unsloth_compiled_cache/UnslothSFTTrainer.py

"""
2025.7.4
2025.7.3
4.53.2
0.19.1
__UNSLOTH_VERSIONING__
"""
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.sft_trainer import (Any, AutoModelForCausalLM, AutoTokenizer, BaseImageProcessor, Callable, ConstantLengthDataset, DataCollator, DataCollatorForLanguageModeling, Dataset, EvalPrediction, FeatureExtractionMixin, IterableDataset, Optional, Path, PeftConfig, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, Trainer, TrainerCallback, TrainingArguments, Union, clone_chat_template, contextlib, dataclass, dataclasses, defaultdict, generate_model_card, get_act_offloading_ctx_manager, get_comet_experiment_url, get_peft_model, is_conversational, is_peft_available, is_wandb_available, nn, os, pad, peft, peft_module_casting_to_bf16, prepare_model_for_kbit_training, torch, version, wandb, warnings, Callable, ConstantLengthDataset, DataCollator, DataCollatorForLanguageModeling, Dataset, IterableDataset, Optional, Union, os, pad, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, os, peft, torch, os)


import os
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling

torch_compile_options = {
    "epilogue_fusion"   : True,
    "max_autotune"      : False,
    "shape_padding"     : True,
    "trace.enabled"     : False,
    "triton.cudagraphs" : False,
}

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def selective_log_softmax(logits, index):
    logits = logits.to(torch.float32)
    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
    # loop to reduce peak mem consumption
    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
    logsumexp_values = torch.logsumexp(logits, dim = -1)
    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
    return per_token_logps
@dataclass
class UnslothSFTConfig(SFTConfig):
    """
    
    Configuration class for the [`SFTTrainer`].

    This class includes only the parameters that are specific to SFT training. For a full list of training arguments,
    please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may
    differ from those in [`~transformers.TrainingArguments`].

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        > Parameters that control the model

        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`SFTTrainer`] is provided as a string.
        chat_template_path (`str` or `None`, *optional*, defaults to `None`):
            If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory
            or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must
            ensure that any special tokens referenced in the template are added to the tokenizer and that the model's
            embedding layer is resized accordingly.

        > Parameters that control the data preprocessing

        dataset_text_field (`str`, *optional*, defaults to `"text"`):
            Name of the column that contains text data in the dataset.
        dataset_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
            Dictionary of optional keyword arguments for the dataset preparation. The only supported key is
            `skip_prepare_dataset`.
        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
            Number of processes to use for processing the dataset.
        eos_token (`str` or `None`, *optional*, defaults to `None`):
            Token used to indicate the end of a turn or sequence. If `None`, it defaults to
            `processing_class.eos_token`.
        pad_token (`int` or `None`, *optional*, defaults to `None`):
            Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`,
            it falls back to `processing_class.eos_token`.
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the right.
            If `None`, no truncation is applied. When packing is enabled, this value sets the sequence length.
        packing (`bool`, *optional*, defaults to `False`):
            Whether to group multiple sequences into fixed-length blocks to improve computational efficiency and reduce
            padding. Uses `max_length` to define sequence length.
        packing_strategy (`str`, *optional*, defaults to `"ffd"`):
            Strategy for packing sequences. Can be either `"ffd"` (first-fit decreasing, default), or `"wrapped"`.
        padding_free (`bool`, *optional*, defaults to `False`):
            Whether to perform forward passes without padding by flattening all sequences in the batch into a single
            continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only
            supported with the `flash_attention_2` attention implementation, which can efficiently handle the flattened
            batch structure. When packing is enabled with strategy `"ffd"`, padding-free is enabled, regardless of the
            value of this parameter.
        pad_to_multiple_of (`int` or `None`, *optional*, defaults to `None`):
            If set, the sequences will be padded to a multiple of this value.
        eval_packing (`bool` or `None`, *optional*, defaults to `None`):
            Whether to pack the eval dataset. If `None`, uses the same value as `packing`.

        > Parameters that control the training

        completion_only_loss (`bool` or `None`, *optional*, defaults to `None`):
            Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is computed
            only on the completion, which is supported only for [prompt-completion](#prompt-completion) datasets. If
            `False`, loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset:
            loss is computed on the completion for [prompt-completion](#prompt-completion) datasets, and on the full
            sequence for [language modeling](#language-modeling) datasets.
        assistant_only_loss (`bool`, *optional*, defaults to `False`):
            Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is computed
            only on the assistant responses, which is supported only for [conversational](#conversational) datasets. If `False`,
            loss is computed on the entire sequence.
        activation_offloading (`bool`, *optional*, defaults to `False`):
            Whether to offload the activations to the CPU.
    
    """
    vllm_sampling_params: Optional[Any] = field(
        default = None,
        metadata = {'help': 'vLLM SamplingParams'},
    )
    unsloth_num_chunks : Optional[int] = field(
        default = -1,
        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
    )
    def __init__(
        self,
        output_dir = None,
        overwrite_output_dir = None,
        do_train = False,
        do_eval = False,
        do_predict = False,
        eval_strategy = 'no',
        prediction_loss_only = False,
        per_device_train_batch_size = 4,
        per_device_eval_batch_size = 4,
        per_gpu_train_batch_size = None,
        per_gpu_eval_batch_size = None,
        gradient_accumulation_steps = 2,
        eval_accumulation_steps = 2,
        eval_delay = 0,
        torch_empty_cache_steps = 250,
        learning_rate = 5e-05,
        weight_decay = 0.01,
        adam_beta1 = 0.9,
        adam_beta2 = 0.999,
        adam_epsilon = 1e-08,
        max_grad_norm = 1.0,
        num_train_epochs = 3.0,
        max_steps = -1,
        lr_scheduler_type = 'linear',
        warmup_ratio = 0.1,
        warmup_steps = 0,
        log_level = 'passive',
        log_level_replica = 'warning',
        log_on_each_node = True,
        logging_dir = None,
        logging_strategy = 'steps',
        logging_first_step = False,
        logging_steps = 1,
        logging_nan_inf_filter = False,
        save_strategy = 'steps',
        save_steps = 500,
        save_total_limit = None,
        save_safetensors = True,
        save_on_each_node = False,
        save_only_model = False,
        restore_callback_states_from_checkpoint = False,
        no_cuda = False,
        use_cpu = False,
        use_mps_device = False,
        seed = 3407,
        data_seed = 3407,
        jit_mode_eval = False,
        use_ipex = False,
        bf16 = False,
        fp16 = False,
        fp16_opt_level = 'O1',
        half_precision_backend = 'auto',
        bf16_full_eval = False,
        fp16_full_eval = False,
        tf32 = None,
        local_rank = -1,
        ddp_backend = None,
        tpu_num_cores = None,
        tpu_metrics_debug = False,
        debug = '',
        dataloader_drop_last = False,
        eval_steps = None,
        dataloader_num_workers = 0,
        dataloader_prefetch_factor = None,
        past_index = -1,
        run_name = None,
        disable_tqdm = None,
        remove_unused_columns = True,
        label_names = None,
        load_best_model_at_end = False,
        metric_for_best_model = None,
        greater_is_better = None,
        ignore_data_skip = False,
        fsdp = '',
        fsdp_min_num_params = 0,
        fsdp_config = None,
        fsdp_transformer_layer_cls_to_wrap = None,
        accelerator_config = None,
        deepspeed = None,
        label_smoothing_factor = 0.0,
        optim = 'adamw_8bit',
        optim_args = None,
        adafactor = False,
        group_by_length = False,
        length_column_name = 'length',
        report_to = None,
        ddp_find_unused_parameters = None,
        ddp_bucket_cap_mb = None,
        ddp_broadcast_buffers = None,
        dataloader_pin_memory = True,
        dataloader_persistent_workers = False,
        skip_memory_metrics = True,
        use_legacy_prediction_loop = False,
        push_to_hub = False,
        resume_from_checkpoint = None,
        hub_model_id = None,
        hub_strategy = 'every_save',
        hub_token = None,
        hub_private_repo = None,
        hub_always_push = False,
        hub_revision = None,
        gradient_checkpointing = False,
        gradient_checkpointing_kwargs = None,
        include_inputs_for_metrics = False,
        eval_do_concat_batches = True,
        fp16_backend = 'auto',
        push_to_hub_model_id = None,
        push_to_hub_organization = None,
        push_to_hub_token = None,
        mp_parameters = '',
        auto_find_batch_size = False,
        full_determinism = False,
        torchdynamo = None,
        ray_scope = 'last',
        ddp_timeout = 1800,
        torch_compile = False,
        torch_compile_backend = None,
        torch_compile_mode = None,
        include_tokens_per_second = False,
        include_num_input_tokens_seen = False,
        neftune_noise_alpha = None,
        optim_target_modules = None,
        batch_eval_metrics = False,
        eval_on_start = False,
        use_liger_kernel = False,
        liger_kernel_config = None,
        eval_use_gather_object = False,
        average_tokens_across_devices = True,
        model_init_kwargs = None,
        chat_template_path = None,
        dataset_text_field = 'text',
        dataset_kwargs = None,
        dataset_num_proc = None,
        eos_token = None,
        pad_token = None,
        max_length = 1024,
        packing = False,
        packing_strategy = 'ffd',
        padding_free = False,
        pad_to_multiple_of = None,
        eval_packing = None,
        completion_only_loss = None,
        assistant_only_loss = False,
        activation_offloading = False,
        max_seq_length = None,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        **kwargs,
    ):
        if learning_rate < 1e-7: raise FloatingPointError(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
        if learning_rate > 1: raise OverflowError(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
            output_dir = 'unsloth_training_checkpoints'
            save_strategy = 'no'
        if dataset_num_proc is None:
            from multiprocessing import cpu_count
            dataset_num_proc = cpu_count()
        
        super().__init__(
            output_dir = output_dir,
            overwrite_output_dir = overwrite_output_dir,
            do_train = do_train,
            do_eval = do_eval,
            do_predict = do_predict,
            eval_strategy = eval_strategy,
            prediction_loss_only = prediction_loss_only,
            per_device_train_batch_size = per_device_train_batch_size,
            per_device_eval_batch_size = per_device_eval_batch_size,
            per_gpu_train_batch_size = per_gpu_train_batch_size,
            per_gpu_eval_batch_size = per_gpu_eval_batch_size,
            gradient_accumulation_steps = gradient_accumulation_steps,
            eval_accumulation_steps = eval_accumulation_steps,
            eval_delay = eval_delay,
            torch_empty_cache_steps = torch_empty_cache_steps,
            learning_rate = learning_rate,
            weight_decay = weight_decay,
            adam_beta1 = adam_beta1,
            adam_beta2 = adam_beta2,
            adam_epsilon = adam_epsilon,
            max_grad_norm = max_grad_norm,
            num_train_epochs = num_train_epochs,
            max_steps = max_steps,
            lr_scheduler_type = lr_scheduler_type,
            warmup_ratio = warmup_ratio,
            warmup_steps = warmup_steps,
            log_level = log_level,
            log_level_replica = log_level_replica,
            log_on_each_node = log_on_each_node,
            logging_dir = logging_dir,
            logging_strategy = logging_strategy,
            logging_first_step = logging_first_step,
            logging_steps = logging_steps,
            logging_nan_inf_filter = logging_nan_inf_filter,
            save_strategy = save_strategy,
            save_steps = save_steps,
            save_total_limit = save_total_limit,
            save_safetensors = save_safetensors,
            save_on_each_node = save_on_each_node,
            save_only_model = save_only_model,
            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
            no_cuda = no_cuda,
            use_cpu = use_cpu,
            use_mps_device = use_mps_device,
            seed = seed,
            data_seed = data_seed,
            jit_mode_eval = jit_mode_eval,
            use_ipex = use_ipex,
            bf16 = bf16,
            fp16 = fp16,
            fp16_opt_level = fp16_opt_level,
            half_precision_backend = half_precision_backend,
            bf16_full_eval = bf16_full_eval,
            fp16_full_eval = fp16_full_eval,
            tf32 = tf32,
            local_rank = local_rank,
            ddp_backend = ddp_backend,
            tpu_num_cores = tpu_num_cores,
            tpu_metrics_debug = tpu_metrics_debug,
            debug = debug,
            dataloader_drop_last = dataloader_drop_last,
            eval_steps = eval_steps,
            dataloader_num_workers = dataloader_num_workers,
            dataloader_prefetch_factor = dataloader_prefetch_factor,
            past_index = past_index,
            run_name = run_name,
            disable_tqdm = disable_tqdm,
            remove_unused_columns = remove_unused_columns,
            label_names = label_names,
            load_best_model_at_end = load_best_model_at_end,
            metric_for_best_model = metric_for_best_model,
            greater_is_better = greater_is_better,
            ignore_data_skip = ignore_data_skip,
            fsdp = fsdp,
            fsdp_min_num_params = fsdp_min_num_params,
            fsdp_config = fsdp_config,
            fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
            accelerator_config = accelerator_config,
            deepspeed = deepspeed,
            label_smoothing_factor = label_smoothing_factor,
            optim = optim,
            optim_args = optim_args,
            adafactor = adafactor,
            group_by_length = group_by_length,
            length_column_name = length_column_name,
            report_to = report_to,
            ddp_find_unused_parameters = ddp_find_unused_parameters,
            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
            ddp_broadcast_buffers = ddp_broadcast_buffers,
            dataloader_pin_memory = dataloader_pin_memory,
            dataloader_persistent_workers = dataloader_persistent_workers,
            skip_memory_metrics = skip_memory_metrics,
            use_legacy_prediction_loop = use_legacy_prediction_loop,
            push_to_hub = push_to_hub,
            resume_from_checkpoint = resume_from_checkpoint,
            hub_model_id = hub_model_id,
            hub_strategy = hub_strategy,
            hub_token = hub_token,
            hub_private_repo = hub_private_repo,
            hub_always_push = hub_always_push,
            hub_revision = hub_revision,
            gradient_checkpointing = gradient_checkpointing,
            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
            include_inputs_for_metrics = include_inputs_for_metrics,
            eval_do_concat_batches = eval_do_concat_batches,
            fp16_backend = fp16_backend,
            push_to_hub_model_id = push_to_hub_model_id,
            push_to_hub_organization = push_to_hub_organization,
            push_to_hub_token = push_to_hub_token,
            mp_parameters = mp_parameters,
            auto_find_batch_size = auto_find_batch_size,
            full_determinism = full_determinism,
            torchdynamo = torchdynamo,
            ray_scope = ray_scope,
            ddp_timeout = ddp_timeout,
            torch_compile = torch_compile,
            torch_compile_backend = torch_compile_backend,
            torch_compile_mode = torch_compile_mode,
            include_tokens_per_second = include_tokens_per_second,
            include_num_input_tokens_seen = include_num_input_tokens_seen,
            neftune_noise_alpha = neftune_noise_alpha,
            optim_target_modules = optim_target_modules,
            batch_eval_metrics = batch_eval_metrics,
            eval_on_start = eval_on_start,
            use_liger_kernel = use_liger_kernel,
            liger_kernel_config = liger_kernel_config,
            eval_use_gather_object = eval_use_gather_object,
            average_tokens_across_devices = average_tokens_across_devices,
            model_init_kwargs = model_init_kwargs,
            chat_template_path = chat_template_path,
            dataset_text_field = dataset_text_field,
            dataset_kwargs = dataset_kwargs,
            dataset_num_proc = dataset_num_proc,
            eos_token = eos_token,
            pad_token = pad_token,
            max_length = max_length,
            packing = packing,
            packing_strategy = packing_strategy,
            padding_free = padding_free,
            pad_to_multiple_of = pad_to_multiple_of,
            eval_packing = eval_packing,
            completion_only_loss = completion_only_loss,
            assistant_only_loss = assistant_only_loss,
            activation_offloading = activation_offloading,
            max_seq_length = max_seq_length,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
pass

class _UnslothSFTTrainer(Trainer):
    """"""

    _tag_names = ["trl", "sft"]

    def __init__(
        self,
        model: Union[str, nn.Module, PreTrainedModel],
        args: Optional[Union[SFTConfig, TrainingArguments]] = None,
        data_collator: Optional[DataCollator] = None,  # type: ignore
        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        compute_loss_func: Optional[Callable] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None),
        optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None,
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional["PeftConfig"] = None,
        formatting_func: Optional[Callable[[dict], str]] = None,
    ):
        # Args
        model_id = model if isinstance(model, str) else model.config._name_or_path
        if args is None:
            model_name = model_id.split("/")[-1]
            args = SFTConfig(f"{model_name}-SFT")
        elif isinstance(args, TrainingArguments) and not isinstance(args, SFTConfig):
            dict_args = args.to_dict()
            dict_args["hub_token"] = args.hub_token  # to_dict hides the hub_token
            dict_args.pop("push_to_hub_token")
            args = SFTConfig(**dict_args)

        # Handle the tokenizer
        if processing_class is None:
            processing_class = AutoTokenizer.from_pretrained(model_id)

        if args.eos_token is not None:
            eos_token = args.eos_token
            eos_token_id = processing_class.convert_tokens_to_ids(eos_token)
            if eos_token_id is None:
                raise ValueError(
                    f"The specified `eos_token` ('{eos_token}') is not found in the vocabulary of the given "
                    f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `eos_token` exists "
                    "in the vocabulary before using it as an EOS token."
                )
            processing_class.eos_token_id = eos_token_id

        # Model
        if args.model_init_kwargs is not None and not isinstance(model, str):
            warnings.warn(
                "You passed model_init_kwargs to the `SFTConfig`, but your model is already instantiated. "
                "The `model_init_kwargs` will be ignored."
            )
        if isinstance(model, str):
            model = self._create_model_from_path(model, args)

        if args.chat_template_path is not None:
            if os.path.isfile(args.chat_template_path) and args.chat_template_path.endswith((".jinja", ".j2")):
                with open(args.chat_template_path, encoding="utf-8") as chat_template_file:
                    processing_class.chat_template = chat_template_file.read()
            else:
                model, processing_class = clone_chat_template(model, processing_class, args.chat_template_path)

        # PEFT configuration and model wrapping
        if False:
            model = self._prepare_peft_model(model, peft_config, args)

        # Data collator
        # FFD packing requires padding-free mode; otherwise, the collator outputs padded attention masks, causing
        # FlashAttention to ignore position_ids and recompute them incorrectly from the padded attention mask.
        self.padding_free = args.padding_free or (args.packing and args.packing_strategy == "ffd")
        if self.padding_free:
            if data_collator is not None:
                raise ValueError("Passing a custom data collator is not supported when using padding-free.")
            if args.packing and args.packing_strategy == "wrapped":
                warnings.warn(
                    "You are passing `padding_free=True` with the 'wrapped' packing strategy, which is not "
                    "recommended. Please refer to the documentation to understand why this is not recommended."
                )
            if model.config._attn_implementation != "flash_attention_2":
                warnings.warn(
                    "Padding-free training is enabled, but the attention implementation is not set to "
                    "'flash_attention_2'. Padding-free training flattens batches into a single sequence, and "
                    "'flash_attention_2' is the only known attention mechanism that reliably supports this. Using "
                    "other implementations may lead to unexpected behavior. To ensure compatibility, set "
                    "`attn_implementation='flash_attention_2'` in the model configuration, or verify that your "
                    "attention mechanism can handle flattened sequences."
                )
            if args.per_device_train_batch_size == 1 and not args.packing:
                warnings.warn(
                    "You are using a per_device_train_batch_size of 1 with padding-free training. Using a batch size "
                    "of 1 anihilate the benefits of padding-free training. Please consider increasing the batch size "
                    "to at least 2."
                )

        if args.completion_only_loss is None:
            first_example = next(iter(train_dataset))
            self.completion_only_loss = "prompt" in first_example
        else:
            self.completion_only_loss = args.completion_only_loss

        if data_collator is None:
            # Get the pad token: if not provided, use the one from the processing class or the eos token
            # if the processing class does not have a pad token.
            pad_token = args.pad_token or processing_class.pad_token or processing_class.eos_token
            pad_token_id = processing_class.convert_tokens_to_ids(pad_token)
            if pad_token_id is None:
                raise ValueError(
                    f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given "
                    f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists "
                    "in the vocabulary before using it as a padding token."
                )
            data_collator = DataCollatorForLanguageModeling(
                pad_token_id=pad_token_id,
                completion_only_loss=self.completion_only_loss,
                padding_free=self.padding_free,
                # Using position_ids without flash_attn hurts the training
                return_position_ids=model.config._attn_implementation == "flash_attention_2",
                pad_to_multiple_of=args.pad_to_multiple_of,
            )

        if (
            args.packing
            and args.packing_strategy == "ffd"
            and model.config._attn_implementation != "flash_attention_2"
        ):
            warnings.warn(
                "You are using packing, but the attention implementation is not set to 'flash_attention_2'. Packing "
                "flattens batches into a single sequence, and 'flash_attention_2' is the only known attention "
                "mechanism that reliably supports this. Using other implementations may lead to cross-contamination "
                "between batches. To avoid this, either disable packing by setting `packing=False`, or set "
                "`attn_implementation='flash_attention_2'` in the model configuration."
            )
        if args.assistant_only_loss and not is_conversational(train_dataset[0]):
            raise ValueError(
                "You set `assistant_only_loss=True`, but the dataset is not conversational. This option is only "
                "supported for conversational datasets."
            )

        # Dataset
        preprocess_dataset = args.dataset_kwargs is None or not args.dataset_kwargs.get("skip_prepare_dataset", False)
        if preprocess_dataset:
            if self.completion_only_loss and formatting_func:
                raise ValueError(
                    "A formatting function was provided while `completion_only_loss=True`, which is incompatible. "
                    "Using a formatter converts the dataset to a language modeling type, conflicting with "
                    "completion-only loss. To resolve this, apply your formatting function before passing the "
                    "dataset, or disable `completion_only_loss` in `SFTConfig`."
                )
            train_dataset = self._prepare_dataset(
                train_dataset, processing_class, args, args.packing, formatting_func, "train"
            )
            if eval_dataset is not None:
                packing = args.packing if args.eval_packing is None else args.eval_packing
                if isinstance(eval_dataset, dict):
                    eval_dataset = {
                        key: self._prepare_dataset(dataset, processing_class, args, packing, formatting_func, key)
                        for key, dataset in eval_dataset.items()
                    }
                else:
                    eval_dataset = self._prepare_dataset(
                        eval_dataset, processing_class, args, packing, formatting_func, "eval"
                    )

        # Initialize the metrics
        self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
        self._total_train_tokens = 0

        # Initialize the Trainer. Parent class will handle:
        # - DeepSpeed configuration [through create_accelerator_and_postprocess]
        # - FSDP setup
        # - Distributed training setup
        # - Optimizer and scheduler creation

        super().__init__(
            model=model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            compute_loss_func=compute_loss_func,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            optimizer_cls_and_kwargs=optimizer_cls_and_kwargs,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # Initialize activation offloading context
        if self.args.activation_offloading:
            self.maybe_activation_offload_context = get_act_offloading_ctx_manager(model=self.model)
        else:
            self.maybe_activation_offload_context = contextlib.nullcontext()

        # Add tags for models that have been loaded with the correct transformers version
        if hasattr(self.model, "add_model_tags"):
            self.model.add_model_tags(self._tag_names)

    def _create_model_from_path(self, model_path: str, args: SFTConfig) -> PreTrainedModel:
        """Creates a model from a path or model identifier."""
        model_init_kwargs = args.model_init_kwargs or {}
        # Handle torch dtype
        torch_dtype = model_init_kwargs.get("torch_dtype")
        if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None:
            pass  # torch_dtype is already a torch.dtype or "auto" or None
        elif isinstance(torch_dtype, str):  # it's a str, but not "auto"
            torch_dtype = getattr(torch, torch_dtype)
            model_init_kwargs["torch_dtype"] = torch_dtype
        else:
            raise ValueError(
                "Invalid `torch_dtype` passed to `SFTConfig`. Expected either 'auto' or a string representing "
                f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}."
            )
        # Disable caching if gradient checkpointing is enabled (not supported)
        # if args.gradient_checkpointing:
        #     model_init_kwargs["use_cache"] = False

        # Create model
        model = AutoModelForCausalLM.from_pretrained(model_path, **model_init_kwargs)
        return model

    def _prepare_peft_model(self, model: PreTrainedModel, peft_config: Any, args: SFTConfig) -> PreTrainedModel:
        """Prepares a model for PEFT training."""
        if not is_peft_available():
            raise ImportError("To use PeftModel, you need to install the `peft` library.")

        if not isinstance(peft_config, PeftConfig):
            raise ValueError(
                f"Expected PeftConfig object but got {type(peft_config)}. If you want to use the PeftModel, you need "
                "to pass a PeftConfig object to the SFTTrainer."
            )

        if isinstance(model, PeftModel):
            return model

        # Handle quantized models (QLoRA)
        is_qlora = getattr(model, "is_loaded_in_4bit", False) or getattr(model, "is_loaded_in_8bit", False)

        is_sharded_qlora = False
        if getattr(model, "is_loaded_in_4bit", False):
            # Check if model is sharded (FSDP/DS-Zero3)
            for _, param in model.named_parameters():
                if param.__class__.__name__ == "Params4bit":
                    is_sharded_qlora = param.data.device.type in {"cpu", "meta"}
                    break

        # Prepare model for kbit training if needed
        if is_qlora and not is_sharded_qlora:
            model = self._prepare_model_for_kbit_training(model, args)
            # Disable gradient checkpointing as it's handled by prepare_model_for_kbit_training
            args = dataclasses.replace(args, gradient_checkpointing=False)
        elif args.gradient_checkpointing:
            model = self._enable_gradient_checkpointing(model, args)

        # Create PEFT model
        if (
            version.parse(peft.__version__) >= version.parse("0.12")  # autocast_adapter_dtype introduced in 0.12
            and getattr(model, "is_loaded_in_4bit", False)
            and is_sharded_qlora
        ):
            model = get_peft_model(model, peft_config, autocast_adapter_dtype=False)
        else:
            model = get_peft_model(model, peft_config)

        # Handle bf16 casting for 4-bit models
        if args.bf16 and getattr(model, "is_loaded_in_4bit", False) and not is_sharded_qlora:
            peft_module_casting_to_bf16(model)

        return model

    def _prepare_model_for_kbit_training(self, model: PreTrainedModel, args: SFTConfig) -> PreTrainedModel:
        """Prepares a quantized model for kbit training."""
        prepare_model_kwargs = {
            "use_gradient_checkpointing": args.gradient_checkpointing,
            "gradient_checkpointing_kwargs": args.gradient_checkpointing_kwargs or {},
        }

        return prepare_model_for_kbit_training(model, **prepare_model_kwargs)

    def _enable_gradient_checkpointing(self, model: PreTrainedModel, args: SFTConfig) -> PreTrainedModel:
        """Enables gradient checkpointing for the model."""
        gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
        use_reentrant = (
            "use_reentrant" not in gradient_checkpointing_kwargs or gradient_checkpointing_kwargs["use_reentrant"]
        )

        if use_reentrant:
            if hasattr(model, "enable_input_require_grads"):
                model.enable_input_require_grads()
            else:

                def make_inputs_require_grad(module, input, output):
                    output.requires_grad_(True)

                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

        return model

    def _prepare_dataset(
        self,
        dataset: Union[Dataset, IterableDataset],
        processing_class,
        args,
        packing: bool,
        formatting_func: Optional[Callable[[dict], str]],
        dataset_name: str,
    ) -> Union[Dataset, IterableDataset]:
        # All Unsloth Zoo code licensed under LGPLv3
        if isinstance(dataset, ConstantLengthDataset): return dataset
    
        map_kwargs = {}
        use_desc = isinstance(dataset, Dataset)
        is_vlm = hasattr(processing_class, "tokenizer")
        tokenizer = processing_class
        if is_vlm: tokenizer = processing_class.tokenizer
    
        # Get max length
        max_seq_length = getattr(args, "max_length", 0)
        if max_seq_length == 0: max_seq_length = getattr(args, "max_seq_length", 0)
        if max_seq_length == 0: max_seq_length = getattr(self, "max_seq_length", 0)
        if max_seq_length == 0: max_seq_length = getattr(self, "max_seq", 0)
        if max_seq_length == 0: raise RuntimeError("Unsloth: max_seq_length is 0! Please specify one!")
        dataset_text_field = getattr(args, "dataset_text_field", "text")
        do_truncation = max_seq_length != 0
        do_formatting_func = False
        do_tokenize = True
    
        # Get correct column names
        column_names = set(next(iter(dataset)).keys())
        used_column_names = ["input_ids"]
        if "attention_mask" in column_names:
            used_column_names.append("attention_mask")
    
        # Check if already tokenized so skip
        from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
        if "labels" in column_names:
            # Most likely forgot data collator!
            if is_vlm and not hasattr(tokenizer, "pad"):
                # Check if processing_class has a .pad, if not, use tokenizer.tokenizer
                raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!")
            self.data_collator = DataCollatorForSeq2Seq(tokenizer)
            used_column_names.append("labels")
            do_tokenize = False
        elif "input_ids" in column_names:
            # Skip dataset prep, and set data collator
            if is_vlm and not hasattr(tokenizer, "pad"):
                # Check if processing_class has a .pad, if not, use tokenizer.tokenizer
                raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!")
            self.data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False)
            do_tokenize = False
        elif dataset_text_field not in column_names:
            do_formatting_func = True
            if formatting_func is None:
                raise RuntimeError("Unsloth: You must specify a `formatting_func`")
        pass
    
        if do_tokenize:
            # Check double BOS tokens
            if do_formatting_func:
                test_text = formatting_func(next(iter(dataset)))
                if not isinstance(test_text, list):
                    raise ValueError(
                        "Unsloth: The `formatting_func` should return a list of processed strings."
                    )
                test_text = test_text[0]
            else:
                test_text = next(iter(dataset))[dataset_text_field][0]
    
            # Get chat template
            chat_template = getattr(processing_class, 'chat_template', '')
            if chat_template == '' and is_vlm:
                chat_template = getattr(tokenizer, 'chat_template', '')
            if chat_template is None:
                chat_template = ''
    
            # Get bos_token
            add_special_tokens = True
            bos_token_1 = getattr(processing_class, 'bos_token', None)
            bos_token_2 = getattr(tokenizer, 'bos_token', None)
            bos_token = bos_token_1 or bos_token_2
    
            if bos_token is not None:
                if test_text.startswith(bos_token) or bos_token in chat_template:
                    add_special_tokens = False
                    print("Unsloth: We found double BOS tokens - we shall remove one automatically.")
            pass
    
            # Create tokenize function
            def _tokenize(example):
                return tokenizer(
                    example[dataset_text_field] if not do_formatting_func else formatting_func(example),
                    truncation = do_truncation,
                    max_length = max_seq_length,
                    return_token_type_ids = False,
                    add_special_tokens = add_special_tokens,
                )
            pass
    
            if not isinstance(dataset, IterableDataset):
                map_kwargs["num_proc"] = getattr(args, "dataset_num_proc", 2)
            else:
                map_kwargs["batch_size"] = dataset._ex_iterable.batch_size
                
            if use_desc: map_kwargs["desc"] = f'Unsloth: Tokenizing ["{dataset_text_field}"]'
            dataset = dataset.map(_tokenize, batched = True, **map_kwargs)
    
            # If VLM, switch data collator since .pad is needed!
            if is_vlm and not hasattr(processing_class, "pad"):
                data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False)
                self.data_collator = data_collator
            pass
        pass
        if packing:
            print("Unsloth: Hugging Face's packing is currently buggy - we're disabling it for now!")
            return dataset
    
            if max_seq_length == 0:
                raise ValueError("When packing is enabled, `max_seq_length` can't be `None`.")
    
            if use_desc: map_kwargs["desc"] = f"Unsloth: Packing {dataset_name} dataset"
            dataset = dataset.select_columns(used_column_names).map(
                pack_examples,
                batched = True,
                fn_kwargs = {"seq_length": max_seq_length,},
                **map_kwargs,
            )
        pass
        return dataset
    
    def _set_signature_columns_if_needed(self):
        # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
        # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids"
        # and "attention_mask"). When using `train_on_completion_only` we add a "completion_mask" column to the
        # dataset. So we need to override the default signature columns to include "completion_mask" as well.
        if self._signature_columns is None:
            self._signature_columns = [
                "input_ids",
                "labels",
                "position_ids",
                "completion_mask",
                "assistant_masks",
            ]

    def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch = None):
        outputs = super().compute_loss(
            model,
            inputs,
            return_outputs = return_outputs,
            num_items_in_batch = num_items_in_batch,
        )
        return outputs

    # Override training step to add activation offloading context.
    def training_step(self, *args, **kwargs):
        with self.maybe_activation_offload_context:
            return super().training_step(*args, **kwargs)

    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
        mode = "train" if self.model.training else "eval"
        metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()}  # average the metrics

        # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs`
        # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format.
        if mode == "eval":
            metrics = {f"eval_{key}": val for key, val in metrics.items()}

        logs = {**logs, **metrics}
        super().log(logs, start_time)
        self._metrics[mode].clear()

    # Ensure the model card is saved along with the checkpoint
    def _save_checkpoint(self, model, trial):
        if self.args.hub_model_id is None:
            model_name = Path(self.args.output_dir).name
        else:
            model_name = self.args.hub_model_id.split("/")[-1]
        self.create_model_card(model_name=model_name)
        super()._save_checkpoint(model, trial)

    def create_model_card(
        self,
        model_name: Optional[str] = None,
        dataset_name: Optional[str] = None,
        tags: Union[str, list[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            model_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the model.
            dataset_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the dataset used for training.
            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
                Tags to be associated with the model card.
        """
        if not self.is_world_process_zero():
            return

        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
            base_model = self.model.config._name_or_path
        else:
            base_model = None

        # normalize `tags` to a mutable set
        if tags is None:
            tags = set()
        elif isinstance(tags, str):
            tags = {tags}
        else:
            tags = set(tags)

        if hasattr(self.model.config, "unsloth_version"):
            tags.add("unsloth")

        tags.update(self._tag_names)

        model_card = generate_model_card(
            base_model=base_model,
            model_name=model_name,
            hub_model_id=self.hub_model_id,
            dataset_name=dataset_name,
            tags=list(tags),
            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="SFT",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothSFTTrainer(_UnslothSFTTrainer):
    """
    
    Trainer for Supervised Fine-Tuning (SFT) method.

    This class is a wrapper around the [`transformers.Trainer`] class and inherits all of its attributes and methods.

    Example:

    ```python
    from datasets import load_dataset
    from trl import SFTTrainer

    dataset = load_dataset("roneneldan/TinyStories", split="train[:1%]")

    trainer = SFTTrainer(model="Qwen/Qwen2-0.5B-Instruct", train_dataset=dataset)
    trainer.train()
    ```

    Args:
        model (`Union[str, PreTrainedModel]`):
            Model to be trained. Can be either:

            - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a
              path to a *directory* containing model weights saved using
              [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
              using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in
              `args.model_init_kwargs`.
            - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
        args ([`SFTConfig`], *optional*, defaults to `None`):
            Configuration for this trainer. If `None`, a default configuration is used.
        data_collator (`DataCollator`, *optional*):
            Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`.
            Will default to a custom [`DataCollatorForLanguageModeling`].
        train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
            Dataset to use for training. SFT supports both [language modeling](#language-modeling) type and
            [prompt-completion](#prompt-completion) type. The format of the samples can be either:

            - [Standard](dataset_formats#standard): Each sample contains plain text.
            - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role
              and content).

            The trainer also supports processed datasets (tokenized) as long as they contain an `input_ids` field.
        eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`):
            Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], *optional*, defaults to `None`):
            Processing class used to process the data. If `None`, the processing class is loaded from the model's name
            with [`~transformers.AutoTokenizer.from_pretrained`].
        callbacks (list of [`~transformers.TrainerCallback`], *optional*, defaults to `None`):
            List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed
            in [here](https://huggingface.co/docs/transformers/main_classes/callback).

            If you want to remove one of the default callbacks used, use the [`~transformers.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*, defaults to `None`):
            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*, defaults to `None`):
            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.
        peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`):
            PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
        formatting_func (`Optional[Callable]`):
            Formatting function applied to the dataset before tokenization. Applying the formatting function explicitly
            converts the dataset into a [language modeling](#language-modeling) type.
    
    """
    def __init__(
        self,
        model,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        compute_loss_func = None,
        compute_metrics = None,
        callbacks = None,
        optimizer_cls_and_kwargs = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        formatting_func = None,
        **kwargs
    ):
        if args is None: args = UnslothSFTConfig()
        use_bf16 = getattr(args, 'bf16', False)
        if type(use_bf16) is not bool: use_bf16 = False
        use_fp16 = getattr(args, 'fp16', False)
        if type(use_fp16) is not bool: use_fp16 = False
        force_float32 = False
        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':
            print('Unsloth: Switching to float32 training since model cannot work with float16')
            force_float32 = True
        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
        dtype = getattr(model.config, 'torch_dtype', None)
        if dtype is None: dtype = model.get_input_embeddings().dtype
        from unsloth_zoo.utils import _get_dtype
        dtype = _get_dtype(dtype)
        float16 = dtype == torch.float16
        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
        if force_float32:
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
            args.fp16 = float16
            args.bf16 = not float16
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
            args.eval_strategy = 'steps'
            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
        if ga_steps is not None and ga_steps > 1:
            from transformers import __version__ as transformers_version
            if Version(transformers_version) <= Version('4.45.2'):
                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
        if getattr(args, 'eval_strategy', 'no') != 'no':
            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
        if type(fp16_full_eval) is not bool: fp16_full_eval = False
        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
        if type(bf16_full_eval) is not bool: bf16_full_eval = False
        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
        if force_float32:
            args.bf16_full_eval = False
            args.fp16_full_eval = False
        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
            args.bf16_full_eval = True
            args.fp16_full_eval = False
        elif not bf16_full_eval and not fp16_full_eval:
            args.bf16_full_eval = args.bf16
            args.fp16_full_eval = args.fp16
        _output_logits = False
        if locals().get('compute_metrics', None) is not None: _output_logits = True
        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
        if _output_logits:
            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
            pass
        else:
            model_max_seq_length = getattr(model, 'max_seq_length', None)
            args_max_seq_length  = getattr(args,  'max_seq_length', None)
            if args_max_seq_length is None and model_max_seq_length is not None:
                max_seq_length = model.max_seq_length
                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
        if 'max_length' not in locals() and not hasattr(args, 'max_length'):
            pass
        else:
            if hasattr(args, 'max_seq_length') and args.max_seq_length is not None and args.max_seq_length > 0:
                if hasattr(args, 'max_length'):
                    args.max_length = args.max_seq_length
                    max_length = args.max_length
            else:
                model_max_length = getattr(model, 'max_seq_length', None)
                # print(model_max_length, 'mml1')
                if model_max_length is None: model_max_length = getattr(model, 'max_length', None)
                # print(model_max_length, 'mml2')
                if model_max_length is not None:
                    args.max_length = model_max_length
                    max_length = args.max_length
                elif hasattr(args, 'max_length') and args.max_length is not None:
                    max_length = args.max_length
                    # if we are here, then we are in a weird case where max_length is set but max_seq_length is not set
                    setattr(model, 'max_seq_length', max_length)
                else:
                    print('Unsloth: We did not find `max_seq_length` or `max_length` in the model or args. We will set it to 1024.')
                    args.max_length = 1024
        if model is not None and hasattr(model, 'for_training'):
            model.for_training()
        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
        if 'processing_class' in locals():
            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
        if not isinstance(data_collator, UnslothVisionDataCollator):
            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
                data_collator = TransformersDataCollatorForLanguageModeling(__tokenizer, mlm = False, mlm_probability = 0.0)
            elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
                data_collator = DataCollatorForSeq2Seq(__tokenizer)
        else:
            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
        if not isinstance(data_collator, UnslothVisionDataCollator):
            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
                if isinstance(data_collator, DataCollatorForSeq2Seq):
                    data_collator = DataCollatorForSeq2Seq(__tokenizer.tokenizer)
                else:
                    data_collator = TransformersDataCollatorForLanguageModeling(__tokenizer.tokenizer, mlm = False, mlm_probability = 0.0)
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('sft_trainer', other_metrics)
        IGNORED_TOKENIZER_NAMES = os.environ.get('UNSLOTH_IGNORED_TOKENIZER_NAMES', '').split('\n')
        from unsloth_zoo.tokenizer_utils import fix_untrained_tokens
        from unsloth_zoo.training_utils  import fix_zero_training_loss
        if 'tokenizer' not in locals(): tokenizer = processing_class
        fix_untrained_tokens(model, tokenizer, train_dataset, IGNORED_TOKENIZER_NAMES, eps = 1e-16)
        fix_zero_training_loss(model, tokenizer, train_dataset)
        
        super().__init__(
            model = model,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            compute_loss_func = compute_loss_func,
            compute_metrics = compute_metrics,
            callbacks = callbacks,
            optimizer_cls_and_kwargs = optimizer_cls_and_kwargs,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            peft_config = peft_config,
            formatting_func = formatting_func,**kwargs)
        if hasattr(self, 'neftune_hook_handle'):
            self.neftune_hook_handle.remove()
            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
        if getattr(args, 'neftune_noise_alpha', None) is not None:
            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
        pass
        
pass