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	"""
	2025.10.1
	2025.10.1
	4.56.2
	0.22.2
	__UNSLOTH_VERSIONING__
	"""

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

	from torch import Tensor
	import torch
	import torch.nn as nn
	from torch.nn import functional as F
	from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
	from trl.trainer.rloo_trainer import (Any, AutoConfig, AutoModelForSequenceClassification, AutoProcessor, AutoTokenizer, DataLoader, Dataset, FSDP, GenerationConfig, IterableDataset, Optional, Path, PeftConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RLOOConfig, RLOOTrainer, RepeatSampler, RewardFunc, Sampler, SyncRefModelCallback, Trainer, TrainerCallback, Union, VLLMClient, apply_chat_template, broadcast_object_list, copy, datasets, defaultdict, deque, disable_dropout_in_model, entropy_from_logits, gather, gather_object, generate_model_card, get_comet_experiment_url, identity, inspect, is_conversational, is_datasets_available, is_flash_attn_2_available, is_peft_model, is_rich_available, is_vllm_available, is_wandb_available, logger, logging, maybe_apply_chat_template, nanmax, nanmin, nanstd, nn, nullcontext, os, pad, partial, prepare_deepspeed, prepare_fsdp, prepare_peft_model, print_prompt_completions_sample, profiling_context, profiling_decorator, re, seed_worker, selective_log_softmax, set_seed, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, textwrap, torch, transformers, truncate_with_protected_tokens, unsplit_pixel_values_by_grid, unwrap_model_for_generation, wandb, warnings, Any, FSDP, Union, apply_chat_template, broadcast_object_list, copy, gather, gather_object, is_conversational, is_flash_attn_2_available, logging, maybe_apply_chat_template, nanstd, nullcontext, os, pad, profiling_context, re, torch, transformers, truncate_with_protected_tokens, unwrap_model_for_generation, FSDP, gather, is_peft_model, nn, nullcontext, os, profiling_decorator, re, Any, Union, profiling_decorator, re, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, torch, unsplit_pixel_values_by_grid, Optional, PreTrainedModel, Trainer, logger, os, re, torch, FSDP, nn, os, re, FSDP, nn, re, torch)


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

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

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

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

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

	prompt_section = input_ids[:, :-logits_to_keep]

	padding_mask = (prompt_section == pad_token_id)

	pad_token_counts = padding_mask.sum(dim=1)

	return pad_token_counts

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

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

	num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt

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

	non_padding_mask = (completion_input_ids != pad_token_id)

	final_mask = shift_mask & non_padding_mask

	return final_mask

	def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
	"""
	Moves all padding tokens in each sequence of a batch to the right.
	"""
	mask = (tensor != pad_id)
	# Must do stable=True since binary mark is unordered
	sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
	packed_tensor = torch.gather(tensor, 1, sorted_indices)
	return packed_tensor
	def vLLMSamplingParams(**kwargs):
	from vllm import SamplingParams
	sampling_params = SamplingParams(**kwargs)
	sampling_params._set_kwargs = kwargs
	return sampling_params
	@dataclass
	class UnslothRLOOConfig(RLOOConfig):
	"""

	Configuration class for the [`RLOOTrainer`].

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

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

	Parameters:
	> Parameters that control the model and reference model

	model_init_kwargs (`str`, `dict[str, Any]` or `None`, optional, defaults to `None`):
	Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
	argument of the [`GRPOTrainer`] is provided as a string.
	disable_dropout (`bool`, optional, defaults to `False`):
	Whether to disable dropout in the model. This is useful for training with a reference model, as it prevents
	the model from generating different logprobs for the same input.

	> Parameters that control the data preprocessing

	remove_unused_columns (`bool`, optional, defaults to `False`):
	Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that
	requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`.
	max_prompt_length (`int` or `None`, optional, defaults to `512`):
	Maximum length of the prompt. If the prompt is longer than this value, it will be truncated left.
	num_generations (`int` or `None`, optional, defaults to `2`):
	Number of generations per prompt to sample. The effective batch size (num_processes * per_device_batch_size
	* gradient_accumulation_steps) must be evenly divisible by this value.
	max_completion_length (`int` or `None`, optional, defaults to `256`):
	Maximum length of the generated completion.
	ds3_gather_for_generation (`bool`, optional, defaults to `True`):
	This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
	improving generation speed. However, disabling this option allows training models that exceed the VRAM
	capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible
	with vLLM generation.
	shuffle_dataset (`bool`, optional, defaults to `True`):
	Whether to shuffle the training dataset.

	> Parameters that control generation

	generation_batch_size: (`int` or `None`, optional, defaults to `None`):
	Batch size to use for generation. If `None`, it defaults to the effective training batch size:
	`per_device_train_batch_size * num_processes * steps_per_generation`. In other words, there is one
	generation batch processed per optimization step. Mutually exclusive with `steps_per_generation`.
	steps_per_generation: (`int` or `None`, optional, defaults to `None`):
	Number of steps per generation. If `None`, it defaults to `gradient_accumulation_steps`. Mutually exclusive
	with `generation_batch_size`.
	temperature (`float`, defaults to `1.0`):
	Temperature for sampling. The higher the temperature, the more random the completions.
	top_p (`float`, optional, defaults to `1.0`):
	Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to
	`1.0` to consider all tokens.
	top_k (`int` or `None`, optional, defaults to `None`):
	Number of highest probability vocabulary tokens to keep for top-k-filtering. If `None`, top-k-filtering is
	disabled and all tokens are considered.
	min_p (`float` or `None`, optional, defaults to `None`):
	Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
	value between `0.0` and `1.0`. Typical values are in the `0.01-0.2` range.
	repetition_penalty (`float`, optional, defaults to `1.0`):
	Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far.
	Values > `1.0` encourage the model to use new tokens, while values < `1.0` encourage the model to repeat
	tokens.
	use_transformers_paged (`bool`, optional, defaults to `False`):
	Whether to use the `transformers` paged implementation for generation. If set to `True`, the `transformers`
	paged implementation will be used for generation instead of the default padded implementation. This
	parameter is only effective when `use_vllm` is set to `False`.
	cache_implementation (`str` or `None`, optional, defaults to `None`):
	Implementation of the cache method for faster generation when `use_vllm` is set to `False`.
	generation_kwargs (`dict[str, Any]` or `None`, optional, defaults to `None`):
	Additional keyword arguments to pass to `GenerationConfig` (if using transformers) or `SamplingParams` (if
	using vLLM) when sampling completions. This can be used to further customize the generation behavior, such
	as setting `suppress_tokens`, `num_beams`, etc. If it contains keys that conflict with the other generation
	parameters (like `min_p`, `top_p`, etc.), they will override them.

	> Parameters that control generation acceleration powered by vLLM

	use_vllm (`bool`, optional, defaults to `False`):
	Whether to use vLLM for generating completions. If set to `True`, the trainer will use vLLM for generation
	instead of the default model.generate(). Requires `vllm` to be installed.
	vllm_mode (`str`, optional, defaults to `"server"`):
	Mode to use for vLLM integration when `use_vllm` is set to `True`. Must be one of `"server"` or
	`"colocate"`.

	- `"server"`: The trainer will send generation requests to a separate vLLM server. Make sure a TRL vLLM
	server is running (start with `trl vllm-serve`).
	- `"colocate"`: vLLM will run in the same process and share the training GPUs. This avoids the need for a
	separate server but may cause resource contention with training.
	vllm_guided_decoding_regex (`str` or `None`, optional, defaults to `None`):
	Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled.

	> Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`)

	vllm_server_base_url (`str` or `None`, optional, defaults to `None`):
	Base URL for the vLLM server (e.g., `"http://localhost:8000"`). If provided, `vllm_server_host` and
	`vllm_server_port` are ignored.
	vllm_server_host (`str`, optional, defaults to `"0.0.0.0"`):
	Host of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided.
	vllm_server_port (`int`, optional, defaults to `8000`):
	Port of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided.
	vllm_server_timeout (`float`, optional, defaults to `240.0`):
	Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up after the
	timeout, a `ConnectionError` is raised.

	> Parameters that control colocated vLLM execution (only used when `vllm_mode` is `"colocate"`)

	vllm_gpu_memory_utilization (`float`, optional, defaults to `0.3`):
	Control the GPU memory utilization for vLLM. This setting only applies when `vllm_mode` is set to
	`"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
	launching the vLLM server via the `--vllm_gpu_memory_utilization` flag.
	vllm_tensor_parallel_size (`int`, optional, defaults to `1`):
	Control the tensor parallel size for vLLM. This setting only applies when `vllm_mode` is set to
	`"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
	launching the vLLM server via the `--vllm_tensor_parallel_size` flag.
	vllm_model_impl (`str`, optional, defaults to `"vllm"`):
	Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use
	the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model
	implementation.

	> Parameters that control the training

	beta (`float`, optional, defaults to `0.05`):
	KL coefficient. If `0.0`, the reference model is not loaded, reducing memory usage and improving training
	speed.
	num_iterations (`int`, optional, defaults to `1`):
	Number of iterations per batch (denoted as μ in the algorithm).
	epsilon (`float`, optional, defaults to `0.2`):
	Epsilon value for clipping.
	epsilon_high (`float` or `None`, optional, defaults to `None`):
	Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the lower-bound
	specified in argument `epsilon`. Paper [DAPO](https://huggingface.co/papers/2503.14476) recommends `0.28`.
	reward_weights (`list[float]` or `None`, optional, defaults to `None`):
	Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are
	weighted equally with weight `1.0`.
	normalize_advantages (`bool`, optional, defaults to `False`):
	Whether to normalize advantages. Normalization is done per generation batch to have mean `0.0` and standard
	deviation of `1.0`.
	reward_clip_range (`tuple[float, float]` or `None`, optional, defaults to `None`):
	Clip range for rewards as (min, max). If `None`, no clipping is applied.
	mask_truncated_completions (`bool`, optional, defaults to `False`):
	When enabled, truncated completions are excluded from the loss calculation, preventing them from being
	incorrectly penalized and introducing noise during training. According to the
	[DAPO](https://huggingface.co/papers/2503.14476) paper, this is a good practice for training stability.
	sync_ref_model (`bool`, optional, defaults to `False`):
	Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using
	the `ref_model_mixup_alpha` parameter. This synchronization originates from the
	[TR-DPO](https://huggingface.co/papers/2404.09656) paper.
	ref_model_mixup_alpha (`float`, optional, defaults to `0.6`):
	α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix
	between the current policy and the previous reference policy during updates. The reference policy is
	updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you
	must set `sync_ref_model=True`.
	ref_model_sync_steps (`int`, optional, defaults to `512`):
	τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how
	frequently the current policy is synchronized with the reference policy. To use this parameter, you must
	set `sync_ref_model=True`.

	> Parameters that control the logging

	log_completions (`bool`, optional, defaults to `False`):
	Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is installed,
	it prints the sample. If `wandb` logging is enabled, it logs it to `wandb`.
	num_completions_to_print (`int` or `None`, optional, defaults to `None`):
	Number of completions to print with `rich`. If `None`, all completions are logged.
	wandb_log_unique_prompts (`bool`, optional, defaults to `False`):
	Whether to log unique prompts in wandb. If `True`, only unique prompts are logged. If `False`, all prompts
	are logged.

	"""
	vllm_sampling_params: Optional[Any] = field(
	default = None,
	metadata = {'help': 'vLLM SamplingParams'},
	)
	unsloth_num_chunks : Optional[int] = field(
	default = -1,
	metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
	)

	def __init__(
	self,
	output_dir = None,
	overwrite_output_dir = None,
	do_train = False,
	do_eval = False,
	do_predict = False,
	eval_strategy = 'no',
	prediction_loss_only = False,
	per_device_train_batch_size = 4,
	per_device_eval_batch_size = 4,
	per_gpu_train_batch_size = None,
	per_gpu_eval_batch_size = None,
	gradient_accumulation_steps = 2,
	eval_accumulation_steps = 2,
	eval_delay = 0,
	torch_empty_cache_steps = 250,
	learning_rate = 5e-05,
	weight_decay = 0.01,
	adam_beta1 = 0.9,
	adam_beta2 = 0.999,
	adam_epsilon = 1e-08,
	max_grad_norm = 1.0,
	num_train_epochs = 3.0,
	max_steps = -1,
	lr_scheduler_type = 'linear',
	warmup_ratio = 0.1,
	warmup_steps = 0,
	log_level = 'passive',
	log_level_replica = 'warning',
	log_on_each_node = True,
	logging_dir = None,
	logging_strategy = 'steps',
	logging_first_step = False,
	logging_steps = 1,
	logging_nan_inf_filter = False,
	save_strategy = 'steps',
	save_steps = 500,
	save_total_limit = None,
	save_safetensors = True,
	save_on_each_node = False,
	save_only_model = False,
	restore_callback_states_from_checkpoint = False,
	no_cuda = False,
	use_cpu = False,
	use_mps_device = False,
	seed = 3407,
	data_seed = 3407,
	jit_mode_eval = False,
	use_ipex = False,
	bf16 = False,
	fp16 = False,
	fp16_opt_level = 'O1',
	half_precision_backend = 'auto',
	bf16_full_eval = False,
	fp16_full_eval = False,
	tf32 = None,
	local_rank = -1,
	ddp_backend = None,
	tpu_num_cores = None,
	tpu_metrics_debug = False,
	debug = '',
	dataloader_drop_last = False,
	eval_steps = None,
	dataloader_num_workers = 0,
	dataloader_prefetch_factor = None,
	past_index = -1,
	run_name = None,
	disable_tqdm = None,
	remove_unused_columns = False,
	label_names = None,
	load_best_model_at_end = False,
	metric_for_best_model = None,
	greater_is_better = None,
	ignore_data_skip = False,
	fsdp = '',
	fsdp_min_num_params = 0,
	fsdp_config = None,
	fsdp_transformer_layer_cls_to_wrap = None,
	accelerator_config = None,
	parallelism_config = None,
	deepspeed = None,
	label_smoothing_factor = 0.0,
	optim = 'adamw_8bit',
	optim_args = None,
	adafactor = False,
	group_by_length = False,
	length_column_name = 'length',
	report_to = None,
	ddp_find_unused_parameters = None,
	ddp_bucket_cap_mb = None,
	ddp_broadcast_buffers = None,
	dataloader_pin_memory = True,
	dataloader_persistent_workers = False,
	skip_memory_metrics = True,
	use_legacy_prediction_loop = False,
	push_to_hub = False,
	resume_from_checkpoint = None,
	hub_model_id = None,
	hub_strategy = 'every_save',
	hub_token = None,
	hub_private_repo = None,
	hub_always_push = False,
	hub_revision = None,
	gradient_checkpointing = True,
	gradient_checkpointing_kwargs = None,
	include_inputs_for_metrics = False,
	eval_do_concat_batches = True,
	fp16_backend = 'auto',
	push_to_hub_model_id = None,
	push_to_hub_organization = None,
	push_to_hub_token = None,
	mp_parameters = '',
	auto_find_batch_size = False,
	full_determinism = False,
	torchdynamo = None,
	ray_scope = 'last',
	ddp_timeout = 1800,
	torch_compile = False,
	torch_compile_backend = None,
	torch_compile_mode = None,
	include_tokens_per_second = False,
	include_num_input_tokens_seen = False,
	neftune_noise_alpha = None,
	optim_target_modules = None,
	batch_eval_metrics = False,
	eval_on_start = False,
	use_liger_kernel = False,
	liger_kernel_config = None,
	eval_use_gather_object = False,
	average_tokens_across_devices = True,
	model_init_kwargs = None,
	disable_dropout = False,
	max_prompt_length = 512,
	num_generations = 8,
	max_completion_length = 256,
	ds3_gather_for_generation = True,
	shuffle_dataset = True,
	generation_batch_size = None,
	steps_per_generation = None,
	temperature = 1.0,
	top_p = 1.0,
	top_k = None,
	min_p = None,
	generation_kwargs = {},
	repetition_penalty = 1.0,
	use_transformers_paged = False,
	cache_implementation = None,
	use_vllm = False,
	vllm_server_base_url = None,
	vllm_mode = 'colocate',
	vllm_model_impl = 'vllm',
	vllm_guided_decoding_regex = None,
	vllm_server_host = '0.0.0.0',
	vllm_server_port = 8000,
	vllm_server_timeout = 240.0,
	vllm_gpu_memory_utilization = 0.3,
	vllm_tensor_parallel_size = 1,
	beta = 0.05,
	num_iterations = 1,
	epsilon = 0.2,
	epsilon_high = None,
	reward_weights = None,
	normalize_advantages = False,
	reward_clip_range = None,
	mask_truncated_completions = False,
	sync_ref_model = False,
	ref_model_mixup_alpha = 0.6,
	ref_model_sync_steps = 512,
	log_completions = False,
	num_completions_to_print = None,
	wandb_log_unique_prompts = False,
	rloo_k = None,
	cliprange = None,
	kl_coef = None,
	exp_name = None,
	normalize_reward = None,
	num_ppo_epochs = None,
	num_mini_batches = None,
	total_episodes = None,
	response_length = None,
	token_level_kl = None,
	dataset_num_proc = None,
	local_rollout_forward_batch_size = None,
	num_sample_generations = None,
	stop_token = None,
	stop_token_id = None,
	missing_eos_penalty = None,
	vllm_sampling_params = None,
	unsloth_num_chunks = -1,

	**kwargs,
	):
	if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
	if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
	if output_dir is None and save_strategy == 'steps' and save_steps == 500:
	output_dir = 'unsloth_training_checkpoints'
	save_strategy = 'no'
	if dataset_num_proc is None:
	from multiprocessing import cpu_count
	dataset_num_proc = max(cpu_count()+4, 2)
	if (per_device_train_batch_size // num_generations) * num_generations != per_device_train_batch_size:
	print('Unsloth: We now expect `per_device_train_batch_size` to be a multiple of `num_generations`.\nWe will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations))
	per_device_train_batch_size = num_generations

	if temperature <= 0:
	raise MathError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')
	elif temperature >= 10:
	raise MathError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')


	super().__init__(
	output_dir = output_dir,
	overwrite_output_dir = overwrite_output_dir,
	do_train = do_train,
	do_eval = do_eval,
	do_predict = do_predict,
	eval_strategy = eval_strategy,
	prediction_loss_only = prediction_loss_only,
	per_device_train_batch_size = per_device_train_batch_size,
	per_device_eval_batch_size = per_device_eval_batch_size,
	per_gpu_train_batch_size = per_gpu_train_batch_size,
	per_gpu_eval_batch_size = per_gpu_eval_batch_size,
	gradient_accumulation_steps = gradient_accumulation_steps,
	eval_accumulation_steps = eval_accumulation_steps,
	eval_delay = eval_delay,
	torch_empty_cache_steps = torch_empty_cache_steps,
	learning_rate = learning_rate,
	weight_decay = weight_decay,
	adam_beta1 = adam_beta1,
	adam_beta2 = adam_beta2,
	adam_epsilon = adam_epsilon,
	max_grad_norm = max_grad_norm,
	num_train_epochs = num_train_epochs,
	max_steps = max_steps,
	lr_scheduler_type = lr_scheduler_type,
	warmup_ratio = warmup_ratio,
	warmup_steps = warmup_steps,
	log_level = log_level,
	log_level_replica = log_level_replica,
	log_on_each_node = log_on_each_node,
	logging_dir = logging_dir,
	logging_strategy = logging_strategy,
	logging_first_step = logging_first_step,
	logging_steps = logging_steps,
	logging_nan_inf_filter = logging_nan_inf_filter,
	save_strategy = save_strategy,
	save_steps = save_steps,
	save_total_limit = save_total_limit,
	save_safetensors = save_safetensors,
	save_on_each_node = save_on_each_node,
	save_only_model = save_only_model,
	restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
	no_cuda = no_cuda,
	use_cpu = use_cpu,
	use_mps_device = use_mps_device,
	seed = seed,
	data_seed = data_seed,
	jit_mode_eval = jit_mode_eval,
	use_ipex = use_ipex,
	bf16 = bf16,
	fp16 = fp16,
	fp16_opt_level = fp16_opt_level,
	half_precision_backend = half_precision_backend,
	bf16_full_eval = bf16_full_eval,
	fp16_full_eval = fp16_full_eval,
	tf32 = tf32,
	local_rank = local_rank,
	ddp_backend = ddp_backend,
	tpu_num_cores = tpu_num_cores,
	tpu_metrics_debug = tpu_metrics_debug,
	debug = debug,
	dataloader_drop_last = dataloader_drop_last,
	eval_steps = eval_steps,
	dataloader_num_workers = dataloader_num_workers,
	dataloader_prefetch_factor = dataloader_prefetch_factor,
	past_index = past_index,
	run_name = run_name,
	disable_tqdm = disable_tqdm,
	remove_unused_columns = remove_unused_columns,
	label_names = label_names,
	load_best_model_at_end = load_best_model_at_end,
	metric_for_best_model = metric_for_best_model,
	greater_is_better = greater_is_better,
	ignore_data_skip = ignore_data_skip,
	fsdp = fsdp,
	fsdp_min_num_params = fsdp_min_num_params,
	fsdp_config = fsdp_config,
	fsdp_transformer_layer_cls_to_wrap = fsdp_transformer_layer_cls_to_wrap,
	accelerator_config = accelerator_config,
	parallelism_config = parallelism_config,
	deepspeed = deepspeed,
	label_smoothing_factor = label_smoothing_factor,
	optim = optim,
	optim_args = optim_args,
	adafactor = adafactor,
	group_by_length = group_by_length,
	length_column_name = length_column_name,
	report_to = report_to,
	ddp_find_unused_parameters = ddp_find_unused_parameters,
	ddp_bucket_cap_mb = ddp_bucket_cap_mb,
	ddp_broadcast_buffers = ddp_broadcast_buffers,
	dataloader_pin_memory = dataloader_pin_memory,
	dataloader_persistent_workers = dataloader_persistent_workers,
	skip_memory_metrics = skip_memory_metrics,
	use_legacy_prediction_loop = use_legacy_prediction_loop,
	push_to_hub = push_to_hub,
	resume_from_checkpoint = resume_from_checkpoint,
	hub_model_id = hub_model_id,
	hub_strategy = hub_strategy,
	hub_token = hub_token,
	hub_private_repo = hub_private_repo,
	hub_always_push = hub_always_push,
	hub_revision = hub_revision,
	gradient_checkpointing = gradient_checkpointing,
	gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
	include_inputs_for_metrics = include_inputs_for_metrics,
	eval_do_concat_batches = eval_do_concat_batches,
	fp16_backend = fp16_backend,
	push_to_hub_model_id = push_to_hub_model_id,
	push_to_hub_organization = push_to_hub_organization,
	push_to_hub_token = push_to_hub_token,
	mp_parameters = mp_parameters,
	auto_find_batch_size = auto_find_batch_size,
	full_determinism = full_determinism,
	torchdynamo = torchdynamo,
	ray_scope = ray_scope,
	ddp_timeout = ddp_timeout,
	torch_compile = torch_compile,
	torch_compile_backend = torch_compile_backend,
	torch_compile_mode = torch_compile_mode,
	include_tokens_per_second = include_tokens_per_second,
	include_num_input_tokens_seen = include_num_input_tokens_seen,
	neftune_noise_alpha = neftune_noise_alpha,
	optim_target_modules = optim_target_modules,
	batch_eval_metrics = batch_eval_metrics,
	eval_on_start = eval_on_start,
	use_liger_kernel = use_liger_kernel,
	liger_kernel_config = liger_kernel_config,
	eval_use_gather_object = eval_use_gather_object,
	average_tokens_across_devices = average_tokens_across_devices,
	model_init_kwargs = model_init_kwargs,
	disable_dropout = disable_dropout,
	max_prompt_length = max_prompt_length,
	num_generations = num_generations,
	max_completion_length = max_completion_length,
	ds3_gather_for_generation = ds3_gather_for_generation,
	shuffle_dataset = shuffle_dataset,
	generation_batch_size = generation_batch_size,
	steps_per_generation = steps_per_generation,
	temperature = temperature,
	top_p = top_p,
	top_k = top_k,
	min_p = min_p,
	generation_kwargs = generation_kwargs,
	repetition_penalty = repetition_penalty,
	use_transformers_paged = use_transformers_paged,
	cache_implementation = cache_implementation,
	use_vllm = use_vllm,
	vllm_server_base_url = vllm_server_base_url,
	vllm_mode = vllm_mode,
	vllm_model_impl = vllm_model_impl,
	vllm_guided_decoding_regex = vllm_guided_decoding_regex,
	vllm_server_host = vllm_server_host,
	vllm_server_port = vllm_server_port,
	vllm_server_timeout = vllm_server_timeout,
	vllm_gpu_memory_utilization = vllm_gpu_memory_utilization,
	vllm_tensor_parallel_size = vllm_tensor_parallel_size,
	beta = beta,
	num_iterations = num_iterations,
	epsilon = epsilon,
	epsilon_high = epsilon_high,
	reward_weights = reward_weights,
	normalize_advantages = normalize_advantages,
	reward_clip_range = reward_clip_range,
	mask_truncated_completions = mask_truncated_completions,
	sync_ref_model = sync_ref_model,
	ref_model_mixup_alpha = ref_model_mixup_alpha,
	ref_model_sync_steps = ref_model_sync_steps,
	log_completions = log_completions,
	num_completions_to_print = num_completions_to_print,
	wandb_log_unique_prompts = wandb_log_unique_prompts,
	rloo_k = rloo_k,
	cliprange = cliprange,
	kl_coef = kl_coef,
	exp_name = exp_name,
	normalize_reward = normalize_reward,
	num_ppo_epochs = num_ppo_epochs,
	num_mini_batches = num_mini_batches,
	total_episodes = total_episodes,
	response_length = response_length,
	token_level_kl = token_level_kl,
	dataset_num_proc = dataset_num_proc,
	local_rollout_forward_batch_size = local_rollout_forward_batch_size,
	num_sample_generations = num_sample_generations,
	stop_token = stop_token,
	stop_token_id = stop_token_id,
	missing_eos_penalty = missing_eos_penalty,**kwargs)
	self.vllm_sampling_params = vllm_sampling_params
	self.unsloth_num_chunks = unsloth_num_chunks

	pass

	class _UnslothRLOOTrainer(Trainer):
	""""""

	_tag_names = ["trl", "rloo"]

	def __init__(
	self,
	# Note for dev: we can remove the default None when we remove the deprecated model parameter in version 0.25.0
	model: Union[str, PreTrainedModel] = None,
	reward_funcs: Union[RewardFunc, list[RewardFunc]] = None,
	args: Optional[RLOOConfig] = None,
	train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
	eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None,
	processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None,
	reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None,
	callbacks: Optional[list[TrainerCallback]] = None,
	optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None),
	peft_config: Optional["PeftConfig"] = None,
	# Deprecated parameters
	config=None,
	reward_model=None,
	policy=None,
	ref_policy=None,
	data_collator=None,
	):

	if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'):
	if (getattr(args, 'use_vllm', False) == False):
	args.use_vllm = True
	# Handle deprecated parameters
	if config is not None:
	warnings.warn(
	"Parameter 'config' is deprecated and will be removed in version 0.25.0. Please use 'args' instead. "
	"We are setting args=config"
	)
	if args is None:
	args = config
	else:
	raise ValueError("Cannot specify both 'config' (deprecated) and 'args'. Please use 'args' only.")

	if reward_model is not None:
	warnings.warn(
	"Parameter 'reward_model' is deprecated and will be removed in version 0.25.0. Please use "
	"'reward_funcs' instead. We are setting reward_funcs=reward_model"
	)
	if reward_funcs is None:
	reward_funcs = reward_model
	else:
	raise ValueError(
	"Cannot specify both 'reward_model' (deprecated) and 'reward_funcs'. Please use 'reward_funcs' "
	"only."
	)
	if policy is not None:
	warnings.warn(
	"Parameter 'policy' is deprecated and will be removed in version 0.25.0. Please use 'model' instead. "
	"We are setting model=policy"
	)
	if model is None:
	model = policy
	else:
	raise ValueError("Cannot specify both 'policy' (deprecated) and 'model'. Please use 'model' only.")
	if ref_policy is not None:
	warnings.warn(
	"Parameter 'ref_policy' is deprecated and will be removed in version 0.25.0. To use the initial model "
	"as the reference model, simply omit this parameter. The parameter is ignored."
	)
	if data_collator is not None:
	warnings.warn(
	"Parameter 'data_collator' is deprecated and will be removed in version 0.25.0. The RLOOTrainer does "
	"not use a data collator, so this parameter is ignored."
	)
	if "input_ids" in train_dataset.column_names:
	warnings.warn(
	"The training dataset contains a column named 'input_ids', indicating that it is pre-tokenized. "
	"Support for pre-tokenized datasets is deprecated and will be removed in version 0.25. Please provide "
	"the raw dataset (conversational or standard) with a 'prompt' column instead."
	)

	def decode(example, tokenizer):
	return {"prompt": tokenizer.decode(example["input_ids"])}

	train_dataset = train_dataset.map(decode, fn_kwargs={"tokenizer": processing_class})
	if eval_dataset is not None and "input_ids" in eval_dataset.column_names:
	warnings.warn(
	"The evaluation dataset contains a column named 'input_ids', indicating that it is pre-tokenized. "
	"Support for pre-tokenized datasets is deprecated and will be removed in version 0.25. Please provide "
	"the raw dataset (conversational or standard) with a 'prompt' column instead."
	)

	def decode(example, tokenizer):
	return {"prompt": tokenizer.decode(example["input_ids"])}

	eval_dataset = eval_dataset.map(decode, fn_kwargs={"tokenizer": processing_class})

	# Args
	if args is None:
	model_name = model if isinstance(model, str) else model.config._name_or_path
	model_name = model_name.split("/")[-1]
	args = RLOOConfig(f"{model_name}-RLOO")

	# Models
	# Trained model
	model_init_kwargs = args.model_init_kwargs or {}
	if isinstance(model, str):
	model_id = model
	torch_dtype = model_init_kwargs.get("torch_dtype")
	if isinstance(torch_dtype, torch.dtype) or torch_dtype == "auto" or torch_dtype is None:
	pass # torch_dtype is already a torch.dtype or "auto" or None
	elif isinstance(torch_dtype, str): # it's a str, but not "auto"
	torch_dtype = getattr(torch, torch_dtype)
	model_init_kwargs["torch_dtype"] = torch_dtype
	else:
	raise ValueError(
	"Invalid `torch_dtype` passed to `RLOOConfig`. Expected either 'auto' or a string representing "
	f"a `torch.dtype` (e.g., 'float32'), but got {torch_dtype}."
	)
	# Disable caching if gradient checkpointing is enabled [not supported]
	config = AutoConfig.from_pretrained(model_id)
	architecture = getattr(transformers, config.architectures[0])
	model = architecture.from_pretrained(model_id, **model_init_kwargs)
	else:
	model_id = model.config._name_or_path
	if args.model_init_kwargs is not None:
	logger.warning(
	"You passed `model_init_kwargs` to the `RLOOConfig`, but your model is already instantiated. "
	"The `model_init_kwargs` will be ignored."
	)

	# Some models [SmolVLM/Idefics3] don't support `logits_to_keep` argument and error out if we pass it
	# Inspect the forward method before we wrap the model with PEFT
	self.model_kwarg_keys = (
	inspect.signature(model.forward).parameters.keys()
	if not hasattr(model, "get_base_model")
	else inspect.signature(model.get_base_model().forward).parameters.keys()
	)

	if False:
	model = prepare_peft_model(model, peft_config, args)

	# Processing class
	if processing_class is None:
	processing_class = AutoProcessor.from_pretrained(model.config._name_or_path)

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

	if tokenizer.pad_token is None:
	tokenizer.pad_token = tokenizer.eos_token

	self.pad_token = tokenizer.pad_token
	self.pad_token_id = tokenizer.pad_token_id
	self.eos_token_id = tokenizer.eos_token_id

	# Reward functions
	if not isinstance(reward_funcs, list):
	reward_funcs = [reward_funcs]
	self.reward_func_names = []
	for i, reward_func in enumerate(reward_funcs):
	if isinstance(reward_func, str):
	reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained(
	reward_func, num_labels=1, **model_init_kwargs
	)
	if isinstance(reward_funcs[i], nn.Module): # Use Module over PretrainedModel for compat w/ compiled models
	self.reward_func_names.append(reward_funcs[i].config._name_or_path.split("/")[-1])
	else:
	self.reward_func_names.append(reward_funcs[i].__name__)
	self.reward_funcs = reward_funcs

	# Reward weights
	if args.reward_weights is not None:
	if len(args.reward_weights) != len(reward_funcs):
	raise ValueError(
	f"Number of reward weights ({len(args.reward_weights)}) must match number of reward "
	f"functions ({len(reward_funcs)})"
	)
	self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32)
	else:
	self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32)

	# Reward processing class
	if reward_processing_classes is None:
	reward_processing_classes = [None] * len(reward_funcs)
	elif not isinstance(reward_processing_classes, list):
	reward_processing_classes = [reward_processing_classes]
	if len(reward_processing_classes) != len(reward_funcs):
	raise ValueError(
	f"The number of reward processing classes ({len(reward_processing_classes)}) must match the number of "
	f"reward functions ({len(reward_funcs)})."
	)

	for i, (reward_processing_class, reward_func) in enumerate(zip(reward_processing_classes, reward_funcs)):
	if isinstance(reward_func, PreTrainedModel):
	if reward_processing_class is None:
	reward_processing_class = AutoTokenizer.from_pretrained(reward_func.config._name_or_path)
	if reward_processing_class.pad_token_id is None:
	reward_processing_class.pad_token = reward_processing_class.eos_token
	# The reward model computes the reward for the latest non-padded token in the input sequence.
	# So it's important to set the pad token ID to the padding token ID of the processing class.
	reward_func.config.pad_token_id = reward_processing_class.pad_token_id
	reward_processing_classes[i] = reward_processing_class

	self.reward_processing_classes = reward_processing_classes

	# Training arguments
	self.max_prompt_length = args.max_prompt_length
	self.max_completion_length = args.max_completion_length
	self.num_generations = args.num_generations
	self.temperature = args.temperature
	self.top_p = args.top_p
	self.top_k = args.top_k
	self.min_p = args.min_p
	self.repetition_penalty = args.repetition_penalty
	self.use_transformers_paged = args.use_transformers_paged
	self.use_vllm = args.use_vllm
	self.vllm_mode = args.vllm_mode
	self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization # only applies to colocation mode
	self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size # only applies to colocation mode
	self.normalize_advantages = args.normalize_advantages
	self.mask_truncated_completions = args.mask_truncated_completions
	self.reward_clip_range = args.reward_clip_range

	# Datasets
	self.shuffle_dataset = args.shuffle_dataset

	if (
	isinstance(train_dataset, IterableDataset)
	or isinstance(eval_dataset, IterableDataset)
	or (
	isinstance(eval_dataset, dict) and any(isinstance(ds, IterableDataset) for ds in eval_dataset.values())
	)
	):
	# See https://github.com/huggingface/trl/issues/3213
	raise NotImplementedError(
	"Iterable datasets are not yet supported in RLOOTrainer. Please use a standard dataset instead."
	)

	# Multi-step
	self.num_iterations = args.num_iterations
	self.epsilon_low = args.epsilon
	self.epsilon_high = args.epsilon_high if args.epsilon_high is not None else args.epsilon
	# Tracks the number of iterations [forward + backward passes], including those within a grad accum cycle
	self._step = 0
	# Buffer the batch to reuse generated outputs across multiple updates. For more details, see
	# `_get_train_sampler` and `_prepare_inputs`.
	self._buffered_inputs = None

	# The trainer estimates the number of FLOPs [floating-point operations] using the number of elements in the
	# input tensor associated with the key "input_ids". However, in RLOO, the sampled data does not include the
	# "input_ids" key. Instead, the available keys is "prompt". As a result, the trainer issues the warning:
	# "Could not estimate the number of tokens of the input, floating-point operations will not be computed." To
	# suppress this warning, we set the "estimate_tokens" key in the model's "warnings_issued" dictionary to True.
	# This acts as a flag to indicate that the warning has already been issued.
	model.warnings_issued["estimate_tokens"] = True

	super().__init__(
	model=model,
	args=args,
	data_collator=identity, # No data collation is needed in RLOO
	train_dataset=train_dataset,
	eval_dataset=eval_dataset,
	processing_class=processing_class,
	callbacks=callbacks,
	optimizers=optimizers,
	)

	# Reference model
	self.beta = args.beta
	if self.beta == 0.0:
	# If beta is 0.0, the reference model is not needed
	self.ref_model = None
	elif is_peft_model(model):
	# If PEFT is used, the reference model is not needed since the adapter can be disabled
	# to revert to the initial model.
	self.ref_model = None
	else:
	# For deepspeed, fsdp or non-distributed models, create a reference model from scratch
	config = AutoConfig.from_pretrained(model_id)
	architecture = getattr(transformers, config.architectures[0])
	self.ref_model = architecture.from_pretrained(model_id, **model_init_kwargs)

	# Disable dropout in the models
	if args.disable_dropout:
	disable_dropout_in_model(model)
	if self.ref_model is not None:
	disable_dropout_in_model(self.ref_model)

	# Initialize the metrics
	self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
	self._total_train_tokens = 0
	self.log_completions = args.log_completions
	self.wandb_log_unique_prompts = args.wandb_log_unique_prompts
	self.num_completions_to_print = args.num_completions_to_print
	# Keep logs sized to the generation batch to record only outputs from the latest model update.
	self._logs = {
	"prompt": deque(maxlen=args.generation_batch_size),
	"completion": deque(maxlen=args.generation_batch_size),
	"rewards": defaultdict(lambda: deque(maxlen=args.generation_batch_size)),
	"advantages": deque(maxlen=args.generation_batch_size),
	}

	# Ensure each process receives a unique seed to prevent duplicate completions when generating with
	# transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but
	# it's safer to set it in all cases.
	set_seed(args.seed, device_specific=True)

	if self.use_vllm:
	if not is_vllm_available():
	raise ImportError(
	"vLLM is not available and `use_vllm` is set to True. Please install vLLM with "
	"`pip install vllm` to use it."
	)

	if self.vllm_mode == "server":
	if self.accelerator.is_main_process:
	if args.vllm_server_base_url is not None:
	base_url = args.vllm_server_base_url
	else:
	base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}"
	self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout)
	self.vllm_client.init_communicator(device=torch.cuda.current_device())

	elif self.vllm_mode == "colocate":
	if not self.accelerator.num_processes % self.vllm_tensor_parallel_size == 0:
	raise ValueError(
	f"vllm_tensor_parallel_size ({self.vllm_tensor_parallel_size}) must divide world size "
	f"({self.accelerator.num_processes}) evenly."
	)

	if self.vllm_tensor_parallel_size > 1:
	self.tp_group, _ = torch.distributed.new_subgroups_by_enumeration(
	[
	list(range(i * self.vllm_tensor_parallel_size, (i + 1) * self.vllm_tensor_parallel_size))
	for i in range(self.accelerator.num_processes // self.vllm_tensor_parallel_size)
	]
	)
	os.environ["RANK"] = str(self.accelerator.process_index)
	os.environ["LOCAL_RANK"] = str(self.accelerator.local_process_index)
	os.environ["WORLD_SIZE"] = str(self.accelerator.num_processes)
	os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "localhost")
	os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "12345")

	if self.max_prompt_length is not None and self.max_completion_length is not None:
	max_model_len = self.max_prompt_length + self.max_completion_length
	else:
	max_model_len = None
	self.llm = model.vllm_engine
	else:
	raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.")
	self.guided_decoding_regex = args.vllm_guided_decoding_regex

	self._last_loaded_step = -1
	self.accelerator.wait_for_everyone()
	else:
	generation_kwargs = {
	"max_new_tokens": self.max_completion_length,
	"do_sample": True,
	"pad_token_id": tokenizer.pad_token_id,
	"bos_token_id": tokenizer.bos_token_id,
	"eos_token_id": tokenizer.eos_token_id,
	"temperature": self.temperature,
	"top_p": self.top_p,
	"top_k": self.top_k,
	"min_p": self.min_p,
	"repetition_penalty": self.repetition_penalty,
	"cache_implementation": args.cache_implementation,
	}
	if args.use_transformers_paged:
	generation_kwargs["max_batch_tokens"] = 512
	generation_kwargs["num_blocks"] = 1024
	generation_kwargs["block_size"] = 128
	if args.generation_kwargs is not None:
	generation_kwargs.update(args.generation_kwargs)
	self.generation_config = GenerationConfig(**generation_kwargs)

	# Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
	# model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
	# self.model_accepts_loss_kwargs to False to enable scaling.
	self.model_accepts_loss_kwargs = False

	# Add tags to the model
	self.model.add_model_tags(self._tag_names)

	if self.ref_model is not None:
	if self.is_deepspeed_enabled:
	self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
	elif self.is_fsdp_enabled:
	self.ref_model = prepare_fsdp(self.ref_model, self.accelerator)
	else:
	self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

	if args.sync_ref_model:
	self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator))

	for i, reward_func in enumerate(self.reward_funcs):
	if isinstance(reward_func, PreTrainedModel):
	if self.is_deepspeed_enabled:
	self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator)
	else:
	# set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp
	self.reward_funcs[i] = self.accelerator.prepare_model(
	reward_func, evaluation_mode=True, device_placement=True
	)

	def _set_signature_columns_if_needed(self):
	# If `self.args.remove_unused_columns` is True, non-signature columns are removed.
	# By default, this method sets `self._signature_columns` to the model's expected inputs.
	# In RLOOTrainer, we preprocess data, so using the model's signature columns doesn't work.
	# Instead, we set them to the columns expected by the `training_step` method, hence the override.
	if self._signature_columns is None:
	self._signature_columns = ["prompt"]

	# This method overrides `Trainer.get_train_dataloader` to support our custom batching strategy.
	# Instead of returning a standard per-step batch (i.e., `per_device_batch_size), our dataloader loads an
	# generation batch (i.e., `per_device_batch_size × steps_per_generation`). This allows us to generate completions
	# once every steps_per_generation step—rather than once per accumulation step—which is significantly more
	# efficient. The only change from the original implementation is multiplying the batch size by
	# `steps_per_generation`. Thus, `_prepare_inputs` is called with this generation batch, and it handles the
	# splitting internally.
	# Maintenance note: This method is a copy-paste of the original `Trainer.get_train_dataloader` with only one line
	# modification. As a result, some parts of the method aren't relevant to RLOO, but we keep them to stay one line
	# apart from the super method, ensuring easier maintenance in the future.
	def get_train_dataloader(self):
	if self.train_dataset is None:
	raise ValueError("Trainer: training requires a train_dataset.")

	train_dataset = self.train_dataset
	data_collator = self.data_collator
	if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
	train_dataset = self._remove_unused_columns(train_dataset, description="training")
	else:
	data_collator = self._get_collator_with_removed_columns(data_collator, description="training")

	dataloader_params = {
	"batch_size": self._train_batch_size * self.args.steps_per_generation, # < this is the change
	"collate_fn": data_collator,
	"num_workers": self.args.dataloader_num_workers,
	"pin_memory": self.args.dataloader_pin_memory,
	"persistent_workers": self.args.dataloader_persistent_workers,
	}

	if not isinstance(train_dataset, torch.utils.data.IterableDataset):
	dataloader_params["sampler"] = self._get_train_sampler()
	dataloader_params["drop_last"] = self.args.dataloader_drop_last
	dataloader_params["worker_init_fn"] = partial(
	seed_worker, num_workers=self.args.dataloader_num_workers, rank=self.args.process_index
	)

	dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor

	return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))

	def _get_train_sampler(self, dataset: Optional[Dataset] = None) -> Sampler:
	# Returns a sampler that
	# 1. ensures each prompt is repeated across multiple processes. This guarantees that identical prompts are
	# distributed to different GPUs, allowing rewards to be computed and normalized correctly within each prompt
	# group. Using the same seed across processes ensures consistent prompt assignment, preventing discrepancies
	# in group formation.
	# 2. repeats the batch multiple times to allow reusing generations across multiple updates. Refer to
	# _prepare_inputs to see how the generations are stored and reused.

	# In the following figure, the values are the prompt indices. The first row shows the first sampled batch, the
	# second row shows the second sampled batch, and so on.
	#
	# \| GPU 0 \| GPU 1 \|
	#
	# global_step step <-───> num_generations=2
	# <-───────> per_device_train_batch_size=3
	# grad_accum ▲ ▲ 0 0 0 0 1 1 2 2 <- Generate for the first `steps_per_generation` (prompts 0 to 11); store the completions; use the first slice to compute the loss
	# =2 ▼ \| 0 1 3 3 4 4 5 5 <- Take the stored generations and use the second slice to compute the loss
	# \|
	# \| 1 2 6 6 7 7 8 8 <- Take the stored generations and use the third slice to compute the loss
	# steps_per_gen=4 ▼ 1 3 9 9 10 10 11 11 <- Take the stored generations and use the fourth slice to compute the loss
	#
	# 2 4 12 12 13 13 14 14 <- Generate for the second `steps_per_generation` (prompts 12 to 23); store the completions; use the first slice to compute the loss
	# 2 5 15 15 16 16 17 17 <- Take the stored generations and use the second slice to compute the loss
	# ...
	if dataset is None:
	dataset = self.train_dataset
	return RepeatSampler(
	data_source=dataset,
	mini_repeat_count=self.num_generations,
	batch_size=self.args.generation_batch_size // self.num_generations,
	repeat_count=self.num_iterations * self.args.steps_per_generation,
	shuffle=self.shuffle_dataset,
	seed=self.args.seed,
	)

	def _get_eval_sampler(self, eval_dataset) -> Sampler:
	# See _get_train_sampler for an explanation of the sampler.
	return RepeatSampler(
	data_source=eval_dataset,
	mini_repeat_count=self.num_generations,
	seed=self.args.seed,
	)

	@profiling_decorator
	def _get_per_token_logps_and_entropies(
	self,
	model,
	input_ids,
	attention_mask,
	logits_to_keep,
	batch_size=None,
	compute_entropy=False,
	) -> dict[str, Optional[torch.Tensor]]:
	"""Compute log-probs and (optionally) entropies for each token."""
	batch_size = batch_size or input_ids.size(0) # Chunk inputs into smaller batches to reduce memory peak
	all_logps = []
	all_entropies = []
	for start in range(0, input_ids.size(0), batch_size):
	input_ids_batch = input_ids[start : start + batch_size]
	attention_mask_batch = attention_mask[start : start + batch_size]

	# Build model inputs - check if the model supports logits_to_keep (some models and VLMs don't)
	model_inputs = {"input_ids": input_ids_batch, "attention_mask": attention_mask_batch}

	# Only add logits_to_keep if the model supports it
	if "logits_to_keep" in self.model_kwarg_keys:
	# We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
	model_inputs["logits_to_keep"] = logits_to_keep + 1

	model_inputs["use_cache"] = False # only used in generation; set False to suppress warnings

	logits = model(**model_inputs).logits
	# Exclude the last value: it corresponds to the next token pred
	logits = logits[:, :-1, :] # (B, L-1, H)
	# Only keep the last logits_to_keep. For model that support logits_to_keep, this is a no-op.
	logits = logits[:, -logits_to_keep:, :] # (B, logits_to_keep, H)
	# Divide logits by sampling temperature.
	# See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details
	logits = logits / self.temperature

	completion_ids = input_ids_batch[:, -logits_to_keep:]
	logps = selective_log_softmax(logits, completion_ids) # compute logprobs
	all_logps.append(logps)

	if compute_entropy:
	with torch.no_grad():
	entropies = entropy_from_logits(logits)
	all_entropies.append(entropies)

	logps = torch.cat(all_logps, dim=0)
	entropies = torch.cat(all_entropies, dim=0) if compute_entropy else None
	return logps, entropies

	def _fix_param_name_to_vllm(self, name, extra_prefixes: Optional[list[str]] = None):
	extra_prefixes = extra_prefixes or []
	prefixes = ["_checkpoint_wrapped_module."] + extra_prefixes
	for prefix in prefixes:
	name = name.replace(prefix, "")
	return name

	def _sync_fsdp1_params_to_vllm(self, module: nn.Module, prefix: str = "", visited=None):
	"""Memory-efficient post-order traversal of FSDP modules to extract full parameters and sync with vLLM."""
	# For FSDP1, we need to recurse into children and also use summon_full_params
	if visited is None:
	visited = set()
	for child_name, child_module in module.named_children():
	child_prefix = f"{prefix}.{child_name}" if prefix else child_name
	self._sync_fsdp1_params_to_vllm(
	child_module, prefix=child_prefix, visited=visited
	) # recurse into the child

	if isinstance(module, FSDP):
	with FSDP.summon_full_params(module, recurse=False, writeback=False):
	for param_name, param in module.named_parameters():
	full_name = f"{prefix}.{param_name}" if prefix else param_name
	full_name = self._fix_param_name_to_vllm(full_name, extra_prefixes=["_fsdp_wrapped_module."])

	if full_name in visited:
	continue # skip FSDP subtrees already traversed
	visited.add(full_name)

	if self.vllm_mode == "server" and self.accelerator.is_main_process:
	self.vllm_client.update_named_param(full_name, param.data)
	elif self.vllm_mode == "colocate":

	pass

	pass

	def _sync_fsdp2_params_to_vllm(self, module: nn.Module):
	# For FSDP2, module already covers all parameters, so no need for recursion
	for name, param in module.items():
	if param.is_cpu:
	param = param.to(torch.device("cuda"))
	param = param.full_tensor()

	if self.vllm_mode == "server" and self.accelerator.is_main_process:
	self.vllm_client.update_named_param(name, param)
	elif self.vllm_mode == "colocate":

	pass

	pass

	@profiling_decorator
	def _move_model_to_vllm(self):
	# For DeepSpeed ZeRO-3 and FSDP, we need to gather all parameters before operations
	deepspeed_plugin = self.accelerator.state.deepspeed_plugin
	zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
	if zero_stage_3:
	import deepspeed

	gather_if_zero3 = deepspeed.zero.GatheredParameters
	else:
	gather_if_zero3 = nullcontext

	if is_peft_model(self.model):
	# With PEFT and FSDP/DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as
	# merging adapters in a sharded manner is not supported.
	# TODO: does this work with FSDP?
	with gather_if_zero3(list(self.model.parameters())):
	self.model.merge_adapter()

	# Update vLLM weights while parameters are gathered
	if self.is_fsdp_enabled: # note if using FSDP, gather_if_zero3 is nullcontext
	# Update vLLM weights while parameters are gathered
	# For PEFT with FSDP we need to use the memory efficient post-order traversal
	fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None)
	fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1
	if fsdp_version == 1:
	self._sync_fsdp1_params_to_vllm(
	self.model
	) # use memory-efficient post-order traversal for FSDP
	elif fsdp_version == 2:
	self._sync_fsdp2_params_to_vllm(self.model)
	else:
	# DeepSpeed ZeRO-3 with PEFT
	for name, param in self.model.named_parameters():
	# When using PEFT, we need to recover the original parameter name and discard some parameters
	name = name.removeprefix("base_model.model.").replace(".base_layer", "")
	if self.model.prefix in name:
	continue
	# When module to save, remove its prefix and discard the original module
	if "original_module" in name:
	continue
	name = self._fix_param_name_to_vllm(name, extra_prefixes=["modules_to_save.default."])

	if self.vllm_mode == "server" and self.accelerator.is_main_process:
	self.vllm_client.update_named_param(name, param.data)
	elif self.vllm_mode == "colocate":

	pass

	pass
	# Unmerge adapters while parameters are still gathered
	self.model.unmerge_adapter()
	# Parameters will automatically be repartitioned when exiting the context
	else:
	# For non-PEFT models, simply gather (if needed) and update each parameter individually.
	if self.is_fsdp_enabled:
	fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None)
	fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1
	if fsdp_version == 1:
	self._sync_fsdp1_params_to_vllm(self.model) # use memory-efficient post-order traversal for FSDP
	elif fsdp_version == 2:
	self._sync_fsdp2_params_to_vllm(self.model)
	else:
	for name, param in self.model.named_parameters():
	name = self._fix_param_name_to_vllm(name)
	with gather_if_zero3([param]):
	if self.vllm_mode == "server" and self.accelerator.is_main_process:
	self.vllm_client.update_named_param(name, param.data)
	elif self.vllm_mode == "colocate":

	pass

	pass

	# Reset cache on vLLM
	if self.vllm_mode == "server" and self.accelerator.is_main_process:
	self.vllm_client.reset_prefix_cache()
	elif self.vllm_mode == "colocate":
	self.llm.reset_prefix_cache()

	@profiling_decorator
	def _prepare_inputs(
	self, generation_batch: dict[str, Union[torch.Tensor, Any]]
	) -> dict[str, Union[torch.Tensor, Any]]:
	# Prepares inputs for model training/evaluation by managing completion generation and batch handling.
	# During training:
	# - Receives the local generation batch (Per-GPU batch size × steps per generation)
	# from the modified training dataloader instead of the standard local batch
	# - Generates completions once for the entire generation batch and splits it into batches of size
	# `per_device_train_batch_size`
	# - Buffers these completions and returns the appropriate slice for the current accumulation step
	# - Optimizes by regenerating completions only periodically (every steps_per_generation * num_iterations)
	# During evaluation:
	# - The input is treated as a standard local batch (no accumulation, no multiple iterations)
	# - Completions are generated for each batch without buffering or reuse
	# Returns a single local batch in both cases.

	mode = "train" if self.model.training else "eval"
	if mode == "train":
	generate_every = self.args.steps_per_generation * self.num_iterations
	if self._step % generate_every == 0 or self._buffered_inputs is None:
	# self._buffered_inputs=None can occur when resuming from a checkpoint
	generation_batch = self._generate_and_score_completions(generation_batch)
	generation_batch = split_pixel_values_by_grid(generation_batch)

	try: generation_batch = shuffle_sequence_dict(generation_batch)

	except: pass
	generation_batches = split_tensor_dict(generation_batch, self.args.steps_per_generation)
	self._buffered_inputs = [unsplit_pixel_values_by_grid(batch) for batch in generation_batches]
	inputs = self._buffered_inputs[self._step % self.args.steps_per_generation]
	self._step += 1
	else:
	# In evaluation, there is neither batch grouping for generation, nor multiple iterations, hence
	# local generation batch == local eval batch
	inputs = self._generate_and_score_completions(generation_batch)
	return inputs

	@profiling_decorator
	def _calculate_rewards(self, inputs, prompts, completions, completion_ids_list):
	device = self.accelerator.device
	rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)

	# Repeat all input columns (but "prompt", "completion", and "completion_ids") to match the num of generations
	keys = [key for key in inputs[0] if key not in ["prompt", "completion", "completion_ids"]]
	reward_kwargs = {key: [example[key] for example in inputs] for key in keys}

	# This allows for dynamic reward shaping based on training progress.
	reward_kwargs["trainer_state"] = self.state

	for i, (reward_func, reward_processing_class, reward_func_name) in enumerate(
	zip(self.reward_funcs, self.reward_processing_classes, self.reward_func_names)
	):
	with profiling_context(self, reward_func_name):
	if isinstance(reward_func, nn.Module): # Module (no PretrainedModel) for compat with compiled models
	if is_conversational(inputs[0]):
	messages = [{"messages": p + c} for p, c in zip(prompts, completions)]
	texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages]
	else:
	texts = [p + c for p, c in zip(prompts, completions)]
	reward_inputs = reward_processing_class(
	text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False
	)
	reward_inputs = super()._prepare_inputs(reward_inputs)
	with torch.inference_mode():
	rewards_per_func[:, i] = reward_func(*reward_inputs).logits[:, 0] # Shape (BG,)
	else:
	output_reward_func = reward_func(
	prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs
	)
	# Convert None values to NaN
	output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func]

	rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)

	# If all reward functions return None for a given row, issue a detailed warning
	if torch.isnan(rewards_per_func).all(dim=1).any():
	nan_row_idx = torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0]
	row_reward_kwargs = {
	key: value[nan_row_idx] for key, value in reward_kwargs.items() if key != "trainer_state"
	}
	row_reward_kwargs["prompt"] = prompts[nan_row_idx]
	row_reward_kwargs["completion"] = completions[nan_row_idx]
	logger.warning(
	f"All reward functions returned None for the following kwargs:\n{row_reward_kwargs}\n"
	"Please ensure that at least one reward function returns a valid reward."
	)

	# Gather the reward per function: this part is crucial, because the rewards are normalized per group and the
	# completions may be distributed across processes
	rewards_per_func = gather(rewards_per_func)
	return rewards_per_func

	def _generate_and_score_completions(
	self, inputs: list[dict[str, Union[torch.Tensor, Any]]]
	) -> dict[str, Union[torch.Tensor, Any]]:
	device = self.accelerator.device
	mode = "train" if self.model.training else "eval"

	prompts = [x["prompt"] for x in inputs]

	# We don't yet support visual reward models/function, so we keep a copy of the original text-only prompts for
	# later use in the reward computation. If images are present, we insert {"type": "image"} as required by the
	# VLM chat template.
	original_prompts = copy.deepcopy(prompts)

	prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]

	prompt_inputs = self.processing_class(
	text=prompts_text,
	return_tensors="pt",
	padding=True,
	padding_side="left",
	add_special_tokens=False,
	)
	prompt_inputs = super()._prepare_inputs(prompt_inputs)
	prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"]

	if self.max_prompt_length is not None:
	# If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens.
	# Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text,
	# because we can't use `skip_special_tokens=True` (some special tokens are still needed for generation).
	prompt_ids, prompt_mask = truncate_with_protected_tokens(
	prompt_ids, prompt_mask, self.max_prompt_length, protected_tokens=[]
	)

	prompts_text = self.processing_class.batch_decode(
	prompt_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
	)
	prompts_text = [re.sub(rf"^({re.escape(self.pad_token)})+", "", text) for text in prompts_text]

	# Generate completions using either vLLM or regular generation
	if self.use_vllm:
	# First, update the vLLM weights if needed
	if self.state.global_step != self._last_loaded_step:
	self._move_model_to_vllm()
	self._last_loaded_step = self.state.global_step

	# Generate completions using vLLM: gather all prompts and use them in a single call in the main process
	if self.vllm_mode == "server":
	all_prompts_text = gather_object(prompts_text)

	if self.accelerator.is_main_process:
	# Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate
	# num_generations outputs for each one. This is faster than generating outputs for each duplicate
	# prompt individually.
	ordered_set_of_prompts = all_prompts_text[:: self.num_generations]

	with profiling_context(self, "vLLM.generate"):
	completion_ids = self.vllm_client.generate(
	prompts=ordered_set_of_prompts,
	n=self.num_generations,
	repetition_penalty=self.repetition_penalty,
	temperature=self.temperature,
	top_p=self.top_p,
	top_k=-1 if self.top_k is None else self.top_k,
	min_p=0.0 if self.min_p is None else self.min_p,
	max_tokens=self.max_completion_length,
	guided_decoding_regex=self.guided_decoding_regex,
	generation_kwargs=self.args.generation_kwargs,
	)
	else:
	completion_ids = [None] * len(all_prompts_text)
	# Broadcast the completions from the main process to all processes, ensuring each process receives its
	# corresponding slice.
	completion_ids = broadcast_object_list(completion_ids, from_process=0)
	process_slice = slice(
	self.accelerator.process_index * len(prompts),
	(self.accelerator.process_index + 1) * len(prompts),
	)
	completion_ids = completion_ids[process_slice]

	# Generate completions using colocated vLLM instances: each device holds vLLM copy and work on their own batch of prompts
	elif self.vllm_mode == "colocate":
	if self.guided_decoding_regex:
	guided_decoding = GuidedDecodingParams(regex=self.guided_decoding_regex)
	else:
	guided_decoding = None

	generation_kwargs = {
	"n": 1, # vLLM on each GPU generates only 1 in colocate mode
	"repetition_penalty": self.repetition_penalty,
	"temperature": self.temperature,
	"top_p": self.top_p,
	"top_k": -1 if self.top_k is None else self.top_k,
	"min_p": 0.0 if self.min_p is None else self.min_p,
	"max_tokens": self.max_completion_length,
	"guided_decoding": guided_decoding,
	}
	if self.args.generation_kwargs is not None:
	generation_kwargs.update(self.args.generation_kwargs)
	sampling_params = SamplingParams(**generation_kwargs)

	if self.vllm_tensor_parallel_size > 1:
	# Gather prompts from all ranks in the TP group and flatten.
	# Each rank starts with its own prompts; after gathering, all ranks see the full group set.
	orig_size = len(prompts_text)
	gathered_prompts = [None for _ in range(self.vllm_tensor_parallel_size)]
	torch.distributed.all_gather_object(gathered_prompts, prompts_text, group=self.tp_group)
	all_prompts_text = [p for sublist in gathered_prompts for p in sublist]

	else:
	all_prompts_text = prompts_text

	vllm_inputs = all_prompts_text

	with profiling_context(self, "vLLM.generate"):
	all_outputs = self.llm.generate(vllm_inputs, sampling_params=sampling_params, use_tqdm=False, lora_request = self.model.load_lora('rloo_trainer_lora_model', load_tensors = True))

	completion_ids = [output.token_ids for outputs in all_outputs for output in outputs.outputs]

	if self.vllm_tensor_parallel_size > 1:
	# Slice completions for this rank within its TP group.
	# Each rank generates all outputs — we keep only our share.
	local_rank_in_group = torch.distributed.get_rank(group=self.tp_group)
	tp_slice = slice(local_rank_in_group * orig_size, (local_rank_in_group + 1) * orig_size)
	completion_ids = completion_ids[tp_slice]

	# Pad the completions, and concatenate them with the prompts
	completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
	completion_ids = pad(completion_ids, padding_value=self.pad_token_id)
	prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)

	elif self.use_transformers_paged:
	# Re-process inputs for paged generation if needed
	paged_prompt_inputs = self.processing_class(text=prompts_text)
	previous_attn = self.model_wrapped.config._attn_implementation

	if is_flash_attn_2_available():
	self.model_wrapped.config._attn_implementation = "paged_attention"
	else:
	self.model_wrapped.config._attn_implementation = "sdpa_paged"
	with (
	profiling_context(self, "transformers.generate_batch"),
	unwrap_model_for_generation(
	self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
	) as unwrapped_model,
	torch.no_grad(),
	FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(),
	):
	# Cast to the appropriate dtype based on training configuration
	if self.args.bf16:
	unwrapped_model.to(torch.bfloat16)
	elif self.args.fp16:
	unwrapped_model.to(torch.float16)
	with torch.inference_mode():
	all_outputs = unwrapped_model.generate_batch(
	paged_prompt_inputs.input_ids, generation_config=self.generation_config, progress_bar=False
	)
	completion_ids = [output.generated_tokens for output in all_outputs.values()]
	completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
	completion_ids = pad(completion_ids, padding_value=self.pad_token_id, padding_side="right")
	prompt_ids = [torch.tensor(ids, device=device) for ids in paged_prompt_inputs.input_ids]
	prompt_ids = pad(prompt_ids, padding_value=self.pad_token_id, padding_side="left")
	prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
	# Restore the original attention implementation, training mode
	self.model_wrapped.config._attn_implementation = previous_attn
	else:
	# Regular generation path
	with (
	profiling_context(self, "transformers.generate"),
	unwrap_model_for_generation(
	self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
	) as unwrapped_model,
	torch.no_grad(),
	FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(),
	):
	prompt_inputs["input_ids"], prompt_inputs["attention_mask"] = prompt_ids, prompt_mask
	prompt_completion_ids = unwrapped_model.generate(
	**prompt_inputs, generation_config=self.generation_config, disable_compile=True
	)
	# Compute prompt length and extract completion ids
	prompt_length = prompt_ids.size(1)
	prompt_ids = prompt_completion_ids[:, :prompt_length]
	completion_ids = prompt_completion_ids[:, prompt_length:]

	# Mask everything after the first EOS token
	is_eos = completion_ids == self.eos_token_id
	eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device)
	eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
	sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1)
	completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()

	# Convert tensor to a list of lists of token IDs. This will be passed to the reward function, avoiding the need
	# to re-tokenize completions if the reward is computed from tokens.
	completion_ids_list = [row[mask_row].tolist() for row, mask_row in zip(completion_ids, completion_mask.bool())]

	# Sum along sequence dimension (dim=1) to get completion length per sequence, used for logging
	completion_lengths = completion_mask.sum(1)

	# If mask_truncated_completions is enabled, zero out truncated completions in completion_mask
	if self.mask_truncated_completions:
	truncated_completions = ~is_eos.any(dim=1)
	completion_mask = completion_mask * (~truncated_completions).unsqueeze(1).int()

	# Concatenate prompt_mask with completion_mask for logit computation
	attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) # (B, P+C)

	logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens
	batch_size = self.args.per_device_train_batch_size if mode == "train" else self.args.per_device_eval_batch_size

	with torch.no_grad():
	# Compute the per-token log probabilities for the current model
	old_per_token_logps, _ = self._get_per_token_logps_and_entropies(
	self.model,
	prompt_completion_ids,
	attention_mask,
	logits_to_keep,
	batch_size,
	)
	old_logps = (old_per_token_logps * completion_mask).sum(1) # mask out padding and tokens after EOS

	# Compute the per-token log probabilities for the reference model
	if self.beta != 0.0:
	if self.ref_model is not None:
	ref_per_token_logps, _ = self._get_per_token_logps_and_entropies(
	self.ref_model,
	prompt_completion_ids,
	attention_mask,
	logits_to_keep,
	batch_size=batch_size,
	)
	else:
	with self.accelerator.unwrap_model(self.model).disable_adapter():
	ref_per_token_logps, _ = self._get_per_token_logps_and_entropies(
	self.model,
	prompt_completion_ids,
	attention_mask,
	logits_to_keep,
	batch_size=batch_size,
	)
	else:
	ref_per_token_logps = None

	# Decode the generated completions
	completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
	if is_conversational(inputs[0]):
	completions = []
	for prompt, completion in zip(prompts, completions_text):
	bootstrap = prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else ""
	completions.append([{"role": "assistant", "content": bootstrap + completion}])
	else:
	completions = completions_text

	# Calculate rewards for each reward function. rewards_per_func aggregates rewards across all processes. This is
	# important because rewards will be normalized per group, and completions are distributed. We will later slice
	# rewards_per_func to extract each process's subset.
	rewards_per_func = self._calculate_rewards(inputs, original_prompts, completions, completion_ids_list)

	# Apply weights to each reward function's output and sum
	rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1)

	# Apply reward clipping if specified
	if self.reward_clip_range:
	rewards = rewards.clamp(min=self.reward_clip_range[0], max=self.reward_clip_range[1])

	# Include the KL penalty in the reward
	if self.beta != 0.0:
	per_token_kl = old_per_token_logps - ref_per_token_logps
	# Apply sequence-level KL penalty to rewards (sum KL across tokens first, then apply to each sequence)
	kl = (per_token_kl * completion_mask).sum(-1)
	kl = gather(kl) # rewards are gathered, so kl must be too
	rewards = rewards - self.beta * kl

	grouped_rewards = rewards.view(-1, self.num_generations)
	mean_grouped_rewards = grouped_rewards.mean(dim=1)
	std_rewards = grouped_rewards.std(dim=1)
	is_std_zero = torch.isclose(std_rewards, torch.zeros_like(std_rewards))

	# RLOO advantages computation
	grouped_sum = grouped_rewards.sum(dim=1, keepdim=True) # (num_prompts, 1)
	baselines = (grouped_sum - grouped_rewards) / (self.num_generations - 1) # (num_prompts, num_generations)
	baselines = baselines.view(-1) # Flatten back to match rewards shape
	advantages = rewards - baselines

	# Normalize advantages
	if self.normalize_advantages:
	advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-4)

	# Slice to keep only the local part of the data
	process_slice = slice(
	self.accelerator.process_index * len(prompts),
	(self.accelerator.process_index + 1) * len(prompts),
	)
	all_process_advantages = advantages.clone() # keep the aggregated advantages for logging
	advantages = advantages[process_slice]

	# Log the metrics
	if mode == "train":
	self.state.num_input_tokens_seen += self.accelerator.gather(attention_mask.sum()).sum().item()
	self._metrics[mode]["num_tokens"] = [self.state.num_input_tokens_seen]

	# Calculate and log the mean KL divergence between current and reference model
	if self.beta != 0.0:
	mean_kl = (per_token_kl * completion_mask).sum() / completion_mask.sum().clamp(min=1.0)
	self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).nanmean().item())

	# Log completion lengths, mean, min, max
	agg_completion_lengths = self.accelerator.gather(completion_lengths)
	self._metrics[mode]["completions/mean_length"].append(agg_completion_lengths.float().mean().item())
	self._metrics[mode]["completions/min_length"].append(agg_completion_lengths.float().min().item())
	self._metrics[mode]["completions/max_length"].append(agg_completion_lengths.float().max().item())

	# Identify sequences that terminated with EOS and log their lengths
	agg_terminated_with_eos = self.accelerator.gather(is_eos.any(dim=1))
	term_completion_lengths = agg_completion_lengths[agg_terminated_with_eos]
	clipped_completions_ratio = 1 - len(term_completion_lengths) / len(agg_completion_lengths)
	self._metrics[mode]["completions/clipped_ratio"].append(clipped_completions_ratio)
	if len(term_completion_lengths) == 0: # edge case where no terminated sequences are found
	term_completion_lengths = torch.zeros(1, device=device)
	self._metrics[mode]["completions/mean_terminated_length"].append(term_completion_lengths.float().mean().item())
	self._metrics[mode]["completions/min_terminated_length"].append(term_completion_lengths.float().min().item())
	self._metrics[mode]["completions/max_terminated_length"].append(term_completion_lengths.float().max().item())

	# Calculate mean reward per function, but only for samples where the function was applied (non-NaN values)
	for i, reward_func_name in enumerate(self.reward_func_names):
	mean_rewards = torch.nanmean(rewards_per_func[:, i]).item()
	self._metrics[mode][f"rewards/{reward_func_name}/mean"].append(mean_rewards)
	std_func_rewards = nanstd(rewards_per_func[:, i]).item()
	self._metrics[mode][f"rewards/{reward_func_name}/std"].append(std_func_rewards)
	self._metrics[mode]["reward"].append(mean_grouped_rewards.mean().item())
	self._metrics[mode]["reward_std"].append(std_rewards.mean().item())
	self._metrics[mode]["frac_reward_zero_std"].append(is_std_zero.float().mean().item())

	# Log prompt and completion texts
	self._logs["prompt"].extend(gather_object(prompts_text))
	self._logs["completion"].extend(gather_object(completions_text))
	for i, name in enumerate(self.reward_func_names):
	self._logs["rewards"][name].extend(rewards_per_func[:, i].tolist())
	self._logs["advantages"].extend(all_process_advantages.tolist())

	output = {
	"prompt_ids": prompt_ids,
	"prompt_mask": prompt_mask,
	"completion_ids": completion_ids,
	"completion_mask": completion_mask,
	"old_logps": old_logps,
	"advantages": advantages,
	}
	return output

	@profiling_decorator
	def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
	if return_outputs:
	raise ValueError("The RLOOTrainer does not support returning outputs")
	return self._compute_loss(model, inputs)

	def _compute_loss(self, model, inputs):
	# Compute the per-token log probabilities for the model
	prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
	completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
	input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
	attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
	logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens

	# Compute the per_token_logps and the entropy at each position in the completion
	per_token_logps, entropies = self._get_per_token_logps_and_entropies(
	model,
	input_ids,
	attention_mask,
	logits_to_keep,
	compute_entropy=True,
	)
	logps = (per_token_logps * completion_mask).sum(1) # mask out padding and tokens after EOS
	old_logps = inputs["old_logps"]
	log_ratio = logps - old_logps

	# Compute the loss
	advantages = inputs["advantages"]
	coef_1 = torch.exp(log_ratio)
	coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high)
	per_sequence_loss1 = coef_1 * advantages
	per_sequence_loss2 = coef_2 * advantages
	per_sequence_loss = -torch.min(per_sequence_loss1, per_sequence_loss2)
	loss = per_sequence_loss.mean()

	# Log the metrics
	mode = "train" if self.model.training else "eval"

	# Entropy
	mean_entropy = (entropies * completion_mask).sum() / completion_mask.sum().clamp(min=1.0)
	self._metrics[mode]["entropy"].append(self.accelerator.gather(mean_entropy).nanmean().item())

	# Compute the clipped probability ratios
	is_low_clipped = (coef_1 < 1 - self.epsilon_low) & (advantages < 0)
	is_high_clipped = (coef_1 > 1 + self.epsilon_high) & (advantages > 0)
	is_region_clipped = is_low_clipped \| is_high_clipped
	gathered_low_clip = self.accelerator.gather(is_low_clipped.float().mean())
	self._metrics[mode]["clip_ratio/low_mean"].append(gathered_low_clip.nanmean().item())
	self._metrics[mode]["clip_ratio/low_min"].append(nanmin(gathered_low_clip).item())
	gathered_high_clip = self.accelerator.gather(is_high_clipped.float().mean())
	self._metrics[mode]["clip_ratio/high_mean"].append(gathered_high_clip.nanmean().item())
	self._metrics[mode]["clip_ratio/high_max"].append(nanmax(gathered_high_clip).item())
	gathered_clip_ratio = self.accelerator.gather(is_region_clipped.float().mean())
	self._metrics[mode]["clip_ratio/region_mean"].append(gathered_clip_ratio.nanmean().item())
	return loss

	def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: Optional[list[str]] = None):
	inputs = self._prepare_inputs(inputs)
	with torch.no_grad():
	with self.compute_loss_context_manager():
	loss = self.compute_loss(model, inputs)
	loss = loss.mean().detach()
	return loss, None, None

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

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

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

	if self.accelerator.is_main_process and self.log_completions:
	if is_rich_available():
	print_prompt_completions_sample(
	self._logs["prompt"],
	self._logs["completion"],
	self._logs["rewards"],
	self._logs["advantages"],
	self.state.global_step,
	self.num_completions_to_print,
	)

	if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None:
	import pandas as pd

	table = {
	"step": [str(self.state.global_step)] * len(self._logs["prompt"]),
	"prompt": self._logs["prompt"],
	"completion": self._logs["completion"],
	**self._logs["rewards"],
	"advantage": self._logs["advantages"],
	}

	df = pd.DataFrame(table)
	if self.wandb_log_unique_prompts:
	df = df.drop_duplicates(subset=["prompt"])
	wandb.log({"completions": wandb.Table(dataframe=df)})

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

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

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

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

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

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

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

	tags.update(self._tag_names)

	# docstyle-ignore
	citation = textwrap.dedent(
	"""\
	@inproceedings{ahmadian2024back,
	title = {{Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs}},
	author = {Arash Ahmadian and Chris Cremer and Matthias Gall{\'{e}} and Marzieh Fadaee and Julia Kreutzer and Olivier Pietquin and Ahmet {\"{U}}st{\"{u}}n and Sara Hooker},
	year = 2024,
	booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand, August 11-16, 2024},
	pages = {12248--12267},
	publisher = {Association for Computational Linguistics},
	editor = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar},
	}
	"""
	)

	model_card = generate_model_card(
	base_model=base_model,
	model_name=model_name,
	hub_model_id=self.hub_model_id,
	dataset_name=dataset_name,
	tags=tags,
	wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None,
	comet_url=get_comet_experiment_url(),
	trainer_name="RLOO",
	trainer_citation=citation,
	paper_title="Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs",
	paper_id="2402.14740",
	)

	model_card.save(os.path.join(self.args.output_dir, "README.md"))
	class UnslothRLOOTrainer(_UnslothRLOOTrainer):
	"""

	Trainer for the Reinforce Leave One Out (RLOO) method. This algorithm was initially proposed in the paper [Back to
	Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs]
	(https://huggingface.co/papers/2402.14740).

	Example:

	```python
	from datasets import load_dataset
	from trl import RLOOTrainer

	dataset = load_dataset("trl-lib/tldr", split="train")
	def reward_func(completions, **kwargs):
	# Dummy reward function that rewards completions with more unique letters.
	return [float(len(set(completion))) for completion in completions]
	trainer = RLOOTrainer(
	model="Qwen/Qwen2-0.5B-Instruct",
	reward_funcs=reward_func,
	train_dataset=dataset,
	)

	trainer.train()
	```

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

	- A string, being the model id of a pretrained model hosted inside a model repo on huggingface.co, or a
	path to a directory containing model weights saved using
	[`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
	using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in
	`args.model_init_kwargs`.
	- A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
	reward_funcs (`Union[RewardFunc, list[RewardFunc]]`):
	Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward
	functions with the prompts and completions and sum the rewards. Can be either:

	- A single reward function, such as:
	- A string: The model ID of a pretrained model hosted inside a model repo on huggingface.co, or a
	path to a directory containing model weights saved using
	[`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
	using [`~transformers.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the
	keyword arguments in `args.model_init_kwargs`.
	- A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported.
	- A custom reward function: The function is provided with the prompts and the generated completions,
	plus any additional columns in the dataset. It should return a list of rewards. Custom reward
	functions can also return `None` when the reward is not applicable to those samples. This is useful
	for multi-task training where different reward functions apply to different types of samples. When a
	reward function returns `None` for a sample, that reward function is excluded from the reward
	calculation for that sample. For more details, see [Using a custom reward
	function](#using-a-custom-reward-function).

	The trainer's state is also passed to the reward function. The trainer's state is an instance of
	[`~transformers.TrainerState`] and can be accessed by accessing the `trainer_state` argument to the
	reward function's signature.
	- A list of reward functions, where each item can independently be any of the above types. Mixing different
	types within the list (e.g., a string model ID and a custom reward function) is allowed.
	args ([`RLOOConfig`], optional, defaults to `None`):
	Configuration for this trainer. If `None`, a default configuration is used.
	train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
	Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is
	ignored. The format of the samples can be either:

	- [Standard](dataset_formats#standard): Each sample contains plain text.
	- [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role
	and content).
	eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset,
	IterableDataset]]`):
	Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
	processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`] or `None`, optional, defaults to `None`):
	Processing class used to process the data. The padding side must be set to "left". If `None`, the
	processing class is loaded from the model's name with [`~transformers.AutoProcessor.from_pretrained`]. A
	padding token, `tokenizer.pad_token`, must be set. If the processing class has not set a padding token,
	`tokenizer.eos_token` will be used as the default.
	reward_processing_classes (`Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]`, optional, defaults to `None`):
	Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either:

	- A single processing class: Used when `reward_funcs` contains only one reward function.
	- A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`.
	If set to `None`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is
	`None`, the tokenizer for the model is automatically loaded using
	[`~transformers.AutoTokenizer.from_pretrained`]. For elements in `reward_funcs` that are custom reward
	functions (not [`~transformers.PreTrainedModel`]), the corresponding entries in `reward_processing_classes`
	are ignored.
	callbacks (list of [`~transformers.TrainerCallback`], optional, defaults to `None`):
	List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed
	in [here](https://huggingface.co/docs/transformers/main_classes/callback).

	If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`]
	method.
	optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, optional, defaults to `(None,
	None)`):
	A tuple containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your
	model and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
	peft_config ([`~peft.PeftConfig`], optional, defaults to `None`):
	PEFT configuration used to wrap the model. If `None`, the model is not wrapped.

	"""
	def __init__(
	self,
	model = None,
	reward_funcs = None,
	args = None,
	train_dataset = None,
	eval_dataset = None,
	processing_class = None,
	reward_processing_classes = None,
	callbacks = None,
	peft_config = None,
	config = None,
	reward_model = None,
	policy = None,
	ref_policy = None,
	data_collator = None,
	**kwargs
	):
	if args is None: args = UnslothRLOOConfig()
	use_bf16 = getattr(args, 'bf16', False)
	if type(use_bf16) is not bool: use_bf16 = False
	use_fp16 = getattr(args, 'fp16', False)
	if type(use_fp16) is not bool: use_fp16 = False
	force_float32 = False
	full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'
	if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'):
	print('Unsloth: Switching to float32 training since model cannot work with float16')
	force_float32 = True
	mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
	dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)
	if dtype is None: dtype = model.get_input_embeddings().dtype
	from unsloth_zoo.utils import _get_dtype
	dtype = _get_dtype(dtype)
	float16 = dtype == torch.float16
	if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
	if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
	if force_float32:
	# Forced float32 training
	args.fp16 = False
	args.bf16 = False
	os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
	elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
	# Mixed precision training
	args.fp16 = float16
	args.bf16 = not float16
	os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
	if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
	args.eval_strategy = 'steps'
	if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
	ga_steps = getattr(args, 'gradient_accumulation_steps', None)
	if ga_steps is not None and ga_steps > 1:
	from transformers import __version__ as transformers_version
	if Version(transformers_version) <= Version('4.45.2'):
	print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
	'`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
	if getattr(args, 'eval_strategy', 'no') != 'no':
	eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
	if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
	if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
	fp16_full_eval = getattr(args, 'fp16_full_eval', False)
	if type(fp16_full_eval) is not bool: fp16_full_eval = False
	bf16_full_eval = getattr(args, 'bf16_full_eval', False)
	if type(bf16_full_eval) is not bool: bf16_full_eval = False
	if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
	if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
	if force_float32:
	args.bf16_full_eval = False
	args.fp16_full_eval = False
	elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
	args.bf16_full_eval = True
	args.fp16_full_eval = False
	elif not bf16_full_eval and not fp16_full_eval:
	args.bf16_full_eval = args.bf16
	args.fp16_full_eval = args.fp16
	_output_logits = False
	if locals().get('compute_metrics', None) is not None: _output_logits = True
	if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
	if _output_logits:
	os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
	if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
	pass
	else:
	model_max_seq_length = getattr(model, 'max_seq_length', None)
	args_max_seq_length = getattr(args, 'max_seq_length', None)
	if args_max_seq_length is None and model_max_seq_length is not None:
	max_seq_length = model.max_seq_length
	if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
	if model is not None and hasattr(model, 'for_training'):
	model.for_training()
	if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
	if 'processing_class' in locals():
	if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
	if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
	__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('rloo_trainer', other_metrics)

	# [TODO] Fix up DataParallel multiplying batch sizes
	# [TODO] DDP works, but DP seems to not work? [TODO]
	if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
	if getattr(args, "_n_gpu", 1) != 1:
	args._n_gpu = 1
	if "model" in locals() and hasattr(model, "for_training"):
	model.for_training()
	super().__init__(
	model = model,
	reward_funcs = reward_funcs,
	args = args,
	train_dataset = train_dataset,
	eval_dataset = eval_dataset,
	processing_class = processing_class,
	reward_processing_classes = reward_processing_classes,
	callbacks = callbacks,
	peft_config = peft_config,
	config = config,
	reward_model = reward_model,
	policy = policy,
	ref_policy = ref_policy,
	data_collator = data_collator,**kwargs)
	if "model" in locals() and hasattr(model, "for_inference"):
	model.for_inference()
	if hasattr(self, 'neftune_hook_handle'):
	self.neftune_hook_handle.remove()
	if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
	if getattr(args, 'neftune_noise_alpha', None) is not None:
	model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
	pass
	if hasattr(self, 'accelerator'):
	scaler = self.accelerator.scaler
	current_model = model
	while hasattr(current_model, 'model'):
	current_model.accelerator_scaler = scaler
	current_model = current_model.model
	current_model.accelerator_scaler = scaler
	pass
	if hasattr(self, 'train'):
	self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)
	pass

	pass


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