unsloth_compiled_cache/UnslothNashMDTrainer.py

"""
2026.3.2
2026.3.4
5.3.0
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.nash_md_trainer import (Any, BaseImageProcessor, BasePairwiseJudge, Callable, Dataset, EvalPrediction, F, FeatureExtractionMixin, GeometricMixtureWrapper, IterableDataset, NashMDConfig, NashMDTrainer, OnlineDPOTrainer, OptimizerNames, Optional, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, TrainerCallback, Union, empty_cache, get_reward, is_conversational, is_peft_available, jinja2, maybe_apply_chat_template, nn, selective_log_softmax, textwrap, torch, truncate_right, unwrap_model_for_generation)


import os
import math
import logging
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

def sanitize_logprob(logprob):
    """Local port of trl.scripts.vllm_serve.sanitize_logprob.
    Filters NaN logprobs from vLLM outputs."""
    value = logprob.logprob
    if math.isnan(value):
        logging.getLogger(__name__).warning(
            f"Generated NaN logprob, token logprob '{logprob}' will be ignored"
        )
        return None
    return value
@dataclass
class UnslothNashMDConfig(NashMDConfig):
    """
    
    Configuration class for the [`NashMDTrainer`].

    Subclass of [`OnlineDPOConfig`] we can use all its arguments and add the following:

    Parameters:
        mixture_coef (`float` or `list[float]`, *optional*, defaults to `0.5`):
            Logit mixture coefficient for the model and reference model. If a list of floats is provided then the
            mixture coefficient is selected for each new epoch and the last coefficient is used for the rest of the
            epochs.
    
    """
    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,
        reward_model_path = None,
        judge = None,
        max_new_tokens = 64,
        max_length = 512,
        temperature = 0.9,
        top_p = 1.0,
        top_k = None,
        min_p = None,
        repetition_penalty = 1.0,
        generation_kwargs = {},
        use_transformers_paged = False,
        cache_implementation = None,
        missing_eos_penalty = None,
        loss_type = 'sigmoid',
        disable_dropout = True,
        use_vllm = False,
        vllm_model_impl = 'vllm',
        vllm_guided_decoding_regex = None,
        vllm_gpu_memory_utilization = 0.55,
        vllm_mode = 'colocate',
        vllm_server_base_url = None,
        vllm_server_host = '0.0.0.0',
        vllm_server_port = 8000,
        vllm_server_timeout = 240.0,
        vllm_tensor_parallel_size = 1,
        ds3_gather_for_generation = True,
        model_init_kwargs = None,
        reward_weights = None,
        dataset_num_proc = None,
        gpu_memory_utilization = None,
        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 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,
            reward_model_path = reward_model_path,
            judge = judge,
            max_new_tokens = max_new_tokens,
            max_length = max_length,
            temperature = temperature,
            top_p = top_p,
            top_k = top_k,
            min_p = min_p,
            repetition_penalty = repetition_penalty,
            generation_kwargs = generation_kwargs,
            use_transformers_paged = use_transformers_paged,
            cache_implementation = cache_implementation,
            missing_eos_penalty = missing_eos_penalty,
            loss_type = loss_type,
            disable_dropout = disable_dropout,
            use_vllm = use_vllm,
            vllm_model_impl = vllm_model_impl,
            vllm_guided_decoding_regex = vllm_guided_decoding_regex,
            vllm_gpu_memory_utilization = vllm_gpu_memory_utilization,
            vllm_mode = vllm_mode,
            vllm_server_base_url = vllm_server_base_url,
            vllm_server_host = vllm_server_host,
            vllm_server_port = vllm_server_port,
            vllm_server_timeout = vllm_server_timeout,
            vllm_tensor_parallel_size = vllm_tensor_parallel_size,
            ds3_gather_for_generation = ds3_gather_for_generation,
            model_init_kwargs = model_init_kwargs,
            reward_weights = reward_weights,
            dataset_num_proc = dataset_num_proc,
            gpu_memory_utilization = gpu_memory_utilization,**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 _UnslothNashMDTrainer(OnlineDPOTrainer):
    """"""

    _tag_names = ["trl", "nash-md"]
    _name = "Nash-MD"
    _paper = {
        "title": "Nash Learning from Human Feedback",
        "id": "2312.00886",
        # docstyle-ignore
        "citation": textwrap.dedent("""\
            @inproceedings{munos2024nash,
                title        = {{Nash Learning from Human Feedback}},
                author       = {R{\'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot},
                year         = 2024,
                booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
                publisher    = {OpenReview.net},
                url          = {https://openreview.net/forum?id=Y5AmNYiyCQ}
            }"""),
    }

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module] = None,
        ref_model: Union[PreTrainedModel, nn.Module] = None,
        reward_funcs: Union[PreTrainedModel, nn.Module, None] = None,
        judge: Optional[BasePairwiseJudge] = None,
        args: Optional[NashMDConfig] = None,
        data_collator: Optional[Callable] = None,
        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        peft_config: Optional[dict] = 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,
        # Deprecated parameters
        reward_model: Optional[Union[PreTrainedModel, nn.Module]] = None,
    ) -> None:
        super().__init__(
            model=model,
            ref_model=ref_model,
            reward_funcs=reward_funcs,
            judge=judge,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            reward_processing_classes=processing_class,
            peft_config=peft_config,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
            reward_model=reward_model,
        )

        self._mixture_coef = self.args.mixture_coef

        # Overwrite the stats dictionary to include NashMD specific statistics
        self.stats = {
            # Remove "non_score_reward", "rlhf_reward", "scores_margin"
            # Add "mixture_coef"
            "loss/kl": [],
            "objective/entropy": [],
            "loss/score": [],
            "rewards/probabilities": [],
            "rewards/accuracies": [],
            "rewards/margins": [],
            "logps/chosen": [],
            "logps/rejected": [],
            "val/model_contain_eos_token": [],
            "val/ref_contain_eos_token": [],
            "beta": [],
            "mixture_coef": [],
        }
        if self.reward_funcs is not None:
            if len(self.reward_funcs) != 1:
                raise ValueError("NashMDTrainer only supports one reward function/model.")
            self.reward_funcs = self.reward_funcs[0]
            self.stats["rewards/chosen"] = []
            self.stats["rewards/rejected"] = []

    @property
    def mixture_coef(self):
        if isinstance(self._mixture_coef, list):
            epoch = self.state.epoch
            return self._mixture_coef[epoch] if epoch < len(self._mixture_coef) else self._mixture_coef[-1]
        else:
            return self._mixture_coef

    def _generate_completions(self, model, prompts):
        # Generate completions from the policy model.
        with unwrap_model_for_generation(model, self.accelerator) as unwrapped_policy_for_gen_ctx:
            model_output = unwrapped_policy_for_gen_ctx.generate(
                input_ids=prompts["input_ids"],
                attention_mask=prompts["attention_mask"],
                generation_config=self.generation_config,
            )

        # Get the DDP/FSDP unwrapped version of the main model.
        # This will be the policy model for GeometricMixtureWrapper (PEFT adapters active if PEFT is used).
        policy_model_for_gmw = self.accelerator.unwrap_model(model)

        # Determine the correct reference model for GeometricMixtureWrapper.
        # This also needs to be DDP/FSDP unwrapped.
        ref_model_for_gmw: torch.nn.Module
        if self.ref_model is None:
            # No explicit ref_model is provided.
            # Use the base of the main `model` if it's a PEFT model.
            # policy_model_for_gmw is already DDP-unwrapped.
            if is_peft_available() and isinstance(policy_model_for_gmw, PeftModel):
                ref_model_for_gmw = policy_model_for_gmw.get_base_model()
            else:
                # Not a PEFT model (or PEFT not available), or already a base model.
                # Use the DDP-unwrapped policy model itself as the reference.
                ref_model_for_gmw = policy_model_for_gmw
        else:
            # An explicit ref_model is provided. Unwrap it for DDP/FSDP.
            ref_model_for_gmw = self.accelerator.unwrap_model(self.ref_model)

        # Both models given to GeometricMixtureWrapper (policy_model_for_gmw and ref_model_for_gmw) are DDP-unwrapped.
        with torch.no_grad():  # Ensure no_grad context for mixture model generation
            mixture_model = GeometricMixtureWrapper(
                model=policy_model_for_gmw,
                ref_model=ref_model_for_gmw,
                generation_config=self.generation_config,
                mixture_coef=self.mixture_coef,
                device=self.accelerator.device,
            )

            mixture_output = mixture_model.generate(
                input_ids=prompts["input_ids"],
                attention_mask=prompts["attention_mask"],
                generation_config=self.generation_config,
            )

        return model_output, mixture_output

    def _process_completions(self, model_output, mixture_output, prompts):
        context_length = prompts["input_ids"].shape[1]

        # Process model completions
        model_completion_ids = model_output[:, context_length:]
        model_completion_ids, model_completion_mask = truncate_right(
            model_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id
        )
        model_data = {
            "input_ids": torch.cat((prompts["input_ids"], model_completion_ids), dim=1),
            "attention_mask": torch.cat((prompts["attention_mask"], model_completion_mask), dim=1),
            "raw": prompts["raw"],
        }

        # Process reference model completions
        mixture_completion_ids = mixture_output[:, context_length:]
        mixture_completion_ids, mixture_completion_mask = truncate_right(
            mixture_completion_ids, self.processing_class.eos_token_id, self.processing_class.pad_token_id
        )
        mixture_data = {
            "input_ids": torch.cat((prompts["input_ids"], mixture_completion_ids), dim=1),
            "attention_mask": torch.cat((prompts["attention_mask"], mixture_completion_mask), dim=1),
            "raw": prompts["raw"],
        }

        return model_data, mixture_data

    def _compute_rewards(self, model_data, mixture_data, context_length):
        with torch.no_grad():
            _, model_scores, _ = get_reward(
                self.reward_funcs, model_data["input_ids"], self.processing_class.pad_token_id, context_length
            )
            _, mixture_scores, _ = get_reward(
                self.reward_funcs, mixture_data["input_ids"], self.processing_class.pad_token_id, context_length
            )

        # Apply EOS penalty if needed
        if self.args.missing_eos_penalty is not None:
            model_contain_eos = torch.any(model_data["input_ids"] == self.processing_class.eos_token_id, dim=-1)
            mixture_contain_eos = torch.any(mixture_data["input_ids"] == self.processing_class.eos_token_id, dim=-1)
            model_scores[~model_contain_eos] -= self.args.missing_eos_penalty
            mixture_scores[~mixture_contain_eos] -= self.args.missing_eos_penalty

        return model_scores, mixture_scores

    def _compute_judge(self, model_data, mixture_data, context_length):
        prompts = model_data["raw"]
        model_data_completions = self.processing_class.batch_decode(
            model_data["input_ids"][:, context_length:], skip_special_tokens=True
        )
        model_data_completions = [completion.strip() for completion in model_data_completions]

        mixture_data_completions = self.processing_class.batch_decode(
            mixture_data["input_ids"][:, context_length:], skip_special_tokens=True
        )
        mixture_data_completions = [completion.strip() for completion in mixture_data_completions]
        if is_conversational({"prompt": prompts[0]}):
            model_data_completions = [
                [{"role": "assistant", "content": completion}] for completion in model_data_completions
            ]
            environment = jinja2.Environment()
            template = environment.from_string(SIMPLE_CHAT_TEMPLATE)
            prompts = [template.render(messages=message) for message in prompts]
            model_data_completions = [template.render(messages=completion) for completion in model_data_completions]

            mixture_data_completions = [
                [{"role": "assistant", "content": completion}] for completion in mixture_data_completions
            ]
            mixture_data_completions = [
                template.render(messages=completion) for completion in mixture_data_completions
            ]

        probability = self.judge.judge(
            prompts,
            list(zip(model_data_completions, mixture_data_completions)),
            return_scores=True,
        )
        return torch.tensor(probability, device=model_data["input_ids"].device)

    def _compute_logprobs(self, model, model_data, context_length):
        def compute_logprobs_for_data(m, data):
            output = m(data["input_ids"], attention_mask=data["attention_mask"])
            logits = output.logits[:, context_length - 1 : -1]
            token_logprobs = selective_log_softmax(logits, data["input_ids"][:, context_length:])
            return token_logprobs

        # Compute logprobs for model completions under the model
        model_logprobs_model_data = compute_logprobs_for_data(model, model_data)

        # Compute logprobs of model completions under the reference model
        with torch.no_grad():
            if self.ref_model is None:
                with model.disable_adapter():
                    ref_logprobs_model_data = compute_logprobs_for_data(model, model_data)
            else:
                ref_logprobs_model_data = compute_logprobs_for_data(self.ref_model, model_data)

        # Mask padding tokens
        model_padding_mask = model_data["attention_mask"][:, context_length:] == 0
        model_logprobs_model_data = model_logprobs_model_data.masked_fill(model_padding_mask, 0.0)
        ref_logprobs_model_data = ref_logprobs_model_data.masked_fill(model_padding_mask, 0.0)

        return (model_logprobs_model_data, ref_logprobs_model_data)

    def _compute_losses(
        self,
        model_logprobs_model_data,
        ref_logprobs_model_data,
        probability,
    ):
        # reinforce score where 0.5 is a control variate
        score = (probability - 0.5) * model_logprobs_model_data.sum(1)

        # kl divergence via reinforce
        with torch.no_grad():
            log_ratio = model_logprobs_model_data - ref_logprobs_model_data
            kl_div_log = log_ratio.sum(1)
        kl_div_loss = (log_ratio * model_logprobs_model_data).sum(1)

        # final loss
        loss = self.beta * kl_div_loss - score

        return loss.mean(), score, kl_div_log

    def _log_statistics(
        self,
        model_data,
        mixture_data,
        model_logprobs_model_data,
        ref_logprobs_model_data,
        probability,
        score,
        kl_div,
        context_length,
        model_scores=None,
        mixture_scores=None,
    ):
        # Helper function to gather and compute mean
        def gather_mean(tensor):
            return self.accelerator.gather_for_metrics(tensor).mean().item()

        # Log score
        self.stats["loss/score"].append(gather_mean(score))
        # Log KL divergence
        self.stats["loss/kl"].append(gather_mean(kl_div))

        # Log logprobs
        model_logprobs_model_data_sum = model_logprobs_model_data.sum(1)
        ref_logprobs_model_data_sum = ref_logprobs_model_data.sum(1)

        self.stats["logps/chosen"].append(gather_mean(model_logprobs_model_data_sum))
        self.stats["logps/rejected"].append(gather_mean(ref_logprobs_model_data_sum))

        # Log rewards
        if self.reward_funcs is not None:
            self.stats["rewards/chosen"].append(gather_mean(model_scores))
            self.stats["rewards/rejected"].append(gather_mean(mixture_scores))

        # Log probabilities
        self.stats["rewards/probabilities"].append(gather_mean(probability))

        # Calculate entropy for model data
        entropy_model_data = -model_logprobs_model_data.sum(1)
        self.stats["objective/entropy"].append(gather_mean(entropy_model_data))

        # Calculate margins
        margin = model_logprobs_model_data_sum - ref_logprobs_model_data_sum
        self.stats["rewards/margins"].append(gather_mean(margin))

        # Calculate accuracy
        accuracy = (margin > 0).float()
        self.stats["rewards/accuracies"].append(gather_mean(accuracy))

        # Log EOS token statistics
        model_eos = (model_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1)
        mixture_eos = (mixture_data["input_ids"][:, context_length:] == self.processing_class.eos_token_id).any(dim=1)
        self.stats["val/model_contain_eos_token"].append(gather_mean(model_eos.float()))
        self.stats["val/ref_contain_eos_token"].append(gather_mean(mixture_eos.float()))

        # Log beta and mixture coef
        self.stats["beta"].append(self.beta)
        self.stats["mixture_coef"].append(self.mixture_coef)

    def training_step(
        self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None
    ) -> torch.Tensor:
        model.train()

        # Apply chat template and tokenize the input
        batch_size = len(next(iter(inputs.values())))
        prompts = inputs["prompt"]
        inputs = [{k: v[i] for k, v in inputs.items()} for i in range(batch_size)]
        inputs = [maybe_apply_chat_template(x, self.processing_class) for x in inputs]
        inputs = [self.tokenize_row(x, self.model.config.is_encoder_decoder, self.processing_class) for x in inputs]
        inputs = self.data_collator(inputs)

        # need the prompt_ only
        inputs = self._prepare_inputs(inputs)
        context_length = inputs["prompt_input_ids"].shape[1]
        prompts = {
            "input_ids": inputs["prompt_input_ids"],
            "attention_mask": inputs["prompt_attention_mask"],
            "raw": prompts,
        }
        del inputs

        # Sample completions from both the model and the reference model
        model_output, mixture_output = self._generate_completions(model, prompts)

        # Process model completions
        model_data, mixture_data = self._process_completions(model_output, mixture_output, prompts)

        # Compute rewards
        if self.reward_funcs is not None:
            model_scores, mixture_scores = self._compute_rewards(model_data, mixture_data, context_length)
            # probability of the model data vs the mixture data
            probability = F.sigmoid(model_scores - mixture_scores)
        else:
            model_scores, mixture_scores = None, None
            probability = self._compute_judge(model_data, mixture_data, context_length)

        # Compute logprobs
        model_logprobs_model_data, ref_logprobs_model_data = self._compute_logprobs(model, model_data, context_length)

        # Compute loss
        loss, score, kl_div = self._compute_losses(model_logprobs_model_data, ref_logprobs_model_data, probability)

        # Log everything
        self._log_statistics(
            model_data,
            mixture_data,
            model_logprobs_model_data.detach(),
            ref_logprobs_model_data,
            probability,
            score.detach(),
            kl_div.detach(),
            context_length,
            model_scores,
            mixture_scores,
        )

        if (
            self.args.torch_empty_cache_steps is not None
            and self.state.global_step % self.args.torch_empty_cache_steps == 0
        ):
            empty_cache()

        kwargs = {}
        # For LOMO optimizers you need to explicitly use the learning rate
        if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]:
            kwargs["learning_rate"] = self._get_learning_rate()

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training

        self.accelerator.backward(loss, **kwargs)

        return loss.detach() / self.args.gradient_accumulation_steps
class UnslothNashMDTrainer(_UnslothNashMDTrainer):
    """
    
    Trainer for the Nash-MD method.

    It is implemented as a subclass of [`OnlineDPOTrainer`].

    Args:
        model ([`~transformers.PreTrainedModel`]):
            The model to train, preferably an `AutoModelForCausalLM`.
        ref_model ([`PreTrainedModelWrapper`]):
            Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation
            and loss. If no reference model is provided, the trainer will create a reference model with the same
            architecture as the model to be optimized.
        reward_funcs ([`~transformers.PreTrainedModel`]):
            The reward model to score completions with, preferably an
            [`~transformers.AutoModelForSequenceClassification`].
        judge ([`BasePairwiseJudge`]):
            The judge to use for pairwise comparison of model completions.
        args ([`NashMDConfig`]):
            The NashMD config arguments to use for training.
        data_collator ([`~transformers.DataCollator`]):
            The data collator to use for training. If None is specified, the default data collator
            ([`DPODataCollatorWithPadding`]) will be used which will pad the sequences to the maximum length of the
            sequences in the batch, given a dataset of paired sequences.
        train_dataset ([`~datasets.Dataset`]):
            The dataset to use for training.
        eval_dataset ([`~datasets.Dataset`]):
            The dataset to use for evaluation.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.BaseImageProcessor`], [`~transformers.FeatureExtractionMixin`] or [`~transformers.ProcessorMixin`], *optional*):
            Processing class used to process the data. If provided, will be used to automatically process the inputs
            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
            reuse the fine-tuned model.
        peft_config (`dict`):
            The peft config to use for training.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to
            metric values.
        callbacks (`list[transformers.TrainerCallback]`):
            The callbacks to use for training.
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.

        reward_model:

            <Deprecated version="0.22.0">

            This parameter is deprecated and will be removed in version 0.25.0. Use `reward_funcs` instead.

            </Deprecated>
    
    """
    def __init__(
        self,
        model = None,
        ref_model = None,
        reward_funcs = None,
        judge = None,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        peft_config = None,
        compute_metrics = None,
        callbacks = None,
        preprocess_logits_for_metrics = None,
        reward_model = None,
        **kwargs
    ):
        if args is None: args = UnslothNashMDConfig()
        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 model is not None:
            _warnings_issued = getattr(model, 'warnings_issued', None)
            if _warnings_issued is None:
                model.warnings_issued = {}
            elif not isinstance(_warnings_issued, dict):
                try:
                    model.warnings_issued = dict(_warnings_issued)
                except Exception:
                    model.warnings_issued = {}
        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('nash_md_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,
            ref_model = ref_model,
            reward_funcs = reward_funcs,
            judge = judge,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            peft_config = peft_config,
            compute_metrics = compute_metrics,
            callbacks = callbacks,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            reward_model = reward_model,**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