code/fine_tune_sft_dpo/unsloth_compiled_cache/UnslothGKDTrainer.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.gkd_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DataCollator, DataCollatorForChatML, Dataset, EvalPrediction, F, FeatureExtractionMixin, GKDConfig, GKDTrainer, GenerationConfig, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTTrainer, TrainerCallback, Union, disable_dropout_in_model, empty_cache, nn, os, prepare_deepspeed, random, textwrap, torch, unwrap_model_for_generation, warnings, AutoModelForCausalLM, BaseImageProcessor, Callable, DataCollator, DataCollatorForChatML, Dataset, EvalPrediction, F, FeatureExtractionMixin, GKDConfig, GenerationConfig, Optional, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTTrainer, TrainerCallback, Union, disable_dropout_in_model, nn, os, prepare_deepspeed, torch, warnings)


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 UnslothGKDConfig(GKDConfig):
    """
    
    Configuration class for [`GKDTrainer`].

    This class includes only the parameters that are specific to GKD training. For a full list of training arguments,
    please refer to the [`~transformers.TrainingArguments`] and [`SFTConfig`] documentation.

    Args:
        temperature (`float`, *optional*, defaults to `0.9`):
            Temperature for sampling. The higher the temperature, the more random the completions.
        lmbda (`float`, *optional*, defaults to `0.5`):
            Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy
            student-generated outputs).
        beta (`float`, *optional*, defaults to `0.5`):
            Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence loss. When
            beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL Divergence.
        max_new_tokens (`int`, *optional*, defaults to `128`):
            Maximum number of tokens to generate per completion.
        teacher_model_name_or_path (`str`, *optional*):
            Model name or path of the teacher model. If `None`, the teacher model will be the same as the model being
            trained.
        teacher_model_init_kwargs (`dict[str, Any]]`, *optional*):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the teacher model
            from a string.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model.
        seq_kd (`bool`, *optional*, defaults to `False`):
            Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised FT on
            teacher-generated output).
    
    """
    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,
        temperature = 0.9,
        lmbda = 0.5,
        beta = 0.5,
        max_new_tokens = 128,
        teacher_model_name_or_path = None,
        teacher_model_init_kwargs = None,
        disable_dropout = True,
        seq_kd = 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
        
        if temperature <= 0:
            raise ValueError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')
        elif temperature >= 10:
            raise ValueError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')
        
        
        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,
            temperature = temperature,
            lmbda = lmbda,
            beta = beta,
            max_new_tokens = max_new_tokens,
            teacher_model_name_or_path = teacher_model_name_or_path,
            teacher_model_init_kwargs = teacher_model_init_kwargs,
            disable_dropout = disable_dropout,
            seq_kd = seq_kd,**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 _UnslothGKDTrainer(SFTTrainer):
    """"""

    _tag_names = ["trl", "gkd"]
    _name = "GKD"
    _paper = {
        "title": "On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes",
        "id": "2306.13649",
        # docstyle-ignore
        "citation": textwrap.dedent("""\
            @inproceedings{agarwal2024on-policy,
                title        = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}},
                author       = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem},
                year         = 2024,
                booktitle    = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
                publisher    = {OpenReview.net},
                url          = {https://openreview.net/forum?id=3zKtaqxLhW},
            }"""),
    }

    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
        teacher_model: Union[PreTrainedModel, nn.Module, str] = None,
        args: Optional[GKDConfig] = None,
        data_collator: Optional[DataCollator] = None,  # type: ignore
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = 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,
        peft_config: Optional["PeftConfig"] = None,
        formatting_func: Optional[Callable] = None,
    ):
        if not os.environ.get("TRL_EXPERIMENTAL_SILENCE"):
            warnings.warn(
                "This trainer will soon be moved to trl.experimental and is a candidate for removal. If you rely on "
                "it and want it to remain, please share your comments here: "
                "https://github.com/huggingface/trl/issues/4223. Silence this warning by setting environment variable "
                "TRL_EXPERIMENTAL_SILENCE=1."
            )
        # Ensure Trainer does not drop non-signature columns used by the collator [e.g., "prompts"]
        args.remove_unused_columns = False
        # Respect a user-provided data_collator; otherwise, provide a ChatML collator that
        if data_collator is None:
            data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_length)

        # Ensure SFTTrainer does not pre-process the dataset when using a ChatML collator,
        # so that raw conversational fields [e.g., "messages"] remain available to the collator.
        if args.dataset_kwargs is None:
            args.dataset_kwargs = {"skip_prepare_dataset": True}
        else:
            args.dataset_kwargs["skip_prepare_dataset"] = True

        # Liger fused GKD loss [JSD]
        self.use_liger_gkd_loss = False
        if args.use_liger_kernel:
            self.liger_jsd_loss = LigerFusedLinearJSDLoss(
                beta=args.beta,
                ignore_index=-100,
                temperature=args.temperature,
                compiled=False,
            )
            self.use_liger_gkd_loss = True

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

        if args.teacher_model_init_kwargs is None:
            teacher_model_init_kwargs = {}
        elif not isinstance(teacher_model, str):
            raise ValueError(
                "You passed teacher_model_init_kwargs to the GKDConfig, but your teacher_model is already instantiated."
            )
        else:
            teacher_model_init_kwargs = args.teacher_model_init_kwargs
            teacher_model_init_kwargs["dtype"] = (
                teacher_model_init_kwargs["dtype"]
                if teacher_model_init_kwargs["dtype"] in ["auto", None]
                else getattr(torch, teacher_model_init_kwargs["dtype"])
            )

        if isinstance(teacher_model, str):
            teacher_model = AutoModelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs)

        # Disable dropout in the model
        if args.disable_dropout:
            disable_dropout_in_model(self.model)

        if self.is_deepspeed_enabled:
            self.teacher_model = prepare_deepspeed(teacher_model, self.accelerator)
        else:
            self.teacher_model = self.accelerator.prepare_model(teacher_model, evaluation_mode=True)

        self.lmbda = args.lmbda
        self.beta = args.beta
        self.temperature = args.temperature
        self.seq_kd = args.seq_kd

        self.generation_config = GenerationConfig(
            max_new_tokens=args.max_new_tokens,
            temperature=args.temperature,
            do_sample=True,
            top_k=0,
            use_cache=False if args.gradient_checkpointing else True,
            pad_token_id=self.processing_class.pad_token_id,
        )
        # Set custom EOS tokens if they are specified by the model's generation
        # config. This is important for models with the Llama 3 chat template,
        # which use special tokens <|eot_id|> and <|eom_id|> to mark the end of
        # turns or messages.
        if (
            hasattr(self.model.generation_config, "eos_token_id")
            and self.model.generation_config.eos_token_id is not None
        ):
            self.generation_config.eos_token_id = self.model.generation_config.eos_token_id

    @staticmethod
    def generalized_jsd_loss(
        student_logits, teacher_logits, labels=None, beta=0.5, temperature=1.0, reduction="batchmean"
    ):
        """
        Compute the generalized Jensen-Shannon Divergence loss for knowledge distillation using F.kl_div. See Eq. (1)
        of https://huggingface.co/papers/2306.13649 for the definition.

        Args:
            student_logits:
                Tensor of shape (batch_size, sequence_length, vocab_size)
            teacher_logits:
                Tensor of shape (batch_size, sequence_length, vocab_size)
            labels:
                Tensor of shape (batch_size, sequence_length) with -100 for padding tokens to ignore when computing
                loss
            beta:
                Interpolation coefficient between 0 and 1 (default: 0.5)
            temperature:
                Softmax temperature (default: 1.0)
            reduction:
                Specifies the reduction to apply to the output (default: 'batchmean')

        Returns:
            loss: Scalar tensor with the generalized JSD loss
        """

        # Apply temperature scaling
        student_logits = student_logits / temperature
        teacher_logits = teacher_logits / temperature

        # Compute log probabilities for student and probabilities for teacher
        student_log_probs = F.log_softmax(student_logits, dim=-1)
        teacher_log_probs = F.log_softmax(teacher_logits, dim=-1)

        if beta == 0:
            jsd = F.kl_div(student_log_probs, teacher_log_probs, reduction="none", log_target=True)
        elif beta == 1:
            jsd = F.kl_div(teacher_log_probs, student_log_probs, reduction="none", log_target=True)
        else:
            # Compute the log of the mixture distribution
            # log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture
            beta = torch.tensor(beta, dtype=student_log_probs.dtype)
            mixture_log_probs = torch.logsumexp(
                torch.stack([student_log_probs + torch.log(1 - beta), teacher_log_probs + torch.log(beta)]),
                dim=0,
            )

            # Compute KL divergences using F.kl_div
            # PyTorch differs from the standard mathematical definition, so the order of the probability distributions is swapped compared to that defined in the paper.
            kl_teacher = F.kl_div(mixture_log_probs, teacher_log_probs, reduction="none", log_target=True)
            kl_student = F.kl_div(mixture_log_probs, student_log_probs, reduction="none", log_target=True)

            # Compute the Generalized Jensen-Shannon Divergence
            jsd = beta * kl_teacher + (1 - beta) * kl_student

        # Masking
        if labels is not None:
            mask = labels != -100
            jsd = jsd[mask]

        # Apply reduction
        if reduction == "batchmean":
            return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / jsd.size(0)
        elif reduction == "sum":
            return jsd.sum()
        elif reduction == "mean":
            return jsd.mean()
        else:
            return jsd

    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
        if self.use_liger_gkd_loss:
            # Forward only through the base models (avoid lm_head to save memory)
            unwrapped_student = self.accelerator.unwrap_model(model)
            if hasattr(unwrapped_student, "get_decoder") and unwrapped_student.get_decoder() is not None:
                base_student = unwrapped_student.get_decoder()
            else:
                base_student = getattr(
                    unwrapped_student, getattr(unwrapped_student, "base_model_prefix", "model"), unwrapped_student
                )

            student_outputs = base_student(
                input_ids=inputs["input_ids"],
                attention_mask=inputs["attention_mask"],
                output_hidden_states=True,
                use_cache=False,
            )

            self.teacher_model.eval()
            unwrapped_teacher = self.accelerator.unwrap_model(self.teacher_model)
            if hasattr(unwrapped_teacher, "get_decoder") and unwrapped_teacher.get_decoder() is not None:
                base_teacher = unwrapped_teacher.get_decoder()
            else:
                base_teacher = getattr(
                    unwrapped_teacher, getattr(unwrapped_teacher, "base_model_prefix", "model"), unwrapped_teacher
                )
            with torch.no_grad():
                teacher_outputs = base_teacher(
                    input_ids=inputs["input_ids"],
                    attention_mask=inputs["attention_mask"],
                    output_hidden_states=True,
                    use_cache=False,
                )

            # hidden states (shifted)
            student_hidden = student_outputs.last_hidden_state[:, :-1].contiguous()
            teacher_hidden = teacher_outputs.last_hidden_state[:, :-1].contiguous()

            # labels mask and labels (shifted)
            labels_mask = inputs["labels"] != -100
            masked_input_ids = torch.where(
                labels_mask, inputs["input_ids"], torch.full_like(inputs["input_ids"], -100)
            )
            true_labels = masked_input_ids[:, 1:].contiguous()

            # heads
            student_head = unwrapped_student.get_output_embeddings()
            teacher_head = unwrapped_teacher.get_output_embeddings()

            # liger fused jsd loss
            loss = self.liger_jsd_loss(
                student_input=student_hidden,
                student_weight=student_head.weight,
                teacher_input=teacher_hidden,
                teacher_weight=teacher_head.weight,
                true_labels=true_labels,
                student_bias=getattr(student_head, "bias", None),
                teacher_bias=getattr(teacher_head, "bias", None),
            )
        else:
            # compute student output
            student_outputs = model(
                input_ids=inputs["input_ids"],
                attention_mask=inputs["attention_mask"],
            )

            # compute teacher output in eval mode
            self.teacher_model.eval()
            with torch.no_grad():
                teacher_outputs = self.teacher_model(
                    input_ids=inputs["input_ids"],
                    attention_mask=inputs["attention_mask"],
                )

            # slice the logits for the generated tokens using the inputs["prompts"] lengths
            prompt_lengths = inputs["prompts"].shape[1]
            shifted_student_logits = student_outputs.logits[:, prompt_lengths - 1 : -1, :]
            shifted_teacher_logits = teacher_outputs.logits[:, prompt_lengths - 1 : -1, :]
            shifted_labels = inputs["labels"][:, prompt_lengths:]

            # compute loss
            loss = self.generalized_jsd_loss(
                student_logits=shifted_student_logits,
                teacher_logits=shifted_teacher_logits,
                labels=shifted_labels,
                beta=self.beta,
            )

        # empty cache
        empty_cache()

        # Return loss
        return (loss, student_outputs) if return_outputs else loss

    @staticmethod
    def generate_on_policy_outputs(model, inputs, generation_config, pad_token_id=None):
        # Generate output with respect to the prompt-only
        generated_outputs = model.generate(
            input_ids=inputs["prompts"],
            attention_mask=inputs.get("prompt_attention_mask", None),
            generation_config=generation_config,
            return_dict_in_generate=True,
        )

        # Get the generated token IDs
        generated_tokens = generated_outputs.sequences
        # Calculate new attention mask
        new_attention_mask = torch.ones_like(generated_tokens)
        new_labels = generated_tokens.clone()

        # If there's pad_token_id, set attention mask to 0 for padding tokens
        if pad_token_id is not None:
            new_labels[new_labels == pad_token_id] = -100
            new_attention_mask[generated_tokens == pad_token_id] = 0

        return generated_tokens, new_attention_mask, new_labels

    def training_step(
        self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
    ) -> torch.Tensor:
        """
        Perform a training step for the Generalized Knowledge Distillation (GKD) model.

        This method implements the on-policy learning approach described in the GKD paper. With probability
        `self.lmbda`, it generates new responses using the student model, which are then used for training instead of
        the original inputs.
        """
        if self.seq_kd:
            with unwrap_model_for_generation(self.teacher_model, self.accelerator) as unwrapped_model:
                new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs(
                    unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id
                )
            inputs["input_ids"] = new_input_ids
            inputs["attention_mask"] = new_attention_mask
            inputs["labels"] = new_labels
        if random.random() <= self.lmbda:
            with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
                new_input_ids, new_attention_mask, new_labels = self.generate_on_policy_outputs(
                    unwrapped_model, inputs, self.generation_config, self.processing_class.pad_token_id
                )
            inputs["input_ids"] = new_input_ids
            inputs["attention_mask"] = new_attention_mask
            inputs["labels"] = new_labels

        loss = super().training_step(model, inputs, num_items_in_batch)
        return loss
class UnslothGKDTrainer(_UnslothGKDTrainer):
    """
    Trainer for Generalized Knowledge Distillation (GKD) of language models.

    For details on GKD, see the paper: [On-Policy Distillation of Language Models: Learning from Self-Generated
    Mistakes](https://huggingface.co/papers/2306.13649).

    Args:
        model ([`~transformers.PreTrainedModel`] or `torch.nn.Module` or `str`, *optional*):
            Model to be trained, or the string identifier of the model to be instantiated from a pretrained model.
        teacher_model ([`~transformers.PreTrainedModel`] or `torch.nn.Module` or `str`, *optional*):
            Teacher model for knowledge distillation, or the string identifier of the model to be instantiated from a
            pretrained model.
        args ([`GKDConfig`], *optional*):
            Training arguments.
        data_collator ([`~transformers.DataCollator`], *optional*):
            Data collator to batch samples from the dataset. It defaults to a [`DataCollatorForChatML`] using the
            `processing_class`.
        train_dataset ([`~datasets.Dataset`], *optional*):
            Dataset for training.
        eval_dataset ([`~datasets.Dataset`] or `dict` of [`~datasets.Dataset`], *optional*):
            Dataset for evaluation.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*):
           Class to process the data.
        compute_metrics (`Callable`, *optional*):
            Function to compute metrics at evaluation. Must take in an [`~transformers.EvalPrediction`] and return a
            dictionary string to float.
        callbacks (`list` of [`~transformers.TrainerCallback`], *optional*):
            Callbacks to use during training.
        optimizers (`tuple` of `torch.optim.Optimizer` and `torch.optim.lr_scheduler.LambdaLR`, *optional*, defaults to `(None, None)`):
            Tuple containing the optimizer and the learning rate scheduler to use for training.
        preprocess_logits_for_metrics (`Callable`, *optional*):
            Function to preprocess the logits before computing the metrics. Must take in the `logits` and `labels` and
            return the logits to be used for metrics computation.
        peft_config ([`~peft.PeftConfig`], *optional*):
            PEFT configuration to use PEFT for training. If `None`, PEFT is not used. If provided, the `model` will be
            wrapped with the specified PEFT adapter.
        formatting_func (`Callable`, *optional*):
            Function to format the dataset. Must take in an example and return an example.
    
    """
    def __init__(
        self,
        model = None,
        teacher_model = None,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        compute_metrics = None,
        callbacks = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        formatting_func = None,
        **kwargs
    ):
        if args is None: args = UnslothGKDConfig()
        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 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('gkd_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(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        super().__init__(
            model = model,
            teacher_model = teacher_model,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            compute_metrics = compute_metrics,
            callbacks = callbacks,
            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