unsloth_compiled_cache/UnslothIterativeSFTTrainer.py

"""
2025.10.1
2025.10.1
4.56.2
0.22.2
__UNSLOTH_VERSIONING__
"""

# Unsloth auto generated code
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

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.iterative_sft_trainer import (AutoModelForCausalLM, AutoTokenizer, BaseImageProcessor, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForSeq2Seq, DataLoader, Dataset, EvalLoopOutput, FeatureExtractionMixin, IterativeSFTConfig, IterativeSFTTrainer, Optional, PPODecorators, Path, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainingArguments, Union, generate_model_card, get_comet_experiment_url, is_peft_available, is_wandb_available, logger, logging, os, torch, wandb, warnings, Optional, PeftModel, PreTrainedModel, Trainer, is_peft_available, logger, os, torch)


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
from transformers.training_args import ParallelMode

# Wrap trainer with padding to right and enable training mode
import functools
from types import MethodType
def prepare_for_training_mode(f):
    @functools.wraps(f)
    def wrapper(self, *args, **kwargs):
        # Enable training mode
        if hasattr(self, 'model') and hasattr(self.model, "for_training"):
            self.model.for_training()
        output = f(self, *args, **kwargs)
        # Return inference mode
        if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
            self.model.for_inference()
        return output
    return wrapper
pass

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 chunked_selective_log_softmax(logits, index):
    # Split into 4 chunks only
    chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = 4, dim = 0)
    chunked_index  = torch.chunk(index.reshape(-1), chunks = 4, dim = 0)
    all_per_token_logps = []
    # Below loop does the same as selective_log_softmax(chunk_logits, chunk_index)
    for chunk_logits, chunk_index in zip(chunked_logits, chunked_index):
        chunk_logits = chunk_logits.to(torch.float32)
        selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim = -1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)
    pass
    all_per_token_logps = torch.concat(all_per_token_logps)
    all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1]))
    return all_per_token_logps

def calculate_pad_tokens_in_prompt(
    input_ids: torch.Tensor,
    logits_to_keep: int,
    pad_token_id: int
) -> torch.Tensor:
    """
    Given prompt tensor, it returns all the left padded tokens in that sequence. so [pad, pad, pad, cat] = 3 tokens 
    """
    if logits_to_keep >= input_ids.shape[1]:
        raise ValueError("logits_to_keep must be smaller than the sequence length.")

    prompt_section = input_ids[:, :-logits_to_keep]

    padding_mask = (prompt_section == pad_token_id)

    pad_token_counts = padding_mask.sum(dim=1)

    return pad_token_counts

def create_completion_attention_mask(
    completion_input_ids: torch.Tensor,
    left_pad_tokens_per_prompt: torch.Tensor,
    max_left_pad: int,
    pad_token_id: int
) -> torch.Tensor:
    """
    Given that we have a sequence, [p,p,p,c,c,c,pad,pad,pad]

    Where p are extra prompt tokens we got from slicing the torch tensor, c is completion tokens
    and pad are pad tokens, this function would make a completion mask that would 0 out the pad
    and p tokens. so in this example [0,0,0,1,1,1,0,0,0]
    """
    batch_size, completion_len = completion_input_ids.shape
    device = completion_input_ids.device

    num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt

    indices = torch.arange(completion_len, device=device).unsqueeze(0)
    shift_mask = indices >= num_tokens_to_mask.unsqueeze(1)

    non_padding_mask = (completion_input_ids != pad_token_id)

    final_mask = shift_mask & non_padding_mask

    return final_mask

def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
    """
    Moves all padding tokens in each sequence of a batch to the right.
    """
    mask = (tensor != pad_id)
    # Must do stable=True since binary mark is unordered
    sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
    packed_tensor = torch.gather(tensor, 1, sorted_indices)
    return packed_tensor
@dataclass
class UnslothIterativeSFTConfig(IterativeSFTConfig):
    """
    
    Configuration class for the [`IterativeSFTTrainer`].

    <Tip warning={true}>

    The [`IterativeSFTTrainer`] is deprecated and will be removed in version 0.24.0. Please use the [`SFTTrainer`].

    </Tip>

    This class includes only the parameters that are specific to Iterative 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 [`IterativeSFTTrainer`] is provided as a string.

        > Parameters that control the data preprocessing

        max_length (`int` or `None`, *optional*, defaults to `None`):
            Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated.
        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
            The truncation mode to use, either `"keep_end"` or `"keep_start"`.
        optimize_device_cache (`bool`, *optional*, defaults to `False`):
            Whether to optimize accelerator cache for slightly more memory-efficient training.
    
    """
    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.'},
    )
    max_seq_length : Optional[int] = field(
        default = None,
        metadata = {'help': 'Maximum sequence length to truncate to.'},
    )
    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,
        parallelism_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 = True,
        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,
        max_length = None,
        truncation_mode = 'keep_end',
        optimize_device_cache = False,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        max_seq_length = None,
        **kwargs,
    ):
        if learning_rate < 1e-7: print(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: print(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'
        
        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,
            parallelism_config = parallelism_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,
            max_length = max_length,
            truncation_mode = truncation_mode,
            optimize_device_cache = optimize_device_cache,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
        self.max_seq_length = max_seq_length
pass

class _UnslothIterativeSFTTrainer(Trainer):
    """"""

    _tag_names = ["trl", "iterative-sft"]

    def __init__(
        self,
        model: Union[str, PreTrainedModel],
        args: Optional[Union[IterativeSFTConfig, TrainingArguments]] = None,
        data_collator: Optional[DataCollator] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
            None,
            None,
        ),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
    ):
        warnings.warn(
            "The `IterativeSFTTrainer` is deprecated and will be removed in version 0.24.0. Please use the "
            "`SFTTrainer`.",
            FutureWarning,
        )

        # 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 = IterativeSFTConfig(f"{model_name}-IterativeSFT")
        elif isinstance(args, TrainingArguments) and not isinstance(args, IterativeSFTConfig):
            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 = IterativeSFTConfig(**dict_args)

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

        # Model
        if args.model_init_kwargs is not None and not isinstance(model, str):
            logger.warning(
                "You passed model_init_kwargs to the `IterativeSFTConfig`, 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)

        # PEFT configuration and model wrapping
        if is_peft_available() and isinstance(model, PeftModel):
            self.is_peft_model = True
        else:
            self.is_peft_model = False

        self.processing_class = processing_class
        self.is_encoder_decoder = getattr(model.config, "is_encoder_decoder", False)

        if data_collator is None:
            if self.is_encoder_decoder:
                self.data_collator = DataCollatorForSeq2Seq(
                    processing_class, label_pad_token_id=-100, pad_to_multiple_of=8
                )
            else:
                self.data_collator = DataCollatorForLanguageModeling(self.processing_class, mlm=False)
        else:
            self.data_collator = data_collator

        self.max_length = args.max_length
        self.truncation_mode = args.truncation_mode
        self.optimize_device_cache = args.optimize_device_cache

        super().__init__(
            model=model,
            args=args,
            data_collator=self.data_collator,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            compute_metrics=compute_metrics,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # 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)

        self.create_optimizer_and_scheduler(self.args.max_steps)

        # prepare model, optimizer and lr_scheduler
        self.model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(
            self.model, self.optimizer, self.lr_scheduler
        )

        self.processing_class.truncation_side = "left" if self.truncation_mode == "keep_end" else "right"

        if not hasattr(self, "accelerator"):
            raise AttributeError(
                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
            )

        PPODecorators.optimize_device_cache = self.optimize_device_cache

    def _create_model_from_path(self, model_path: str, args: IterativeSFTConfig) -> PreTrainedModel:
        """Creates a model from a path or model identifier."""
        model_init_kwargs = args.model_init_kwargs or {}
        return AutoModelForCausalLM.from_pretrained(model_path, **model_init_kwargs)

    def prepare_model_inputs(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, labels: torch.Tensor):
        if attention_mask is None:
            attention_mask = [torch.ones_like(ids) for ids in input_ids]

        if self.is_encoder_decoder:
            input_data = self.data_collator(
                [
                    {"input_ids": ids, "attention_mask": att, "labels": lab}
                    for ids, att, lab in zip(input_ids, attention_mask, labels)
                ]
            ).to(self.model.device)

            input_data.pop("decoder_input_ids", None)  # This is directly computed inside the model

            input_data["labels"][input_data["labels"] == self.processing_class.pad_token_id] = -100

        else:
            input_data = self.data_collator(
                [{"input_ids": ids, "attention_mask": att} for ids, att in zip(input_ids, attention_mask)]
            ).to(self.model.device)

        # truncate in case the user has provided input_ids, attention_mask and labels
        if self.max_length is not None:
            if self.truncation_mode == "keep_start":
                input_data = {k: v[: self.max_length] for k, v in input_data.items()}
            elif self.truncation_mode == "keep_end":
                input_data = {k: v[-self.max_length :] for k, v in input_data.items()}
            else:
                raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")

        return input_data

    @staticmethod
    def _step_safety_checker(
        input_ids: list[torch.LongTensor],
        attention_mask: list[torch.LongTensor],
        labels: list[torch.LongTensor],
        texts: list[str],
        texts_labels: list[str],
    ):
        """
        Check if the input data is valid for training.

        Args:
            input_ids (list[`torch.LongTensor`]):
                List of tensors containing the input_ids
            attention_mask (list[`torch.LongTensor`]):
                List of tensors containing the attention_mask
            labels (list[`torch.FloatTensor`]):
                List of tensors containing the labels
            texts (list[`str`]):
                List of string containing the text input.
            texts_labels (list[`str`]):
                List of string containing the text labels.

        Returns:
            `tuple`: The input data.
        """
        if texts is None:
            if attention_mask is None:
                for name, tensor_list in zip(["input_ids", "labels"], [input_ids, labels]):
                    if not isinstance(tensor_list, list):
                        raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}")
                    if not isinstance(tensor_list[0], torch.Tensor):
                        raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}")
            else:
                for name, tensor_list in zip(
                    ["input_ids", "attention_mask", "labels"], [input_ids, attention_mask, labels]
                ):
                    if not isinstance(tensor_list, list):
                        raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}")
                    if not isinstance(tensor_list[0], torch.Tensor):
                        raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}")
        else:
            if not isinstance(texts, list):
                raise ValueError(f"'text' must be a list of strings - got {type(texts)}")
            if not isinstance(texts[0], str):
                raise ValueError(f"Elements in 'text' must be strings - got {type(texts[0])}")
            if texts_labels is not None:
                if not isinstance(texts_labels, list):
                    raise ValueError(f"'text_labels' must be a list of strings - got {type(texts_labels)}")
                if not isinstance(texts_labels[0], str):
                    raise ValueError(f"Elements in 'text_labels' must be strings - got {type(texts_labels[0])}")

        return input_ids, attention_mask, labels, texts, texts_labels

    @PPODecorators.empty_device_cache()
    def step(
        self,
        input_ids: Optional[list[torch.LongTensor]] = None,
        attention_mask: Optional[list[torch.LongTensor]] = None,
        labels: Optional[list[torch.LongTensor]] = None,
        texts: Optional[list[str]] = None,
        texts_labels: Optional[list[str]] = None,
    ):
        """
        Run an optimisation step given a list of input_ids, attention_mask, and labels or a list of text and
        text_labels.

        Args:
            input_ids (list[`torch.LongTensor`]):
                List of tensors containing the input_ids (if not provided, text will be used)
            attention_mask (list[`torch.LongTensor`], , *optional*):
                List of tensors containing the attention_mask
            labels (list[`torch.FloatTensor`], *optional*):
                List of tensors containing the labels (if set to None, will default to input_ids)
            texts (list[`str`], *optional*):
                List of strings containing the text input (if not provided, input_ids will directly be used)
            texts_labels (list[`str`], *optional*):
                List of strings containing the text labels (if set to None, will default to text)

        Returns:
            `dict[str, Any]`: A summary of the training statistics
        """
        self.model.train()

        if self.state.global_step == 0:
            self.tr_loss = torch.tensor(0.0).to(self.args.device)
            self._globalstep_last_logged = self.state.global_step

        if input_ids is None and texts is None:
            raise ValueError("Step should include `input_ids` or `texts` as keyword arguments.")
        elif input_ids is not None and texts is not None:
            logger.warning(
                "Both `input_ids` and `texts` argument are provided. `input_ids` will be ignored. "
                "Please provide only one of the two.",
            )

        if labels is None and texts_labels is None and self.is_encoder_decoder:
            raise ValueError(
                "No 'labels' or 'text_labels' are provided. When using an encoder-decoder architecture, 'labels' or 'text_labels' must be passed."
            )

        # Convert Column to list if not already
        input_ids = input_ids[:] if input_ids is not None else None
        attention_mask = attention_mask[:] if attention_mask is not None else None
        labels = labels[:] if labels is not None else None
        texts = texts[:] if texts is not None else None
        texts_labels = texts_labels[:] if texts_labels is not None else None

        input_ids, attention_mask, labels, texts, texts_labels = self._step_safety_checker(
            input_ids, attention_mask, labels, texts, texts_labels
        )

        if texts is not None:
            model_inputs = self.processing_class(
                texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt"
            )

            input_ids, attention_mask = model_inputs["input_ids"], model_inputs["attention_mask"]

        if texts_labels is not None:
            labels = self.processing_class(
                texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt"
            )["input_ids"]

        if labels is None:
            labels = input_ids

        model_inputs = self.prepare_model_inputs(input_ids, attention_mask, labels)

        model_inputs_names = list(model_inputs.keys())

        batch_dict = {}
        batch_dict.update(model_inputs)

        def collator(data):
            return_dict = dict()
            for key in data[0]:
                if key in ["input_ids", "attention_mask", "labels"]:
                    return_dict[key] = torch.stack([d[key] for d in data]).to(self.model.device)
            return return_dict

        batch_data = Dataset.from_dict(batch_dict)
        batch_data.set_format("torch")

        step_dataloader = DataLoader(
            batch_data,
            batch_size=self.args.per_device_train_batch_size,
            shuffle=True,
            collate_fn=collator,
        )

        for _, batch in enumerate(step_dataloader):
            with self.accelerator.accumulate(self.model):
                model_inputs = {k: batch[k] for k in model_inputs_names}
                loss = self.compute_loss(self.model, model_inputs)

                if self.args.n_gpu > 1:
                    loss = loss.mean()

                tr_loss_step = loss.detach()

                self.accelerator.backward(loss)

                if self.accelerator.sync_gradients and self.args.max_grad_norm is not None:
                    self.accelerator.clip_grad_norm_(
                        self.model.parameters(),
                        self.args.max_grad_norm,
                    )

                self.optimizer.step()
                self.optimizer.zero_grad()
                if self.lr_scheduler is not None:
                    self.lr_scheduler.step()

                self.state.global_step += 1

                # update stats etc
                self.tr_loss += tr_loss_step

                self._maybe_log_save_evaluate()

    def _maybe_log_save_evaluate(self):
        # check if eval is required
        if self.args.eval_steps is not None:
            if self.state.global_step % self.args.eval_steps == 0 and self.state.global_step != 0:
                self.evaluate(self.eval_dataset)

        # check if logging is required
        if self.args.logging_steps is not None:
            if self.state.global_step % self.args.logging_steps == 0 and self.state.global_step != 0:
                logs: dict[str, float] = {}

                tr_loss_scalar = self._nested_gather(self.tr_loss).mean().item()

                # reset tr_loss to zero
                self.tr_loss -= self.tr_loss

                logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
                logs["learning_rate"] = self._get_learning_rate()

                self._globalstep_last_logged = self.state.global_step

                self.log(logs)

    # 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")

        if "JOB_ID" in os.environ:
            tags.add("hf_jobs")

        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=tags,
            wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="Iterative SFT",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothIterativeSFTTrainer(_UnslothIterativeSFTTrainer):
    """
    
    The IterativeSFTTrainer can be used to finetune models with methods that requires some steps between optimization.

    <Tip warning={true}>

    The [`IterativeSFTTrainer`] is deprecated and will be removed in version 0.24.0. Please use the [`SFTTrainer`].

    </Tip>

    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 ([`IterativeSFTConfig`], *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 [`~transformers.default_data_collator`] if no `processing_class` is provided, an instance
            of [`~transformers.DataCollatorWithPadding`] otherwise if the processing_class is a feature extractor or
            tokenizer.
        eval_dataset (`datasets.Dataset`):
            The dataset to use for evaluation.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *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`].
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to
            metric values.
    
    """
    def __init__(
        self,
        model,
        args = None,
        data_collator = None,
        eval_dataset = None,
        processing_class = None,
        preprocess_logits_for_metrics = None,
        compute_metrics = None,
        **kwargs
    ):
        if args is None: args = UnslothIterativeSFTConfig()
        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
        full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'
        if not full_finetuning and (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, 'dtype', None) or 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:
            # Forced float32 training
            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':
            # Mixed precision training
            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 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'
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('iterative_sft_trainer', other_metrics)
        
        # [TODO] Fix up DataParallel multiplying batch sizes
        # [TODO] DDP works, but DP seems to not work? [TODO]
        if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
            if getattr(args, "_n_gpu", 1) != 1:
                args._n_gpu = 1
        if "model" in locals() and hasattr(model, "for_training"):
            model.for_training()
        super().__init__(
            model = model,
            args = args,
            data_collator = data_collator,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            compute_metrics = compute_metrics,**kwargs)
        if "model" in locals() and hasattr(model, "for_inference"):
            model.for_inference()
        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
        if hasattr(self, 'accelerator'):
            scaler = self.accelerator.scaler
            current_model = model
            while hasattr(current_model, 'model'):
                current_model.accelerator_scaler = scaler
                current_model = current_model.model
            current_model.accelerator_scaler = scaler
        pass
        if hasattr(self, 'train'):
            self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)
        pass
        
pass


if hasattr(logger, "addFilter"):
    import logging
    class HideLoggingMessage(logging.Filter):
        def __init__(self, text): self.text = text
        def filter(self, x): return not (self.text in x.getMessage())
    pass
    logger.addFilter(HideLoggingMessage("`use_cache=True`"))