code/fine_tune_sft_dpo/unsloth_compiled_cache/UnslothSFTTrainer.py

"""
2026.2.1
2026.2.1
5.2.0
0.24.0
__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 unsloth_zoo.temporary_patches.common import torch_compile
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.sft_trainer import (Any, AutoProcessor, BaseTrainer, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForVisionLanguageModeling, Dataset, EvalPrediction, FLASH_ATTENTION_VARIANTS, IterableDataset, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, TrainerCallback, TrainingArguments, Union, clone_chat_template, contextlib, create_model_from_path, dataclass, defaultdict, dft_loss, get_act_offloading_ctx_manager, is_conversational, logger, logging, nn, os, pack_dataset, pad, selective_log_softmax, torch, Any, AutoProcessor, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForVisionLanguageModeling, Dataset, EvalPrediction, FLASH_ATTENTION_VARIANTS, IterableDataset, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, TrainerCallback, TrainingArguments, Union, clone_chat_template, contextlib, create_model_from_path, defaultdict, dft_loss, get_act_offloading_ctx_manager, is_conversational, logger, os, pad, torch, Callable, DataCollator, DataCollatorForLanguageModeling, Dataset, IterableDataset, Optional, Union, os, pack_dataset, pad, PreTrainedModel, logger, os, 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
import inspect
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
from transformers.training_args import ParallelMode
from unsloth_zoo.device_type import DEVICE_TYPE, device_synchronize

# Wrap trainer with padding to right and enable training mode
# Also patches W&B since multiple runs must use wandb.finish()
import functools
from types import MethodType
try:
    from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers
except:
    def reset_unsloth_gradient_checkpointing_buffers(): pass
def prepare_for_training_mode(f):
    @functools.wraps(f)
    def wrapper(self, *args, **kwargs):
        # Enable training mode
        _was_training = None
        # Get gradient checkpointing setting from training arguments
        use_gc = getattr(self.args, 'gradient_checkpointing', True)
        if hasattr(self, 'model') and hasattr(self.model, "training"):
            _was_training = self.model.training
        if hasattr(self, 'model') and hasattr(self.model, "for_training"):
            self.model.for_training(use_gradient_checkpointing=use_gc)
        output = f(self, *args, **kwargs)
        # Restore previous mode when possible
        if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
            if _was_training is False:
                self.model.for_inference()
            elif _was_training is True and hasattr(self.model, "for_training"):
                self.model.for_training(use_gradient_checkpointing=use_gc)
        # Reset gradient checkpointing buffers to free memory while staying ready for next run
        try:
            reset_unsloth_gradient_checkpointing_buffers()
        except:
            pass
        # Patch W&B to enable logging on future runs, otherwise it'll overwrite the first run
        try:
            import wandb
            wandb.finish()
        except:
            pass
        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_hidden_states_selective_log_softmax(
    hidden_states: torch.Tensor,
    lm_head: torch.Tensor,
    index: torch.Tensor,
    chunks: int = 4,
    logit_scale_multiply: float = 0.0,
    logit_scale_divide: float = 0.0,
    logit_softcapping: float = 0.0,
    temperature: float = 1.0,
) -> torch.Tensor:
    # All Unsloth Zoo code licensed under AGPL3
    flat_hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
    flat_index = index.reshape(-1)

    chunked_hidden_states = torch.chunk(flat_hidden_states, chunks=chunks, dim=0)
    chunked_index = torch.chunk(flat_index, chunks=chunks, dim=0)

    all_per_token_logps = []

    for chunk_hidden_states, chunk_index in zip(chunked_hidden_states, chunked_index):
        chunk_logits = chunk_hidden_states.to(lm_head.dtype) @ lm_head.t()

        if logit_scale_multiply != 0.0:
            chunk_logits = chunk_logits * logit_scale_multiply
        if logit_scale_divide != 0.0:
            chunk_logits = chunk_logits / logit_scale_divide
        if logit_softcapping != 0.0:
            chunk_logits = chunk_logits * torch.tanh(chunk_logits / logit_softcapping)

        chunk_logits = chunk_logits.to(torch.float32)

        if temperature != 1.0:
            chunk_logits = chunk_logits / temperature

        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)

    all_per_token_logps = torch.concat(all_per_token_logps)

    all_per_token_logps = all_per_token_logps.reshape((hidden_states.shape[0], hidden_states.shape[1]))
    return all_per_token_logps

@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

def align_logprobs_with_mask(
    logprob_tensor: torch.Tensor,
    attention_mask: torch.Tensor,
    pad_value: float = 0.0
) -> torch.Tensor:
    """
    Aligns a log probability tensor with a given attention mask.
    """

    device = logprob_tensor.device
    batch_size, logprob_seq_len = logprob_tensor.shape
    mask_seq_len = attention_mask.shape[1]

    padded_logprobs = torch.full(
        attention_mask.shape,
        fill_value=pad_value,
        dtype=logprob_tensor.dtype,
        device=device
    )

    left_pad_counts = torch.argmax(attention_mask, dim=1)

    cols = torch.arange(logprob_seq_len, device=device)
    dest_indices = left_pad_counts.unsqueeze(1) + cols

    # Create destination row indices
    # Shape: [batch_size, logprob_seq_len]
    row_indices = torch.arange(batch_size, device=device).unsqueeze(1).expand_as(dest_indices)

    # --- 4. Filter out-of-bounds indices and perform assignment ---
    # Create a mask to identify only the indices that are within the bounds
    # of the target tensor's sequence length.
    valid_mask = dest_indices < mask_seq_len

    # Use this mask to select only the valid row indices, column indices,
    # and the corresponding values from the logprob tensor.
    # This flattens the selected elements into 1D tensors.
    valid_rows = row_indices[valid_mask]
    valid_cols = dest_indices[valid_mask]
    valid_vals = logprob_tensor[valid_mask]

    # Place the valid values into their correct positions in the padded tensor
    # using a single, efficient advanced indexing operation.
    padded_logprobs[valid_rows, valid_cols] = valid_vals

    return padded_logprobs

def autotune_batch_and_chunks(
    total_input_rows,
    seq_len,
    hidden_size,
    vocab_size,
    dtype_bytes=16,
    multiplier=None
):
    if multiplier is None:
        final_m = max(4, seq_len // 4096)
    else:
        final_m = multiplier

    if torch.cuda.is_available():
        free_bytes, _ = torch.cuda.mem_get_info()
        limit_gb = (free_bytes / (1024**3))*.80
    elif hasattr(torch, "xpu") and torch.xpu.is_available():
        # For XPU: estimate free memory from total - reserved
        total_mem = torch.xpu.get_device_properties(0).total_memory
        reserved_mem = torch.xpu.memory_reserved()
        free_bytes = total_mem - reserved_mem
        limit_gb = (free_bytes / (1024**3)) * 0.80
    else:
        # Fallback: assume 8GB available
        limit_gb = 8.0

    bytes_to_gb = 1024**3

    b_vals = torch.arange(total_input_rows, 0, -1, device='cpu', dtype=torch.float32)

    hidden_gb = (b_vals * seq_len * hidden_size * dtype_bytes) / bytes_to_gb

    base_logits = ((b_vals/total_input_rows) * b_vals * seq_len * vocab_size * dtype_bytes) / bytes_to_gb
    logits_gb = base_logits / final_m

    total_mem_gb = hidden_gb + logits_gb

    valid_mask = total_mem_gb <= limit_gb
    valid_indices = torch.nonzero(valid_mask, as_tuple=False)

    if valid_indices.shape[0] == 0:
        #This means your GPU will OOM
        return 4, final_m

    best_idx = valid_indices[0].item()
    final_b = int(b_vals[best_idx].item())

    return final_b, final_m
@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]`, *optional*):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`SFTTrainer`] is provided as a string. If you're training a MoE architecture and want to
            include the load balancing/auxilliary loss as a part of the final loss, remember to set
            `output_router_logits=True` in this dictionary.
        chat_template_path (`str`, *optional*):
            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]`, *optional*):
            Dictionary of optional keyword arguments for the dataset preparation. The only supported key is
            `skip_prepare_dataset`. When the model is a VLM, `skip_prepare_dataset` is automatically treated as `True`
            regardless of the provided value, since preprocessing is done on the fly.
        dataset_num_proc (`int`, *optional*):
            Number of processes to use for processing the dataset.
        eos_token (`str`, *optional*):
            Token used to indicate the end of a turn or sequence. If `None`, it defaults to
            `processing_class.eos_token`.
        pad_token (`str`, *optional*):
            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 `"bfd"`):
            Strategy for packing sequences. Can be either `"bfd"` (best-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 FlashAttention 2 or 3, which can efficiently handle the flattened batch structure. When
            packing is enabled with strategy `"bfd"`, padding-free is enabled, regardless of the value of this
            parameter.
        pad_to_multiple_of (`int`, *optional*):
            If set, the sequences will be padded to a multiple of this value.
        eval_packing (`bool`, *optional*):
            Whether to pack the eval dataset. If `None`, uses the same value as `packing`.

        > Parameters that control the training

        completion_only_loss (`bool`, *optional*):
            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.
        loss_type (`str`, *optional*, defaults to `"nll"`):
            Type of loss to use. Possible values are `"nll"` (negative log-likelihood, default) and `"dft"` (Dynamic
            Fine-Tuning, as described in [this paper](https://huggingface.co/papers/2508.05629)).
        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.'},
    )
    unsloth_logit_chunk_multiplier : Optional[int] = field(
            default = None,
            metadata = {'help': 'Multiplier for chunked logit computations.'},
        )
    unsloth_grpo_mini_batch : Optional[int] = field(
        default = None,
        metadata = {'help': 'Mini batch size for GRPO hidden state accumulation. Default is None unless user defines it.'},
    )
    max_seq_length : Optional[int] = field(
        default = None,
        metadata = {'help': 'Maximum sequence length to truncate to.'},
    )
    def __init__(
        self,
        output_dir = None,
        per_device_train_batch_size = 4,
        num_train_epochs = 3.0,
        max_steps = -1,
        learning_rate = 5e-05,
        lr_scheduler_type = 'linear',
        lr_scheduler_kwargs = None,
        warmup_steps = 0.1,
        optim = 'adamw_8bit',
        optim_args = None,
        weight_decay = 0.01,
        adam_beta1 = 0.9,
        adam_beta2 = 0.999,
        adam_epsilon = 1e-08,
        optim_target_modules = None,
        gradient_accumulation_steps = 2,
        average_tokens_across_devices = True,
        max_grad_norm = 1.0,
        label_smoothing_factor = 0.0,
        bf16 = False,
        fp16 = False,
        bf16_full_eval = False,
        fp16_full_eval = False,
        tf32 = None,
        gradient_checkpointing = True,
        gradient_checkpointing_kwargs = None,
        torch_compile = False,
        torch_compile_backend = None,
        torch_compile_mode = None,
        use_liger_kernel = False,
        liger_kernel_config = None,
        use_cache = False,
        neftune_noise_alpha = None,
        torch_empty_cache_steps = 250,
        auto_find_batch_size = False,
        logging_strategy = 'steps',
        logging_steps = 1,
        logging_first_step = False,
        log_on_each_node = True,
        logging_nan_inf_filter = False,
        include_num_input_tokens_seen = False,
        log_level = 'passive',
        log_level_replica = 'warning',
        disable_tqdm = None,
        report_to = 'none',
        run_name = None,
        project = 'huggingface',
        trackio_space_id = 'trackio',
        eval_strategy = 'no',
        eval_steps = None,
        eval_delay = 0,
        per_device_eval_batch_size = 4,
        prediction_loss_only = False,
        eval_on_start = False,
        eval_do_concat_batches = True,
        eval_use_gather_object = False,
        eval_accumulation_steps = 2,
        batch_eval_metrics = False,
        save_only_model = False,
        save_strategy = 'steps',
        save_steps = 500,
        save_on_each_node = False,
        save_total_limit = None,
        enable_jit_checkpoint = False,
        push_to_hub = False,
        hub_token = None,
        hub_private_repo = None,
        hub_model_id = None,
        hub_strategy = 'every_save',
        hub_always_push = False,
        hub_revision = None,
        load_best_model_at_end = False,
        metric_for_best_model = None,
        greater_is_better = None,
        ignore_data_skip = False,
        restore_callback_states_from_checkpoint = False,
        full_determinism = False,
        seed = 3407,
        data_seed = 3407,
        use_cpu = False,
        accelerator_config = None,
        parallelism_config = None,
        dataloader_drop_last = False,
        dataloader_num_workers = 0,
        dataloader_pin_memory = True,
        dataloader_persistent_workers = False,
        dataloader_prefetch_factor = None,
        remove_unused_columns = True,
        label_names = None,
        train_sampling_strategy = 'random',
        length_column_name = 'length',
        ddp_find_unused_parameters = None,
        ddp_bucket_cap_mb = None,
        ddp_broadcast_buffers = None,
        ddp_backend = None,
        ddp_timeout = 1800,
        fsdp = None,
        fsdp_config = None,
        deepspeed = None,
        debug = '',
        skip_memory_metrics = True,
        do_train = False,
        do_eval = False,
        do_predict = False,
        resume_from_checkpoint = None,
        warmup_ratio = None,
        logging_dir = None,
        local_rank = -1,
        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 = 'bfd',
        padding_free = False,
        pad_to_multiple_of = None,
        eval_packing = None,
        completion_only_loss = None,
        assistant_only_loss = False,
        loss_type = 'nll',
        activation_offloading = False,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        unsloth_logit_chunk_multiplier = None, 
        unsloth_grpo_mini_batch = None, 
        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 num_train_epochs is None:
            num_train_epochs = 3.0  # Default to 3 epochs if None, max_steps will override
        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
            output_dir = 'unsloth_training_checkpoints'
            save_strategy = 'no'
        import multiprocessing as _mp
        if _mp.get_start_method() != 'fork':
            dataset_num_proc = None
        elif dataset_num_proc is None:
            import psutil
            dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)
            memory_gb_left = psutil.virtual_memory().available / (1024**3)
            if memory_gb_left <= 2: dataset_num_proc = 1
            else: dataset_num_proc = min(dataset_num_proc, int(memory_gb_left))
        if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1':
            from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION
            if HAS_FLEX_ATTENTION and pad_to_multiple_of is None:
                from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE
                pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE
        
        
        super().__init__(
            output_dir = output_dir,
            per_device_train_batch_size = per_device_train_batch_size,
            num_train_epochs = num_train_epochs,
            max_steps = max_steps,
            learning_rate = learning_rate,
            lr_scheduler_type = lr_scheduler_type,
            lr_scheduler_kwargs = lr_scheduler_kwargs,
            warmup_steps = warmup_steps,
            optim = optim,
            optim_args = optim_args,
            weight_decay = weight_decay,
            adam_beta1 = adam_beta1,
            adam_beta2 = adam_beta2,
            adam_epsilon = adam_epsilon,
            optim_target_modules = optim_target_modules,
            gradient_accumulation_steps = gradient_accumulation_steps,
            average_tokens_across_devices = average_tokens_across_devices,
            max_grad_norm = max_grad_norm,
            label_smoothing_factor = label_smoothing_factor,
            bf16 = bf16,
            fp16 = fp16,
            bf16_full_eval = bf16_full_eval,
            fp16_full_eval = fp16_full_eval,
            tf32 = tf32,
            gradient_checkpointing = gradient_checkpointing,
            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
            torch_compile = torch_compile,
            torch_compile_backend = torch_compile_backend,
            torch_compile_mode = torch_compile_mode,
            use_liger_kernel = use_liger_kernel,
            liger_kernel_config = liger_kernel_config,
            use_cache = use_cache,
            neftune_noise_alpha = neftune_noise_alpha,
            torch_empty_cache_steps = torch_empty_cache_steps,
            auto_find_batch_size = auto_find_batch_size,
            logging_strategy = logging_strategy,
            logging_steps = logging_steps,
            logging_first_step = logging_first_step,
            log_on_each_node = log_on_each_node,
            logging_nan_inf_filter = logging_nan_inf_filter,
            include_num_input_tokens_seen = include_num_input_tokens_seen,
            log_level = log_level,
            log_level_replica = log_level_replica,
            disable_tqdm = disable_tqdm,
            report_to = report_to,
            run_name = run_name,
            project = project,
            trackio_space_id = trackio_space_id,
            eval_strategy = eval_strategy,
            eval_steps = eval_steps,
            eval_delay = eval_delay,
            per_device_eval_batch_size = per_device_eval_batch_size,
            prediction_loss_only = prediction_loss_only,
            eval_on_start = eval_on_start,
            eval_do_concat_batches = eval_do_concat_batches,
            eval_use_gather_object = eval_use_gather_object,
            eval_accumulation_steps = eval_accumulation_steps,
            batch_eval_metrics = batch_eval_metrics,
            save_only_model = save_only_model,
            save_strategy = save_strategy,
            save_steps = save_steps,
            save_on_each_node = save_on_each_node,
            save_total_limit = save_total_limit,
            enable_jit_checkpoint = enable_jit_checkpoint,
            push_to_hub = push_to_hub,
            hub_token = hub_token,
            hub_private_repo = hub_private_repo,
            hub_model_id = hub_model_id,
            hub_strategy = hub_strategy,
            hub_always_push = hub_always_push,
            hub_revision = hub_revision,
            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,
            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
            full_determinism = full_determinism,
            seed = seed,
            data_seed = data_seed,
            use_cpu = use_cpu,
            accelerator_config = accelerator_config,
            parallelism_config = parallelism_config,
            dataloader_drop_last = dataloader_drop_last,
            dataloader_num_workers = dataloader_num_workers,
            dataloader_pin_memory = dataloader_pin_memory,
            dataloader_persistent_workers = dataloader_persistent_workers,
            dataloader_prefetch_factor = dataloader_prefetch_factor,
            remove_unused_columns = remove_unused_columns,
            label_names = label_names,
            train_sampling_strategy = train_sampling_strategy,
            length_column_name = length_column_name,
            ddp_find_unused_parameters = ddp_find_unused_parameters,
            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
            ddp_broadcast_buffers = ddp_broadcast_buffers,
            ddp_backend = ddp_backend,
            ddp_timeout = ddp_timeout,
            fsdp = fsdp,
            fsdp_config = fsdp_config,
            deepspeed = deepspeed,
            debug = debug,
            skip_memory_metrics = skip_memory_metrics,
            do_train = do_train,
            do_eval = do_eval,
            do_predict = do_predict,
            resume_from_checkpoint = resume_from_checkpoint,
            warmup_ratio = warmup_ratio,
            logging_dir = logging_dir,
            local_rank = local_rank,
            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,
            loss_type = loss_type,
            activation_offloading = activation_offloading,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
        if unsloth_grpo_mini_batch is not None:
            if self.generation_batch_size >= unsloth_grpo_mini_batch:
                self.unsloth_grpo_mini_batch = unsloth_grpo_mini_batch
            else:
                raise ValueError(
                    f"Unsloth GRPO mini batch size needs to be less than or equal to the effective generation batch size, "
                    f"which is self.per_device_train_batch_size * gradient_accumulation_steps."
                )
        self.unsloth_logit_chunk_multiplier = unsloth_logit_chunk_multiplier
        self.max_seq_length = max_seq_length

pass

class _UnslothSFTTrainer(BaseTrainer):
    """"""

    _tag_names = ["trl", "sft"]
    _name = "SFT"

    def __init__(
        self,
        model: Union[str, PreTrainedModel],
        args: Optional[Union[SFTConfig, TrainingArguments]] = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[Union[PreTrainedTokenizerBase, 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
        if args is None:
            model_name = model if isinstance(model, str) else model.config._name_or_path
            model_name = model_name.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", None)
            args = SFTConfig(**dict_args)

        # Model
        if isinstance(model, str):
            model = create_model_from_path(model, **args.model_init_kwargs or {})
        else:
            if args.model_init_kwargs is not None:
                logger.warning(
                    "You passed `model_init_kwargs` to the `SFTConfig`, but your model is already instantiated. "
                    "The `model_init_kwargs` will be ignored."
                )
        model_id = model.config._name_or_path

        # Processing class
        if processing_class is None:
            processing_class = AutoProcessor.from_pretrained(model_id)

        # Handle pad token for processors or tokenizers
        if isinstance(processing_class, ProcessorMixin):
            tokenizer = processing_class.tokenizer
            self._is_vlm = True
        elif isinstance(processing_class, PreTrainedTokenizerBase):
            tokenizer = processing_class
            self._is_vlm = False
        else:
            raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`")

        if args.eos_token is not None:
            eos_token = args.eos_token
            eos_token_id = tokenizer.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."
                )
            tokenizer.eos_token_id = eos_token_id

        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()
                added_tokens = []
            else:
                model, processing_class, added_tokens = clone_chat_template(
                    model, processing_class, args.chat_template_path
                )
        else:
            added_tokens = []

        # Catch some wrong configurations related to VLMs
        if self._is_vlm and args.packing:
            raise ValueError(
                "Packing is not supported for vision-language models. Please set `packing=False` in the SFTConfig."
            )
        if self._is_vlm and args.padding_free:
            raise ValueError(
                "Padding-free training is yet not supported for vision-language models. Please set "
                "`padding_free=False` in the `SFTConfig`."
            )
        if self._is_vlm and args.assistant_only_loss:
            raise ValueError(
                "Assistant-only loss is not yet supported for vision-language models. Please set "
                "`assistant_only_loss=False` in the `SFTConfig`."
            )

        # PEFT configuration and model wrapping
        if False:
            if added_tokens:
                # Ensure that the added tokens are trainable
                if peft_config.trainable_token_indices is None:
                    peft_config.trainable_token_indices = {"embed_tokens": added_tokens}
                elif "embed_tokens" not in peft_config.trainable_token_indices:
                    peft_config.trainable_token_indices["embed_tokens"] = added_tokens
                else:
                    peft_config.trainable_token_indices["embed_tokens"].extend(added_tokens)

                # Ensure that the lm_head is trainable
                if peft_config.modules_to_save is None or "lm_head" not in peft_config.modules_to_save:
                    logger.warning(
                        "Cloning chat template added new tokens to the tokenizer, but 'lm_head' is not in PEFT's "
                        "`modules_to_save`. As a result, the model may not learn to generate outputs with these new "
                        "tokens, leading to degraded generation quality. To fix this, add "
                        "`modules_to_save=['lm_head']` to your PEFT configuration."
                    )

                    if peft_config.modules_to_save is None:
                        peft_config.modules_to_save = ["lm_head"]
                    else:
                        peft_config.modules_to_save.append("lm_head")

        # In Prompt Tuning a small set of trainable virtual tokens [continuous prompt embeddings] is prepended to the
        # input. We store the number of these tokens so we can account for them correctly when calculating accuracy.
        self.num_virtual_tokens = 0

        if False:
            pass
            if model.active_adapter in model.peft_config:
                peft_model_config = model.peft_config[model.active_adapter]
                self.num_virtual_tokens = getattr(peft_model_config, "num_virtual_tokens", 0)

        # Data collator
        # BFD 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 == "bfd")
        use_flash_attention = model.config._attn_implementation in FLASH_ATTENTION_VARIANTS
        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":
                logger.warning(
                    "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 not use_flash_attention:
                logger.warning(
                    "Padding-free training is enabled, but the attention implementation is not set to a supported "
                    "flash attention variant. Padding-free training flattens batches into a single sequence, and only "
                    "the following implementations are known to reliably support this: "
                    f"{', '.join(sorted(FLASH_ATTENTION_VARIANTS))}. Using other implementations may lead to "
                    "unexpected behavior. To ensure compatibility, set `attn_implementation` in the model "
                    "configuration to one of these supported options or verify that your attention mechanism can "
                    "handle flattened sequences."
                )
        # Decide whether to use completion-only loss: if not specified, then it is set to True if the dataset format
        # is prompt-completion, and False if the dataset format is language modeling.
        dataset_sample = next(iter(train_dataset))
        if args.completion_only_loss is None:
            self.completion_only_loss = "prompt" in dataset_sample and "completion" in dataset_sample
        else:
            self.completion_only_loss = args.completion_only_loss

        self._is_vision_dataset = "image" in dataset_sample or "images" in dataset_sample
        # Unsloth: override _is_vlm for VLM models that pass a bare tokenizer
        if not self._is_vlm and self._is_vision_dataset:
            _m = model
            if hasattr(_m, "model"): _m = _m.model
            if hasattr(getattr(_m, "config", None), "vision_config") or \
               _m.__class__.__name__.endswith("ForConditionalGeneration"):
                self._is_vlm = True
        if self._is_vision_dataset and not self._is_vlm:
            raise ValueError(
                "The dataset appears to be vision-related (contains 'image' or 'images' keys), but the provided "
                "model does not seem to be a vision-language model. Please check your model and dataset."
            )

        if data_collator is None and not self._is_vision_dataset:
            # 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 tokenizer.pad_token or tokenizer.eos_token
            pad_token_id = tokenizer.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,
                pad_to_multiple_of=args.pad_to_multiple_of,
            )
        elif data_collator is None and self._is_vision_dataset:
            data_collator = DataCollatorForVisionLanguageModeling(
                processor=processing_class,
                max_length=args.max_length,
                completion_only_loss=self.completion_only_loss,
                pad_to_multiple_of=args.pad_to_multiple_of,
                dataset_text_field=args.dataset_text_field,
            )

        if args.packing and args.packing_strategy == "bfd" and not use_flash_attention:
            logger.warning(
                "You are using packing, but the attention implementation is not set to a supported flash attention "
                "variant. Packing gathers multiple samples into a single sequence, and only the following "
                f"implementations are known to reliably support this: {', '.join(sorted(FLASH_ATTENTION_VARIANTS))}. "
                "Using other implementations may lead to cross-contamination between samples. To avoid this, either "
                "disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration "
                "to one of these supported options."
            )
        if args.assistant_only_loss and not is_conversational(dataset_sample):
            raise ValueError(
                "You set `assistant_only_loss=True`, but the dataset is not conversational. This option is only "
                "supported for conversational datasets."
            )

        # Dataset
        # Skip dataset preparation if `skip_prepare_dataset=True` in `dataset_kwargs`, or if it's a VLM, where
        # preprocessing [e.g., image-to-pixel conversion] is too costly and done on the fly instead.
        skip_prepare_dataset = (
            args.dataset_kwargs is not None
            and args.dataset_kwargs.get("skip_prepare_dataset", False)
            or self._is_vision_dataset
        )
        if not skip_prepare_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`."
                )
            self._unsloth_model_ref = model
            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"
                    )

        # Loss function
        if args.loss_type == "nll":
            pass  # use the default loss
        elif args.loss_type == "dft":
            if compute_loss_func is not None:
                raise ValueError(
                    "You passed a `compute_loss_func` together with `loss_type='dft'` to the `SFTTrainer`. "
                    "When using `loss_type='dft'`, the loss function is internally set to the DFT loss, so passing a "
                    "`compute_loss_func` is not allowed."
                )
            compute_loss_func = dft_loss
        else:
            raise ValueError(f"Invalid `loss_type` {args.loss_type} passed. Supported values are 'nll' and 'dft'.")

        # 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)

        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)

    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
        try:
            if isinstance(dataset, ConstantLengthDataset): return dataset
        except:
            pass
    
        map_kwargs = {}
        use_desc = isinstance(dataset, Dataset)
        is_vlm = hasattr(processing_class, "tokenizer")
        tokenizer = processing_class
        if is_vlm: tokenizer = processing_class.tokenizer
    
        # Dynamic detection: check if model's module defines a function
        # that requires token_type_ids when is_training=True
        import sys as _sys
        _needs_token_type_ids = False
        # Split to avoid compiler substring match on masking_utils names
        _ccm = 'create_' + 'causal_mask_mapping'
        _model = getattr(self, '_unsloth_model_ref', None) or getattr(self, 'model', None)
        if _model is not None:
            for _m in (_model, getattr(_model, 'model', None)):
                if _m is None: continue
                _mod = _sys.modules.get(type(_m).__module__)
                if _mod is not None and hasattr(_mod, _ccm):
                    _needs_token_type_ids = True
                    break
    
        if not _needs_token_type_ids:
            # Fallback: model not yet available, check processor class MRO
            for _base in type(processing_class).__mro__:
                _base_mod = getattr(_base, '__module__', '')
                if 'transformers.models.' in _base_mod:
                    _modeling_mod = _base_mod.replace('.processing_', '.modeling_')
                    _mod = _sys.modules.get(_modeling_mod)
                    if _mod is not None and hasattr(_mod, _ccm):
                        _needs_token_type_ids = True
                        break
        if _needs_token_type_ids and hasattr(args, 'remove_unused_columns'):
            args.remove_unused_columns = False
    
        # 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")
        if _needs_token_type_ids:
            used_column_names.append("token_type_ids")
    
        # 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 = _needs_token_type_ids,
                    add_special_tokens = add_special_tokens,
                )
            pass
    
            if not isinstance(dataset, IterableDataset):
                import multiprocessing as _mp
                if _mp.get_start_method() != 'fork':
                    dataset_num_proc = None
                else:
                    dataset_num_proc = getattr(args, "dataset_num_proc", None)
                    if dataset_num_proc is None:
                        import psutil
                        dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)
                        memory_gb_left = psutil.virtual_memory().available / (1024**3)
                        if memory_gb_left <= 2:
                            dataset_num_proc = 1
                        else:
                            dataset_num_proc = min(dataset_num_proc, int(memory_gb_left))
                map_kwargs["num_proc"] = dataset_num_proc
            else:
                map_kwargs["batch_size"] = dataset._ex_iterable.batch_size
                
            if use_desc: map_kwargs["desc"] = f'Unsloth: Tokenizing ["{dataset_text_field}"]'
            import warnings as _w
            with _w.catch_warnings():
                _w.filterwarnings("ignore", message=".*couldn't be hashed properly.*")
                dataset = dataset.map(_tokenize, batched = True, remove_columns = list(column_names), **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:
            # Try using new packing which works in TRL
            try:
                pack_dataset
            except:
                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 = pack_dataset(
                dataset.select_columns(used_column_names),
                max_seq_length,
                getattr(args, "packing_strategy", "bfd"),
                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:
            if self._is_vision_dataset:
                self._signature_columns = ["messages", "prompt", "completion", "images", "input_ids", "labels", "attention_mask", "seq_lengths", "completion_mask", "assistant_masks"]
            else:
                self._signature_columns = ["input_ids", "labels", "seq_lengths", "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.update(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)
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 `<ModelArchitecture>.from_pretrained` (where `<ModelArchitecture>` is derived from the model
              config) with the keyword arguments in `args.model_init_kwargs`.
            - A [`~transformers.PreTrainedModel`] object.
            If you're training a model with an MoE architecture and want to include the load balancing/auxilliary loss
            as a part of the final loss, remember to set the `output_router_logits` config of the model to `True`.
        args ([`SFTConfig`], *optional*):
            Configuration for this trainer. If `None`, a default configuration is used.
        data_collator ([`~transformers.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 [`~trainer.sft_trainer.DataCollatorForLanguageModeling`] if the model is a language model
            and [`~trainer.sft_trainer.DataCollatorForVisionLanguageModeling`] if the model is a vision-language model.
        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`], [`~transformers.ProcessorMixin`], *optional*):
            Processing class used to process the data. If `None`, the processing class is loaded from the model's name
            with [`~transformers.AutoProcessor.from_pretrained`]. A padding token, `tokenizer.pad_token`, must be set.
            If the processing class has not set a padding token, `tokenizer.eos_token` will be used as the default.
        compute_loss_func (`Callable`, *optional*):
            A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated
            batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default [loss
            function](https://github.com/huggingface/transformers/blob/052e652d6d53c2b26ffde87e039b723949a53493/src/transformers/trainer.py#L3618)
            used by [`Trainer`].
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function that will be used to compute metrics at evaluation. Must take a
            [`~transformers.EvalPrediction`] and return a dictionary string to metric values. When passing
            [`SFTConfig`] with `batch_eval_metrics` set to `True`, your `compute_metrics` function must take a boolean
            `compute_result` argument. This will be triggered after the last eval batch to signal that the function
            needs to calculate and return the global summary statistics rather than accumulating the batch-level
            statistics.
        callbacks (list of [`~transformers.TrainerCallback`], *optional*):
            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[Optional[torch.optim.Optimizer], Optional[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 [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`.
        optimizer_cls_and_kwargs (`tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*):
            A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in
            `args`. Incompatible with the `optimizers` argument.

            Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before
            initializing the Trainer.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            A function that preprocess the logits right before caching them at each evaluation step. Must take two
            tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
            by this function will be reflected in the predictions received by `compute_metrics`.

            Note that the labels (second parameter) will be `None` if the dataset does not have them.
        peft_config ([`~peft.PeftConfig`], *optional*):
            PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
        formatting_func (`Callable`, *optional*):
            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
        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().weight.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'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        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 hasattr(args, 'mixed_precision'): args.mixed_precision = 'fp16' if float16 else 'bf16'
            # args.mixed_precision is a new argument which needs to be set now
        elif mixed_precision_dtype == 'bfloat16':
            # Both False since bfloat16 full finetuning doesn't do any autocasting.
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        
        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
            elif args_max_seq_length is not None and model_max_seq_length is not None:
                if args_max_seq_length > model_max_seq_length:
                    print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but '
                           'the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')
                    args.max_seq_length = model_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)
                if model_max_length is None: model_max_length = getattr(model, 'max_length', None)
                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(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        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,
                    pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                )
            elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
                data_collator = DataCollatorForSeq2Seq(
                    __tokenizer,
                    pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                )
        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,
                        pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                    )
                else:
                    data_collator = TransformersDataCollatorForLanguageModeling(
                        __tokenizer.tokenizer,
                        mlm = False,
                        mlm_probability = 0.0,
                        pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                    )
        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)
        
        # [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(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        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 "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
        if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):
            _vllm_tok = self.llm.get_tokenizer()
            _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)
            if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:
                _vllm_tok.chat_template = _pc.chat_template
        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`"))