels have different sequence length (for instance because you're doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100. Returns: *NamedTuple* A namedtuple with the following keys: - predictions (`np.ndarray`): The predictions on `test_dataset`. - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some). - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained labels). Prediction)rrrprsrtru) predictionsrr)r(r)rrrrrvrwrrrrWrcrrxryrY on_predictrprwrrSrr) rrrrptest_dataloaderrrrrrrrpredict^s. $  zTrainer.predictrrcCs|j}|dur |n|j}|jr|jdurt|ddd\}}|j|jd|d}t|jj dkrp||jurpt } |jsC|j rI|jj dkrI|j |n|jj|dd}tt | d |_|j ra||_||juri||_|jrp|j|_|js|jr|jtj|jd }n |jr|jtj|jd }|jj} td |d t|rtd ||ntdtd| |t |j!drt"|j!jr|j!||j#_$t%|dd} |j&dkrd|_'t(|jj)dd} t(|jj)dd} t(|jj)dd}t(|jj)dd}d}i}d}t*|D]N\}}t+|}|dur ||7}| dur |} |j,||||d\}}}t%|jdd}d|j-vr@|.||nd}t/rJt01|dur\|2|3| }| 4||dur~|jj5|ddd}|2|}|jj6ry|dkr~|4||dur|jj5|ddd}|dur|jj5|ddd}|j7dur|7||}|2|}|jj6r|dkr| 4||dur|2|}|jj6r|dkr|4||j#8||j9|j:|_:|jj6r,|j;dur!|dur!|dur!|jjd(||d||d}~~~~tj?@q|jAdurU|d|jAdkrU| B| B|B|B~~~~tj?@q|jjC|_2|j&rkt |drktD|d| E} | E} |E}|E}t| rt| }n tF| tGrt%| d ddkr| j}n t|r||}n|}|dkr|dkr|}|j;dur| dur|dur|jj6sd|j-vr| nd|d<d|j-vr|nd|d<|;t>d(| |d|}n|duri}tH|}tF| tIr| rtJK| LM||d!<ntF| tJjNr!| LM||d!<t |d"r/|jO||d#<t |d$r=|j||d%<tI|PD]}|Q|d&sZ|R|||d&|<qCtS| |||d'S)) Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`. Works both with or without labels. NrTr inferenceFrrrevaluation_modertrr ***** Running  *****rz Num examples: Unknown Batch size = rr padding_indexrrrrQr )dim pad_indexrrlossesrrrrr_lossrrsmodel_preparation_timertrErrrrr)TrrrrCr$r!rrr8rO_modelsrrJrr prepare_modelrrrNr%rGrrfloat16rrHrsrrrr^rrr;rTrdrYrr4rrr:eval_do_concat_batchesrrBprediction_stepinclude_for_metricsr#rrrgather_functionrepeatrpad_across_processesrron_prediction_steprprwrrend_of_dataloaderrPrr5eval_accumulation_stepsto_cpu_and_numpygather_for_metricsr& get_arraysrr;rZrr concatenaterrrrrrrrO)rrrrrrrprrErrrr all_losses all_preds all_labels all_inputsreval_set_kwargsobserved_num_examplesrLrQobserved_batch_sizerrr;r inputs_decode is_last_step batch_kwargsrrrrrrws                                         zTrainer.evaluation_loopcCs||durdStr|durd}t||}|Strt|}|S|jjdur,|jjjdks8|jjdur<|jjdkr. return_lossNTrkeys_to_ignore_at_inferencepast_key_valuesc3r$rr)rrrrrrr%r rc3&|]\}}|dgvr|VqdSrNrrrrrr$c3 |] \}}|vr|VqdSrrrrrrr)r@c3rrrrrrrrrc3rrrrrrrrrr)r8rzallrrrrGr;rr4rrGrrrJrrrrr r0r1rrr+r2rrrr)rrrQrrr has_labelsrloss_without_labelsr; raw_outputsr9 logits_mbrrrBr)rrQrrsr*                 *  zTrainer.prediction_stepcCst|jdr |j|SdS)a For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point operations for every backward + forward pass. If using another model, either implement such a method in the model or subclass and override this method. Args: inputs (`Dict[str, Union[torch.Tensor, Any]]`): The inputs and targets of the model. Returns: `int`: The number of floating-point operations. rr)r;rrr)rrrrs zTrainer.floating_point_opstokencCsn|sdS|jjdurt|jjj}n|jj}|dur |n|jj}t|||jj dd}|j |_d|_ dS)zE Initializes a git repo in `self.args.hub_model_id`. NT)rprivater) rrr^rrabsoluter hub_tokenr hub_private_reporepo_idpush_in_progress)rr repo_namerepo_urlrrrr`s  zTrainer.init_hf_repolanguagelicensetagsrfinetuned_fromtasks dataset_tags dataset_argsc  Cs|sdStj|jjd} d} tj| rIt| j d} | dk} t| j j } |durI| durIt |t r;|g}| D] }||vrH||q=tj|||||||||| d }|}t| d }||Wdn1sqwY| r|j|j|jjdSdS)a Creates a draft of a model card using the information available to the `Trainer`. Args: language (`str`, *optional*): The language of the model (if applicable) license (`str`, *optional*): The license of the model. Will default to the license of the pretrained model used, if the original model given to the `Trainer` comes from a repo on the Hub. tags (`str` or `List[str]`, *optional*): Some tags to be included in the metadata of the model card. model_name (`str`, *optional*): The name of the model. finetuned_from (`str`, *optional*): The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo of the original model given to the `Trainer` (if it comes from the Hub). tasks (`str` or `List[str]`, *optional*): One or several task identifiers, to be included in the metadata of the model card. dataset_tags (`str` or `List[str]`, *optional*): One or several dataset tags, to be included in the metadata of the model card. dataset (`str` or `List[str]`, *optional*): One or several dataset identifiers, to be included in the metadata of the model card. dataset_args (`str` or `List[str]`, *optional*): One or several dataset arguments, to be included in the metadata of the model card. Nz README.mdF library_namer) rrrrrrrrrw)rrbr2rrrrr ryrhrrrrrVr% from_trainer to_model_cardopenwriterOr(rcreate_or_update_model_card)rrrrrrrrrrmodel_card_filepathis_peft_libraryr existing_tagsritraining_summary model_cardrfrrrcreate_model_cardsD%     zTrainer.create_model_cardc CsB|r |jjtjkr dS|jjs|jdur|jsdS|jj}t t t g}t t fD]>}tj||}tj|rh||t|}t|}Wdn1sTwYtt|d}||q*trt|tttg|D]} tjtj|| rt tj|| tj|| qv|j!dur|j!"|t#$|jtj|t%|jj&t'j(krd|j)j*} n dt+|j)j,} t-|j.|| |jj/ddt0dgd} | g} |jjtj1tj2fvr|jjtj1krdnt3|j4} t-|j.|| | d |jj/dd }| ||jdus|jrt5| |_dS|jj6| dS) N weight_mapzTraining in progress, step zTraining in progress, epoch Trrb)r folder_pathrEr run_as_futureignore_patternszlast-checkpointz , checkpoint)rr path_in_reporErr)7rr hub_strategyrRENDhub_always_pushris_donerrjrnrlrmrkrbr2rrrVrjsonloadsreadrr6r7extendrrgrirhr1rrrYrr7rXrrUrrpr3rrr r^rrM CHECKPOINTALL_CHECKPOINTSrrrpjobs)rrrmodeling_files index_file index_pathrfrh shard_files modeling_filerEmodel_push_job push_jobsrcheckpoint_pushrrrr^sh            zTrainer._push_from_checkpointcCsBt|dsdS|jdur|jstd|jdSdSdS)Nrz\Waiting for the current checkpoint push to be finished, this might take a couple of minutes.)r;rrrrwait_until_donerrrrr3s  zTrainer._finish_current_pushEnd of trainingrEblockingrevisionc Ks6|dd}|dur%|jjr%|jjdurt|jjj}n |jjdd}|dur+|n|jj}|jdur:|j |d|j dd| sFdSt |j ddduryd |vrWg|d <t|d tre|d g|d <|j jD]}||d vrx|d |qi|jd d|i||t|j|jj||| d td g|d S)u Upload `self.model` and `self.processing_class` to the 🤗 model hub on the repo `self.args.hub_model_id`. Parameters: commit_message (`str`, *optional*, defaults to `"End of training"`): Message to commit while pushing. blocking (`bool`, *optional*, defaults to `True`): Whether the function should return only when the `git push` has finished. token (`str`, *optional*, defaults to `None`): Token with write permission to overwrite Trainer's original args. revision (`str`, *optional*): The git revision to commit from. Defaults to the head of the "main" branch. kwargs (`Dict[str, Any]`, *optional*): Additional keyword arguments passed along to [`~Trainer.create_model_card`]. Returns: The URL of the repository where the model was pushed if `blocking=False`, or a `Future` object tracking the progress of the commit if `blocking=True`. rN/r)rTr0 model_tagsrrrb)rrrErrrrr)rrrar^rrrrrr`r4rr4rrrrrVrr3r rM)rrErrrrr model_tagrrrr_s>        zTrainer.push_to_hubc&Cs|j}t|s td|dur|n|j}|jr%|jdur%t|ddd\}}|j|jd|d}t |j j dkrd||jurd|jsA|j rG|j |n|j j|dd}|j rU||_||jur]||_|jrd|j|_|js|jrt|jtj|jd }n |jr|jtj|jd }t|d dr|jn|j} | durtd ||} td |d td| td| d} d} d} d}d}i}td|j}t || | d}|sd}t!|drt"|j#t$r|j#j}t || |d}t || |d}t || |d}|%t!|j&dr t'|j&j%r |j&%|j(dkrd|_)||j*_+t,|D]\}}|j-||||d\}}}t|jdd}d|j.vr@|/||nd}|dur\|0| }| durS|ntj1| |fdd} |duro| durh|nt2| |dd} |dur| dur{|nt2| |dd} |dur|dur|nt2||dd}|j*3||j4|j5|_5|jj6r|j7dur| dur| dur|j j8j9}i} d|j.vr| nd| d<d|j.vr|nd| d<|j7t:d(| | d| |d}|jj6s|j;dur.|d|j;dkr.|<|=| d|s|<|=| d |<|=| d!|<|=|d"~ ~ ~ ~tj>?d#\} } } }q|j(r?t!|d$r?t@|d$|<|=| d|sf|<|=| d |<|=| d!|<|=|d"|A}!|sq|And}"|sz|And}#|s|And}$|j7dur|"dur|#dur|jj6sd|j.vr|!nd|d<d|j.vr|$nd|d<|7t:d(|"|#d|}n|duri}tB|}|!dur|!CD||d%<tE|FD]}%|%G|d&s|H|%||d&|%<qtI|"|#|| d'S))rz+dataloader must implement a working __len__NrTrFrrr_is_accelerate_preparedz\Batch size cannot be None. Ensure the dataloader has a valid batch_size or total_batch_size.rrrrr )make_multiple_ofrrrrrrQ)rrrrrrr eval_losses eval_predseval_label_idseval_inputs_ids)NNNNrrrErr)Jrr^rrrrCr$r!rrr8rOrrJrrrNr%rGrrrrrHrsr4rrrrrrbrr9r;rrr?rrTrdrrrYrrrrr#rcatrFrrprwrrrrrPr add_arrays_gather_and_numpifyrr5r&finalizerZrrrrrrrO)&rrrrrrrprrErrr losses_host preds_host labels_host inputs_hostrrreval_losses_gathererr preds_gathererlabels_gathererinputs_gathererrLrQrrr;rrrrr eval_losspredsr inputs_idsrrrrrvs                            zTrainer.prediction_loopcCsX|durdStrt||}t |Strt|}t |S|jjtjkr(t|}t |S)rN) rrIrrrrDrerErArHrrrrrs zTrainer._gather_and_numpifycCsB|sdSddg}tjtj|jjdr7ttj|jjd }|}Wdn1s1wYnd}|}|D]}||vrT| drM||7}q=|d|7}q=||krttj|jjdd}t d|| |Wdn1s}wY|j dtd |js|jd |jdSdS) z8Add SageMaker Checkpointing patterns to .gitignore file.Nz*.sagemaker-uploadingz*.sagemaker-uploadedz .gitignorer rz"Writing .gitignore file. Content: g?z'Add *.sagemaker patterns to .gitignore.)rrbr2rrrepo local_dirrrrPrr'rgit_addrsleep is_repo_clean git_commitgit_push)rpatternsrfcurrent_contentcontentpatternrrr_add_sm_patterns_to_gitignores6        z%Trainer._add_sm_patterns_to_gitignorec si}tdr|jjjdur|jjj}d|vr&|jjdkr td|d|j_|jjtdrKgd}td"ifdd|D}tdrK|jj|_ d }td s[|rZt d n|rf|jj sft d ||_ d d|jji}tdr}||d<n||jjdkrd|_tttdkrt|jjd|d<ntdtd"i||_|jj|_dt|jjvrtj |j|jj!d|_t"|jj#dddu|_$t"|jj#dddu|_%t"|jj#dddu|_|j%r|jj#j&}dD]}t'|||jj()|t"||q|j*r|jj+rtd|j$r#t"|jdddur#|,|jj-rD|j$s0|j%rD|jj.rD|j$r;dnd}t|d|j$rZ|jj#jj/dkrZ|jj0rZtd|jj-rr|j%rtd t1|jj#j&j2vrvtd!dSdSdS)#Nrrr zThe `AcceleratorConfig`'s `num_steps` is set but `gradient_accumulation_steps` is greater than 1 in the passed `TrainingArguments`If using the passed `AcceleratorConfig` is desired, do not set the `TrainingArguments` `gradient_accumulation_steps`.) split_batchesdispatch_batches even_batchesuse_seedable_samplercsi|]}||qSr)r)rraccelerator_configrrrrz>Trainer.create_accelerator_and_postprocess..z1.1.0 non_blockingrzp`non_blocking` is only supported in accelerate v0.30.0 and above. Please upgrade accelerate to use this feature.zx`non_blocking` is enabled but `dataloader_pin_memory` is not. For the best performance, it's recommended to enable both.gradient_accumulation_kwargsrdataloader_configTr)tp_sizetorch_tp_pluginz4Requires accelerate>1.3.0 to use Tensor Parallelism.use_gather_object)r5r)limit_all_gathersactivation_checkpointingzThe activation_checkpointing in FSDP config and the gradient_checkpointing in training arg can't be set to True simultaneously. Please use FSDP's activation_checkpointing logic when using FSDP.r DeepSpeedrzJ can't be used with `save_only_model` along with `load_best_model_at_end`.zo`auto_find_batch_size` isn't supported yet with DeepSpeed Zero-3. Please consider using Zero-2, Zero-1, or FSDPSHARDED_STATE_DICTzWsave_only_model option is not compatible with FSDP state dict type 'SHARDED_STATE_DICT'r)3rtrr/r1rrto_dictr data_seedrr=rrr<r0rrWr3 is_tp_enabledrrrJrrrOrrrrrrrreval_use_gather_objectr4rprCrJrrrArr7rrrr zero_stagerrrI) rgrad_acc_kwargsrr2r0rrrwrapperrr.rr&s               z*Trainer.create_accelerator_and_postprocesscCsBddlm}|jjj}||jj|_|jj|_|j|j |dS)zO Sets values in the deepspeed plugin based on the Trainer args rrN) rrrOrprrrdeepspeed_configrr)rrr ds_pluginrrrrYs   z#Trainer.propagate_args_to_deepspeedcCs|jrOt|jrQddlm}ddlm}t|jj|r$||j|j j j _ t |jddtjkrS|jjjjjrUtttdkrW|j j j j|jjjjdddSdSdSdSdSdS)Nr) PeftConfig)fsdp_auto_wrap_policyrz0.27.0T)r{)rJrrrrDpeft.utils.otherrEractive_peft_configrOrprrr4rrKrr?bnb_4bit_quant_storager&rrrJset_mixed_precision)rrDrErrrres      z"Trainer._fsdp_qlora_plugin_updatesc Csg}d}t|D]}z |t|WqtyYnwt|dko1d|dvo1|jp1|jdu}|rKz tdd|D}Wn tt fyJYnw|durv|j j r[|j |}t|rv||}|j jdkrv|dkrv|d}||fS)Nrr;cSsg|] }|ddqS)r;r)ner%)rrrrrrsz-Trainer.get_batch_samples..r )rrVrE StopIterationr8rQrr%rrrr>rOrr is_tensorrrcr unsqueeze)rrIrMrrNrOrEcount_num_items_in_batchrrrrus8       zTrainer.get_batch_samplesr5c Cs|j}|dk}t|rt|nd}|dur't||jd}|r't|j|}|rS||}|jdkrD||t ||dk} ||} n,t|j} |||j} n|jdkrht j } |}||j}|j|} nt d|j| ||| |||fS)a Calculates and returns the following values: - `num_train_epochs` - `num_update_steps_per_epoch` - `num_examples` - `num_train_samples` - `epoch_based` - `len_dataloader` - `max_steps` rNr zYargs.max_steps must be set to a positive value if dataloader does not have a length, was ) rer^r8rbrrxryrfrrsysmaxsizer) rrrr5rer8r9r6rrfr7rrrrsD        z#Trainer.set_initial_training_values) NNNNNNNNNNrNN)rNr)F)TN)NNN)NNNNN)NNrrNNr)FN)NF)NNr)Nr) NNNNNNNNN)rTNN)r3 __module__ __qualname____doc__trainer_pt_utilsrrrrrrrr&rModulerfrrrrrrr0r rr-rrPrr6rrr  OptimizerrULambdaLRrrrrpropertyrsetterrrrZrrrLrrrrgrhSamplerrrrrrrrrrrr1r9 parameterrr: staticmethodrrrrrrr/r)r-rVr~r^rprrr*rr-rr*r~rrrrrrrrrrrrQrNrr,r#rGr+r*rr2rrr4rFrGr+rMr.rrrSrrOrwrrrr`rrr3r_rvrr)r&rrrrrrrrr<sp_        ! # -=" C"  9.8 +   r h z  )+&42Am) $T&6  P4 3H+     i  D   W  i  JB  T  ; ' q -r(rSrrrrimportlib.metadatarrrrxrbrrdr1rOr'rr/collections.abcrpathlibrtypingrrrrr integrationsr huggingface_hub.utilsrgrrrrtorch.distributed distributedr)huggingface_hubr r r packagingrrtorch.utils.datarrrrrrrconfiguration_utilsrdata.data_collatorrrr debug_utilsrr!feature_extraction_sequence_utilsrfeature_extraction_utilsrrrrimage_processing_utilsr integrations.deepspeedr!r"r#integrations.tpur$ modelcardr%modeling_utilsr&r'r(models.auto.modeling_autor)r* optimizationr+r,processing_utilsr- pytorch_utilsr.r/tokenization_utils_baser0trainer_callbackr1r2r3r4r5r6r7r8rTr9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrL trainer_utilsrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrerdrerfrgrhrirjrkrlrmrnrorprqrrrsrtrurvrwrxryrzr{r|r}r~rrrrrrrrrrrrrrrrrutils.deprecationrutils.quantization_configrrWr\utils.notebookrr rrtorch_xla.core.xla_modelr xla_modelrtorch_xla.debug.metricsr'rr$ torch_xla XLA_VERSIONrrtorch_xla.distributed.spmdspmdrtorch_xla.runtimeruntimer!smdistributed.modelparallel.torch modelparallelrosmdistributed.modelparallel SMP_VERSIONrnrrrrsafetensors.torchr|rr acceleraterrrJaccelerate.staterrrrrrrrr DATA_SAMPLERSraccelerate.data_loaderrrrrrrr  get_loggerr3rrXr6r9rrr:rbrrrrrs                 (X d +         $