200 lines
25 KiB
Plaintext
200 lines
25 KiB
Plaintext
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0.21.0
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__UNSLOTH_VERSIONING__
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é )ÚTensorN)Ú
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functional)ÚAnyÚListÚOptionalÚTupleÚUnionÚDictÚSetÚCallable)3r ÚBaseImageProcessorr Ú DataCollatorÚDatasetÚEvalPredictionÚFeatureExtractionMixinÚFrozenInstanceErrorr Ú PartialStateÚPathÚ PeftModelÚPreTrainedModelÚPreTrainedTokenizerBaseÚProcessorMixinÚ RewardConfigÚ RewardDataCollatorWithPaddingÚ
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RewardTrainerÚTrainerÚTrainerCallbackr Ú _tokenizeÚcompute_accuracyÚdecode_and_strip_paddingÚ defaultdictÚdisable_dropout_in_modelÚ
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gather_objectÚgenerate_model_cardÚget_comet_experiment_urlÚinspectÚis_peft_availableÚis_rich_availableÚis_wandb_availableÚ log_table_to_comet_experimentÚmaybe_apply_chat_templateÚ
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nested_detachÚnnÚosÚpdÚ prepare_model_for_kbit_trainingÚprint_rich_tableÚreplaceÚtorchÚwarningsr r r r r& r- r2 )Ú*)Ú dataclassÚfield)ÚVersion)Ú nullcontext)ÚDataCollatorForSeq2SeqÚ DataCollatorForLanguageModelingTF)Úepilogue_fusionÚ max_autotuneÚ
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shape_paddingz
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trace.enabledztriton.cudagraphs)ÚdynamicÚ fullgraphÚoptionsc
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|dd�}|| } | | ¡ q! t |¡}| | jd | jd f¡}|S )Néÿÿÿÿé r )ÚchunksÚdim)rD Úindex©rD é )
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r2 ÚchunkÚreshapeÚshapeÚzipÚtoÚfloat32ÚgatherÚ unsqueezeÚsqueezeÚ logsumexpÚappendÚconcat)
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ÚlogitsrE Úchunked_logitsÚ
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chunked_indexÚall_per_token_logpsÚ chunk_logitsÚ chunk_indexÚselected_logitsÚlogsumexp_valuesÚper_token_logps© r] úT/workspace/DS-LLM-TEMPLATE-FINETUNING/unsloth_compiled_cache/UnslothRewardTrainer.pyÚ chunked_selective_log_softmax" s
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r_ c s† e Zd ZU dZedddid�Zee ed< edddid�Z ee
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! ! " # $ $ % & ' ( # $ ) * + d.‡ fd,d-„ Z ‡ Z
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S )/ÚUnslothRewardConfigaI
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Configuration class for the [`RewardTrainer`].
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This class includes only the parameters that are specific to Reward training. For a full list of training
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arguments, please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this
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class may differ from those in [`~transformers.TrainingArguments`].
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Using [`~transformers.HfArgumentParser`] we can turn this class into
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[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
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command line.
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Parameters:
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max_length (`int` or `None`, *optional*, defaults to `1024`):
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Maximum length of the sequences (prompt + completion) in the batch, filters out entries that exceed the
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limit. This argument is required if you want to use the default data collator.
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disable_dropout (`bool`, *optional*, defaults to `True`):
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Whether to disable dropout in the model.
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dataset_num_proc (`int`, *optional*, defaults to `None`):
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Number of processes to use for processing the dataset.
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center_rewards_coefficient (`float`, *optional*, defaults to `None`):
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Coefficient to incentivize the reward model to output mean-zero rewards (proposed by
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https://huggingface.co/papers/2312.09244, Eq. 2). Recommended value: `0.01`.
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remove_unused_columns (`bool`, *optional*, defaults to `False`):
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Whether to remove the columns that are not used by the model's forward pass. Can be `True` only if the
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dataset is pretokenized.
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NÚhelpzvLLM SamplingParams)ÚdefaultÚmetadataÚvllm_sampling_paramsrA z8Chunk size to reduce memory usage. -1 is most efficient.Úunsloth_num_chunksz'Maximum sequence length to truncate to.Úmax_seq_lengthFÚnorB é r éú ç-C ëâ6
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?ç{®Gáz„?çÍÌÌÌÌÌì?ç+‡ÙÎ÷ï?ç:Œ0âŽyE>ç ð?ç @Úlinearçš™™™™™¹?ÚpassiveÚwarningTÚstepsrG éô éO
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every_saveÚlasté é cˆ Š s´ |dk r t d|› d�ƒ‚|dkrtd|› d�ƒ‚|d u r(|#dkr(|$dkr(d}d }#|ƒd u r:d
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ƒ}ƒtƒ jd’i d|“d|“d|“d|“d|“d|“d|“d|“d| “d|
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“d|“d |“d |“d |“d |“d |“d!|“d"|“d#|“d$|“d%|“d&|“d'|“d(|“d)| “d*| “d+| “d,| “d-| “d.|!“d/|"“d0|#“d1|$“d2|%“d3|&“d4|'“d5|(“d6|)“d7|*“d8|+“d9|,“d:|-“d;|.“d<|/“d=|0“d>|1“d?|2“d@|3“dA|4“dB|5“dC|6“dD|7“dE|8“dF|9“dG|:“dH|;“dI|<“dJ|=“dK|>“dL|?“dM|@“dN|A“dO|B“dP|C“dQ|D“dR|E“dS|F“dT|G“dU|H“dV|I“dW|J“dX|K“dY|L“dZ|M“d[|N“d\|O“d]|P“d^|Q“d_|R“d`|S“da|T“db|U“dc|V“dd|W“de|X“df|Y“dg|Z“dh|[“di|\“dj|]“dk|^“dl|_“dm|`“dn|a“do|b“dp|c“dq|d“dr|e“ds|f“dt|g“du|h“dv|i“dw|j“dx|k“dy|l“dz|m“d{|n“d||o“d}|p“d~|q“d|r“d€|s“d�|t“d‚|u“dƒ|v“d„|w“d…|x“d†|y“d‡|z“dˆ|{“d‰||“dŠ|}“d‹|~“dŒ|“d�|€“dŽ|�“d�|‚“d�|ƒ“d‘|„“|ˆ¤Ž |…| _|†| _|‡| _ d S )“NgH¯¼šò×z>z Unsloth: Your learning rate of `zi` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!rG za` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!ru rv Ú unsloth_training_checkpointsrg r )Ú cpu_countrB rh Ú
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output_dirÚoverwrite_output_dirÚdo_trainÚdo_evalÚ
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do_predictÚ
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eval_strategyÚprediction_loss_onlyÚper_device_train_batch_sizeÚper_device_eval_batch_sizeÚper_gpu_train_batch_sizeÚper_gpu_eval_batch_sizeÚgradient_accumulation_stepsÚeval_accumulation_stepsÚ
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eval_delayÚtorch_empty_cache_stepsÚ
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learning_rateÚ weight_decayÚ
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adam_beta1Ú
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adam_beta2Ú adam_epsilonÚ
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max_grad_normÚnum_train_epochsÚ max_stepsÚlr_scheduler_typeÚ warmup_ratioÚ warmup_stepsÚ log_levelÚlog_level_replicaÚlog_on_each_nodeÚ logging_dirÚlogging_strategyÚlogging_first_stepÚ
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logging_stepsÚlogging_nan_inf_filterÚ
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save_strategyÚ
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save_stepsÚsave_total_limitÚsave_safetensorsÚsave_on_each_nodeÚsave_only_modelÚ'restore_callback_states_from_checkpointÚno_cudaÚuse_cpuÚuse_mps_deviceÚseedÚ data_seedÚ
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jit_mode_evalÚuse_ipexÚbf16Úfp16Úfp16_opt_levelÚhalf_precision_backendÚbf16_full_evalÚfp16_full_evalÚtf32Ú
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local_rankÚ ddp_backendÚ
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tpu_num_coresÚtpu_metrics_debugÚdebugÚdataloader_drop_lastÚ
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eval_stepsÚdataloader_num_workersÚdataloader_prefetch_factorÚ
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past_indexÚrun_nameÚ disable_tqdmÚremove_unused_columnsÚ label_namesÚload_best_model_at_endÚmetric_for_best_modelÚgreater_is_betterÚignore_data_skipÚfsdpÚfsdp_min_num_paramsÚ fsdp_configÚ"fsdp_transformer_layer_cls_to_wrapÚaccelerator_configÚ deepspeedÚlabel_smoothing_factorÚoptimÚ
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optim_argsÚ adafactorÚgroup_by_lengthÚlength_column_nameÚ report_toÚddp_find_unused_parametersÚddp_bucket_cap_mbÚddp_broadcast_buffersÚdataloader_pin_memoryÚ dataloader_persistent_workersÚskip_memory_metricsÚuse_legacy_prediction_loopÚ push_to_hubÚresume_from_checkpointÚ hub_model_idÚ hub_strategyÚ hub_tokenÚhub_private_repoÚhub_always_pushÚ hub_revisionÚgradient_checkpointingÚ gradient_checkpointing_kwargsÚinclude_inputs_for_metricsÚeval_do_concat_batchesÚ fp16_backendÚpush_to_hub_model_idÚpush_to_hub_organizationÚpush_to_hub_tokenÚ
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mp_parametersÚauto_find_batch_sizeÚfull_determinismÚ torchdynamoÚ ray_scopeÚ ddp_timeoutÚ
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torch_compileÚtorch_compile_backendÚtorch_compile_modeÚinclude_tokens_per_secondÚ include_num_input_tokens_seenÚneftune_noise_alphaÚoptim_target_modulesÚbatch_eval_metricsÚ
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eval_on_startÚuse_liger_kernelÚliger_kernel_configÚeval_use_gather_objectÚ average_tokens_across_devicesÚ
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max_lengthÚdisable_dropoutÚdataset_num_procÚcenter_rewards_coefficientr] )
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OverflowErrorÚmultiprocessingrƒ ÚmaxÚsuperÚ__init__rd re rf )ŠÚselfr„ r… r† r‡ rˆ r‰ rŠ r‹ rŒ r� rŽ r� r� r‘ r’ r“ r” r• r– r— r˜ r™ rš r› rœ r� rž rŸ r r¡ r¢ r£ r¤ r¥ r¦ r§ r¨ r© rª r« r¬ r r® r¯ r° r± r² r³ r´ rµ r¶ r· r¸ r¹ rº r» r¼ r½ r¾ r¿ rÀ rÁ r rà rÄ rÅ rÆ rÇ rÈ rÉ rÊ rË rÌ rÍ rÎ rÏ rÐ rÑ rÒ rÓ rÔ rÕ rÖ r× rØ rÙ rÚ rÛ rÜ rÝ rÞ rß rà rá râ rã rä rå ræ rç rè ré rê rë rì rí rî rï rð rñ rò ró rô rõ rö r÷ rø rù rú rû rü rý rþ rÿ r r r r r r r r rd re rf Úkwargsrƒ ©Ú __class__r] r^ r
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Initialize RewardTrainer.
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Args:
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model (`transformers.PreTrainedModel`):
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The model to train, preferably an `AutoModelForSequenceClassification`.
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args (`RewardConfig`):
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The arguments to use for training.
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data_collator (`transformers.DataCollator`):
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The data collator to use for training. If None is specified, the default data collator
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(`RewardDataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of
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the sequences in the batch, given a dataset of paired sequences.
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train_dataset (`datasets.Dataset`):
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The dataset to use for training.
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eval_dataset (`datasets.Dataset`):
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The dataset to use for evaluation.
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processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`):
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Processing class used to process the data. If provided, will be used to automatically process the
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inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted
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training or reuse the fine-tuned model.
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model_init (`Callable[[], transformers.PreTrainedModel]`):
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The model initializer to use for training. If None is specified, the default model initializer will be
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used.
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compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`):
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The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`)
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will be used.
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callbacks (`list[transformers.TrainerCallback]`):
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The callbacks to use for training.
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optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
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The optimizer and scheduler to use for training.
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preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
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The function to use to preprocess the logits before computing the metrics.
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peft_config (`dict`, defaults to `None`):
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The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped
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in a PEFT model.
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