unsloth_deepseek/src/unsloth_compiled_cache/UnslothORPOTrainer.py

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from trl.trainer.orpo_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DPODataCollatorWithPadding, DataCollator, DataLoader, Dataset, EvalLoopOutput, F, FeatureExtractionMixin, Literal, ORPOConfig, ORPOTrainer, Optional, PartialState, PeftModel, PreTrainedModel, PreTrainedModelWrapper, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, Union, add_bos_token_if_needed, add_eos_token_if_needed, amp, deepcopy, defaultdict, disable_dropout_in_model, generate_model_card, get_comet_experiment_url, inspect, is_comet_available, is_peft_available, is_torch_fx_proxy, is_torch_xla_available, is_wandb_available, log_table_to_comet_experiment, maybe_apply_chat_template, maybe_extract_prompt, nn, np, nullcontext, os, pad_to_length, pd, peft_module_casting_to_bf16, prepare_model_for_kbit_training, random, textwrap, torch, transformers, version, warnings)


import os
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
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 selective_log_softmax(logits, index):
    logits = logits.to(torch.float32)
    selected_logits = torch.gather(logits, dim = -1, index = index.unsqueeze(-1)).squeeze(-1)
    # loop to reduce peak mem consumption
    # logsumexp_values = torch.stack([torch.logsumexp(lg, dim=-1) for lg in logits])
    logsumexp_values = torch.logsumexp(logits, dim = -1)
    per_token_logps = selected_logits - logsumexp_values  # log_softmax(x_i) = x_i - logsumexp(x)
    return per_token_logps
@dataclass
class UnslothORPOConfig(ORPOConfig):
    """
    
    Configuration class for the [`ORPOTrainer`].

    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:
        learning_rate (`float`, *optional*, defaults to `1e-6`):
            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
            [`~transformers.TrainingArguments`].
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
            to use the default data collator.
        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
            Maximum length of the prompt. This argument is required if you want to use the default data collator.
        max_completion_length (`int` or `None`, *optional*, defaults to `None`):
            Maximum length of the completion. This argument is required if you want to use the default data collator
            and your model is an encoder-decoder.
        beta (`float`, *optional*, defaults to `0.1`):
            Parameter controlling the relative ratio loss weight in the ORPO loss. In the [paper](https://huggingface.co/papers/2403.07691),
            it is denoted by λ. In the [code](https://github.com/xfactlab/orpo), it is denoted by `alpha`.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model.
        label_pad_token_id (`int`, *optional*, defaults to `-100`):
            Label pad token id. This argument is required if you want to use the default data collator.
        padding_value (`int` or `None`, *optional*, defaults to `None`):
            Padding value to use. If `None`, the padding value of the tokenizer is used.
        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
            This argument is required if you want to use the default data collator.
        generate_during_eval (`bool`, *optional*, defaults to `False`):
            If `True`, generates and logs completions from the model to W&B or Comet during evaluation.
        is_encoder_decoder (`bool` or `None`, *optional*, defaults to `None`):
            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
            you need to specify if the model returned by the callable is an encoder-decoder model.
        model_init_kwargs (`dict[str, Any]` or `None`, *optional*, defaults to `None`):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
            string.
        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
            Number of processes to use for processing the dataset.
    
    """
    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 = True,
        label_names = None,
        load_best_model_at_end = False,
        metric_for_best_model = None,
        greater_is_better = None,
        ignore_data_skip = False,
        fsdp = '',
        fsdp_min_num_params = 0,
        fsdp_config = None,
        fsdp_transformer_layer_cls_to_wrap = None,
        accelerator_config = None,
        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,
        gradient_checkpointing = False,
        gradient_checkpointing_kwargs = None,
        include_inputs_for_metrics = False,
        eval_do_concat_batches = True,
        fp16_backend = 'auto',
        evaluation_strategy = None,
        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,
        dispatch_batches = None,
        split_batches = 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,
        eval_use_gather_object = False,
        average_tokens_across_devices = False,
        max_length = 1024,
        max_prompt_length = 512,
        max_completion_length = None,
        beta = 0.1,
        disable_dropout = True,
        label_pad_token_id = -100,
        padding_value = None,
        truncation_mode = 'keep_end',
        generate_during_eval = False,
        is_encoder_decoder = None,
        model_init_kwargs = None,
        dataset_num_proc = None,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        **kwargs,
    ):
        if learning_rate < 1e-7: raise FloatingPointError(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: raise OverflowError(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 = cpu_count()
        
        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,
            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,
            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,
            evaluation_strategy = evaluation_strategy,
            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,
            dispatch_batches = dispatch_batches,
            split_batches = split_batches,
            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,
            eval_use_gather_object = eval_use_gather_object,
            average_tokens_across_devices = average_tokens_across_devices,
            max_length = max_length,
            max_prompt_length = max_prompt_length,
            max_completion_length = max_completion_length,
            beta = beta,
            disable_dropout = disable_dropout,
            label_pad_token_id = label_pad_token_id,
            padding_value = padding_value,
            truncation_mode = truncation_mode,
            generate_during_eval = generate_during_eval,
            is_encoder_decoder = is_encoder_decoder,
            model_init_kwargs = model_init_kwargs,
            dataset_num_proc = dataset_num_proc,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
pass

class _UnslothORPOTrainer(Trainer):
    r""""""

    _tag_names = ["trl", "orpo"]

    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
        args: Optional[ORPOConfig] = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional[dict] = None,
        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
    ):
        if args.model_init_kwargs is None:
            model_init_kwargs = {}
        elif not isinstance(model, str):
            raise ValueError("You passed model_kwargs to the ORPOTrainer. But your model is already instantiated.")
        else:
            model_init_kwargs = args.model_init_kwargs
            torch_dtype = model_init_kwargs.get("torch_dtype")
            if torch_dtype is not None:
                # Convert to `torch.dtype` if an str is passed
                if isinstance(torch_dtype, str) and torch_dtype != "auto":
                    torch_dtype = getattr(torch, torch_dtype)
                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
                    raise ValueError(
                        f"Invalid `torch_dtype` passed to the ORPOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
                    )
                model_init_kwargs["torch_dtype"] = torch_dtype

        if isinstance(model, str):
            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)

        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
        # has been called in order to properly call autocast if needed.
        self._peft_has_been_casted_to_bf16 = False

        if not is_peft_available() and peft_config is not None:
            raise ValueError(
                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models"
            )
        elif is_peft_available() and peft_config is not None:
            # if model is a peft model and we have a peft_config, we merge and unload it first
            if isinstance(model, PeftModel):
                model = model.merge_and_unload()

            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
                _support_gc_kwargs = hasattr(
                    args, "gradient_checkpointing_kwargs"
                ) and "gradient_checkpointing_kwargs" in list(
                    inspect.signature(prepare_model_for_kbit_training).parameters
                )

                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}

                if _support_gc_kwargs:
                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs

                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
            elif getattr(args, "gradient_checkpointing", False):
                # For backward compatibility with older versions of transformers
                if hasattr(model, "enable_input_require_grads"):
                    model.enable_input_require_grads()
                else:

                    def make_inputs_require_grad(module, input, output):
                        output.requires_grad_(True)

                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

            # get peft model with the given config
            model = model
            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
                peft_module_casting_to_bf16(model)
                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
                self._peft_has_been_casted_to_bf16 = True

        # For models that use gradient_checkpointing, we need to attach a hook that enables input
        # to explicitly have `requires_grad=True`, otherwise training will either silently
        # fail or completely fail.
        elif getattr(args, "gradient_checkpointing", False):
            # For backward compatibility with older versions of transformers
            if hasattr(model, "enable_input_require_grads"):
                model.enable_input_require_grads()
            else:

                def make_inputs_require_grad(module, input, output):
                    output.requires_grad_(True)

                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
            raise ValueError(
                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
                " Please install `wandb` or `comet-ml` to resolve."
            )

        if model is not None:
            self.is_encoder_decoder = model.config.is_encoder_decoder
        elif args.is_encoder_decoder is None:
            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
        else:
            self.is_encoder_decoder = args.is_encoder_decoder

        if self.is_encoder_decoder:
            self.decoder_start_token_id = model.config.decoder_start_token_id
            self.pad_token_id = model.config.pad_token_id

        if processing_class is None:
            raise ValueError("processing_class must be specified to tokenize a ORPO dataset.")
        if args.max_length is None:
            warnings.warn(
                "`max_length` is not set in the ORPOConfig's init"
                " it will default to `512` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_length = 512
        else:
            max_length = args.max_length
        if args.max_prompt_length is None:
            warnings.warn(
                "`max_prompt_length` is not set in the ORPOConfig's init"
                " it will default to `128` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            max_prompt_length = 128
        else:
            max_prompt_length = args.max_prompt_length

        if args.max_completion_length is None and self.is_encoder_decoder:
            warnings.warn(
                "When using an encoder decoder architecture, you should set `max_completion_length` in the ORPOConfig's init"
                " it will default to `128` by default, but you should do it yourself in the future.",
                UserWarning,
            )
            self.max_completion_length = 128
        else:
            self.max_completion_length = args.max_completion_length

        if data_collator is None:
            data_collator = DPODataCollatorWithPadding(
                pad_token_id=processing_class.pad_token_id,
                label_pad_token_id=args.label_pad_token_id,
                is_encoder_decoder=self.is_encoder_decoder,
            )

            if args.remove_unused_columns:
                args.remove_unused_columns = False
                # warn users
                warnings.warn(
                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your TrainingArguments"
                    " we have set it for you, but you should do it yourself in the future.",
                    UserWarning,
                )

            self.use_dpo_data_collator = True
        else:
            self.use_dpo_data_collator = False

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

        self.max_length = max_length
        self.generate_during_eval = args.generate_during_eval
        self.label_pad_token_id = args.label_pad_token_id
        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
        self.max_prompt_length = max_prompt_length
        self.truncation_mode = args.truncation_mode
        self.processing_class = processing_class

        self.beta = args.beta
        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
            warnings.warn(
                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
                "loss.",
                UserWarning,
            )

        self._stored_metrics = defaultdict(lambda: defaultdict(list))

        # 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 ORPO, the sampled data does not include the
        # "input_ids" key. Instead, the available keys are "prompt_input_ids", "chosen_input_ids", and
        # "rejected_input_ids". 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

        # Compute that only on the main process for faster data processing.
        # see: https://github.com/huggingface/trl/pull/1255
        with PartialState().local_main_process_first():
            # Extract the prompt if needed, and apply the chat template if needed
            train_dataset = train_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
            train_dataset = train_dataset.map(
                maybe_apply_chat_template, fn_kwargs={"tokenizer": processing_class}, num_proc=args.dataset_num_proc
            )
            train_dataset = train_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)
            if eval_dataset is not None:
                eval_dataset = eval_dataset.map(maybe_extract_prompt, num_proc=args.dataset_num_proc)
                eval_dataset = eval_dataset.map(
                    maybe_apply_chat_template,
                    fn_kwargs={"tokenizer": processing_class},
                    num_proc=args.dataset_num_proc,
                )
                eval_dataset = eval_dataset.map(self.tokenize_row, num_proc=args.dataset_num_proc)

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

        # Add tags for models that have been loaded with the correct transformers version
        if hasattr(self.model, "add_model_tags"):
            self.model.add_model_tags(self._tag_names)

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

    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)

        if model is not None:
            if hasattr(model, "config"):
                hidden_size = (
                    max(model.config.hidden_sizes)
                    if getattr(model.config, "hidden_sizes", None)
                    else getattr(model.config, "hidden_size", None)
                )
                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
                    config_kwargs.update(
                        {
                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
                        }
                    )

        # If ZeRO-3 is used, we shard both the active and reference model.
        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
        if config_kwargs["zero_optimization"]["stage"] != 3:
            config_kwargs["zero_optimization"]["stage"] = 0
        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
        model.eval()
        return model

    def build_tokenized_answer(self, prompt, answer):
        """
        Llama tokenizer does satisfy `enc(a + b) = enc(a) + enc(b)`.
        It does ensure `enc(a + b) = enc(a) + enc(a + b)[len(enc(a)):]`.
        Reference:
            https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
        """

        full_tokenized = self.processing_class(prompt + answer, add_special_tokens=False)
        prompt_input_ids = self.processing_class(prompt, add_special_tokens=False)["input_ids"]

        answer_input_ids = full_tokenized["input_ids"][len(prompt_input_ids) :]
        answer_attention_mask = full_tokenized["attention_mask"][len(prompt_input_ids) :]

        # Concat tokens to form `enc(a) + enc(a + b)[len(enc(a)):]`
        full_concat_input_ids = np.concatenate([prompt_input_ids, answer_input_ids])

        # Prepare input tokens for token by token comparison
        full_input_ids = np.array(full_tokenized["input_ids"])

        if len(full_input_ids) != len(full_concat_input_ids):
            raise ValueError("Prompt input ids and answer input ids should have the same length.")

        # On some tokenizers, like Llama-2 tokenizer, there are occasions where tokens
        # can be merged together when tokenizing prompt+answer. This could result
        # on the last token from the prompt being different when tokenized on its own
        # vs when done as prompt+answer.
        response_token_ids_start_idx = len(prompt_input_ids)

        # If tokenized prompt is different than both prompt+answer, then it means the
        # last token has changed due to merging.
        if prompt_input_ids != full_tokenized["input_ids"][:response_token_ids_start_idx]:
            response_token_ids_start_idx -= 1

        prompt_input_ids = full_tokenized["input_ids"][:response_token_ids_start_idx]
        prompt_attention_mask = full_tokenized["attention_mask"][:response_token_ids_start_idx]

        if len(prompt_input_ids) != len(prompt_attention_mask):
            raise ValueError("Prompt input ids and attention mask should have the same length.")

        answer_input_ids = full_tokenized["input_ids"][response_token_ids_start_idx:]
        answer_attention_mask = full_tokenized["attention_mask"][response_token_ids_start_idx:]

        return dict(
            prompt_input_ids=prompt_input_ids,
            prompt_attention_mask=prompt_attention_mask,
            input_ids=answer_input_ids,
            attention_mask=answer_attention_mask,
        )

    def tokenize_row(self, feature, model: Optional[Union[PreTrainedModel, nn.Module]] = None) -> dict:
        """Tokenize a single row from a ORPO specific dataset.

        At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
        in case the prompt + chosen or prompt + rejected responses is/are too long. First
        we truncate the prompt; if we're still too long, we truncate the chosen/rejected.

        We also create the labels for the chosen/rejected responses, which are of length equal to
        the sum of the length of the prompt and the chosen/rejected response, with
        label_pad_token_id  for the prompt tokens.
        """
        batch = {}
        prompt = feature["prompt"]
        chosen = feature["chosen"]
        rejected = feature["rejected"]

        if not self.is_encoder_decoder:
            # Check issues below for more details
            #  1. https://github.com/huggingface/trl/issues/907
            #  2. https://github.com/EleutherAI/lm-evaluation-harness/pull/531#issuecomment-1595586257
            #  3. https://github.com/LianjiaTech/BELLE/issues/337

            if not isinstance(prompt, str):
                raise ValueError(f"prompt should be an str but got {type(prompt)}")
            prompt_tokens = self.processing_class(prompt, add_special_tokens=False)
            prompt_tokens = {f"prompt_{k}": v for k, v in prompt_tokens.items()}

            if not isinstance(chosen, str):
                raise ValueError(f"chosen should be an str but got {type(chosen)}")
            chosen_tokens = self.build_tokenized_answer(prompt, chosen)

            if not isinstance(rejected, str):
                raise ValueError(f"rejected should be an str but got {type(rejected)}")
            rejected_tokens = self.build_tokenized_answer(prompt, rejected)

            # Last prompt token might get merged by tokenizer and
            # it should not be included for generation if that happens
            prompt_len_input_ids = len(prompt_tokens["prompt_input_ids"])

            chosen_prompt_len_input_ids = len(chosen_tokens["prompt_input_ids"])
            rejected_prompt_len_input_ids = len(rejected_tokens["prompt_input_ids"])
            prompt_len_input_ids = min(chosen_prompt_len_input_ids, rejected_prompt_len_input_ids)

            for k, v in prompt_tokens.items():
                prompt_tokens[k] = v[:prompt_len_input_ids]

            # Make sure prompts only have one different token at most an
            # and length only differs by 1 at most
            num_diff_tokens = sum(
                [a != b for a, b in zip(chosen_tokens["prompt_input_ids"], rejected_tokens["prompt_input_ids"])]
            )
            num_diff_len = abs(chosen_prompt_len_input_ids - rejected_prompt_len_input_ids)
            if num_diff_tokens > 1 or num_diff_len > 1:
                raise ValueError(
                    "Chosen and rejected prompt_input_ids might only differ on the "
                    "last token due to tokenizer merge ops."
                )

            # add BOS token to head of prompt. Avoid adding if it's already there
            prompt_tokens, chosen_tokens, rejected_tokens = add_bos_token_if_needed(
                self.processing_class.bos_token_id,
                prompt_len_input_ids,
                prompt_tokens,
                chosen_prompt_len_input_ids,
                chosen_tokens,
                rejected_prompt_len_input_ids,
                rejected_tokens,
            )

            # add EOS token to end of answer. Avoid adding if it's already there
            chosen_tokens, rejected_tokens = add_eos_token_if_needed(
                self.processing_class.eos_token_id, chosen_tokens, rejected_tokens
            )

            longer_response_length = max(len(chosen_tokens["input_ids"]), len(rejected_tokens["input_ids"]))

            # if combined sequence is too long, truncate the prompt
            for answer_tokens in [chosen_tokens, rejected_tokens, prompt_tokens]:
                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
                    if self.truncation_mode == "keep_start":
                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
                            answer_tokens[k] = answer_tokens[k][: self.max_prompt_length]
                    elif self.truncation_mode == "keep_end":
                        for k in ["prompt_input_ids", "prompt_attention_mask"]:
                            answer_tokens[k] = answer_tokens[k][-self.max_prompt_length :]
                    else:
                        raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")

            # if that's still too long, truncate the response
            for answer_tokens in [chosen_tokens, rejected_tokens]:
                if len(answer_tokens["prompt_input_ids"]) + longer_response_length > self.max_length:
                    for k in ["input_ids", "attention_mask"]:
                        answer_tokens[k] = answer_tokens[k][: self.max_length - self.max_prompt_length]

            # Create labels
            chosen_sequence_tokens = {
                k: chosen_tokens[f"prompt_{k}"] + chosen_tokens[k] for k in ["input_ids", "attention_mask"]
            }
            rejected_sequence_tokens = {
                k: rejected_tokens[f"prompt_{k}"] + rejected_tokens[k] for k in ["input_ids", "attention_mask"]
            }
            chosen_sequence_tokens["labels"] = chosen_sequence_tokens["input_ids"][:]
            chosen_sequence_tokens["labels"][: len(chosen_tokens["prompt_input_ids"])] = [
                self.label_pad_token_id
            ] * len(chosen_tokens["prompt_input_ids"])
            rejected_sequence_tokens["labels"] = rejected_sequence_tokens["input_ids"][:]
            rejected_sequence_tokens["labels"][: len(rejected_tokens["prompt_input_ids"])] = [
                self.label_pad_token_id
            ] * len(rejected_tokens["prompt_input_ids"])

            for k, toks in {
                "chosen_": chosen_sequence_tokens,
                "rejected_": rejected_sequence_tokens,
                "": prompt_tokens,
            }.items():
                for type_key, tokens in toks.items():
                    if type_key == "token_type_ids":
                        continue
                    batch[f"{k}{type_key}"] = tokens

        else:
            chosen_tokens = self.processing_class(
                chosen, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
            )
            rejected_tokens = self.processing_class(
                rejected, truncation=True, max_length=self.max_completion_length, add_special_tokens=True
            )
            prompt_tokens = self.processing_class(
                prompt, truncation=True, max_length=self.max_prompt_length, add_special_tokens=True
            )

            batch["chosen_labels"] = chosen_tokens["input_ids"]
            batch["rejected_labels"] = rejected_tokens["input_ids"]
            batch["prompt_input_ids"] = prompt_tokens["input_ids"]
            batch["prompt_attention_mask"] = prompt_tokens["attention_mask"]

            if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"):
                batch["rejected_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
                    labels=torch.tensor(batch["rejected_labels"])
                )
                batch["chosen_decoder_input_ids"] = model.prepare_decoder_input_ids_from_labels(
                    labels=torch.tensor(batch["chosen_labels"])
                )

        if is_torch_xla_available():
            # Pad the sequences to global max_length to avoid TorchXLA recompilation
            for k in batch:
                if "labels" in k or self.is_encoder_decoder:
                    pad_value = self.label_pad_token_id
                elif k.endswith("_input_ids"):
                    pad_value = self.padding_value
                elif k.endswith("_attention_mask"):
                    pad_value = 0
                batch[k] = batch[k] + [pad_value] * (self.max_length - len(batch[k]))
        return batch

    @staticmethod
    def concatenated_inputs(
        batch: dict[str, Union[list, torch.LongTensor]],
        is_encoder_decoder: bool = False,
        label_pad_token_id: int = -100,
        padding_value: int = 0,
        device: Optional[torch.device] = None,
    ) -> dict[str, torch.LongTensor]:
        """Concatenate the chosen and rejected inputs into a single tensor.

        Args:
            batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).
            is_encoder_decoder: Whether the model is an encoder-decoder model.
            label_pad_token_id: The label pad token id.
            padding_value: The padding value to use for the concatenated inputs_ids.
            device: The device for the concatenated inputs.

        Returns:
            A dictionary containing the concatenated inputs under the key 'concatenated_input_ids'.
        """
        concatenated_batch = {}

        if is_encoder_decoder:
            max_length = max(batch["chosen_labels"].shape[1], batch["rejected_labels"].shape[1])
        else:
            max_length = max(batch["chosen_input_ids"].shape[1], batch["rejected_input_ids"].shape[1])

        for k in batch:
            if k.startswith("chosen") and isinstance(batch[k], torch.Tensor):
                if "labels" in k or is_encoder_decoder:
                    pad_value = label_pad_token_id
                elif k.endswith("_input_ids"):
                    pad_value = padding_value
                elif k.endswith("_attention_mask"):
                    pad_value = 0
                concatenated_key = k.replace("chosen", "concatenated")
                concatenated_batch[concatenated_key] = pad_to_length(batch[k], max_length, pad_value=pad_value)
        for k in batch:
            if k.startswith("rejected") and isinstance(batch[k], torch.Tensor):
                if "labels" in k or is_encoder_decoder:
                    pad_value = label_pad_token_id
                elif k.endswith("_input_ids"):
                    pad_value = padding_value
                elif k.endswith("_attention_mask"):
                    pad_value = 0
                concatenated_key = k.replace("rejected", "concatenated")
                concatenated_batch[concatenated_key] = torch.cat(
                    (
                        concatenated_batch[concatenated_key],
                        pad_to_length(batch[k], max_length, pad_value=pad_value),
                    ),
                    dim=0,
                ).to(device=device)

        if is_encoder_decoder:
            concatenated_batch["concatenated_input_ids"] = batch["prompt_input_ids"].repeat(2, 1).to(device=device)
            concatenated_batch["concatenated_attention_mask"] = (
                batch["prompt_attention_mask"].repeat(2, 1).to(device=device)
            )

        return concatenated_batch

    def odds_ratio_loss(
        self,
        policy_chosen_logps: torch.FloatTensor,
        policy_rejected_logps: torch.FloatTensor,
    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Compute ORPO's odds ratio (OR) loss for a batch of policy and reference model log probabilities.

        Args:
            policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
            policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_size,)

        Returns:
            A tuple of three tensors: (losses, chosen_rewards, rejected_rewards).
            The losses tensor contains the ORPO loss for each example in the batch.
            The chosen_rewards and rejected_rewards tensors contain the rewards for the chosen and rejected responses, respectively.
            The log odds ratio of the chosen responses over the rejected responses ratio for logging purposes.
            The `log(sigmoid(log_odds_chosen))` for logging purposes.
        """

        # Derived from Eqs. (4) and (7) from https://huggingface.co/papers/2403.07691 by using log identities and exp(log(P(y|x)) = P(y|x)
        log_odds = (policy_chosen_logps - policy_rejected_logps) - (
            torch.log1p(-torch.exp(policy_chosen_logps)) - torch.log1p(-torch.exp(policy_rejected_logps))
        )
        ratio = F.logsigmoid(log_odds)
        losses = self.beta * ratio

        chosen_rewards = self.beta * (policy_chosen_logps.to(self.accelerator.device)).detach()
        rejected_rewards = self.beta * (policy_rejected_logps.to(self.accelerator.device)).detach()

        return losses, chosen_rewards, rejected_rewards, torch.mean(ratio), torch.mean(log_odds)

    @staticmethod
    def get_batch_logps(
        logits: torch.FloatTensor,
        labels: torch.LongTensor,
        average_log_prob: bool = False,
        label_pad_token_id: int = -100,
        is_encoder_decoder: bool = False,
    ) -> torch.FloatTensor:
        """Compute the log probabilities of the given labels under the given logits.

        Args:
            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
            labels: Labels for which to compute the log probabilities. Label tokens with a value of label_pad_token_id are ignored. Shape: (batch_size, sequence_length)
            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
            label_pad_token_id: The label pad token id.
            is_encoder_decoder: Whether the model is an encoder-decoder model.

        Returns:
            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
        """
        if logits.shape[:-1] != labels.shape:
            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")

        if not is_encoder_decoder:
            labels = labels[:, 1:].clone()
            logits = logits[:, :-1, :]
        loss_mask = labels != label_pad_token_id

        # dummy token; we'll ignore the losses on these tokens later
        labels = torch.where(labels == label_pad_token_id, 0, labels)

        per_token_logps = selective_log_softmax(logits, labels)

        if average_log_prob:
            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
        else:
            return (per_token_logps * loss_mask).sum(-1)

    def concatenated_forward(
        self, model: nn.Module, batch: dict[str, Union[list, torch.LongTensor]]
    ) -> tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.

        We do this to avoid doing two forward passes, because it's faster for FSDP.
        """
        concatenated_batch = self.concatenated_inputs(
            batch,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
            padding_value=self.padding_value,
            device=self.accelerator.device,
        )
        len_chosen = batch["chosen_labels"].shape[0]

        model_kwargs = (
            {
                "decoder_input_ids": self._shift_right(concatenated_batch["concatenated_labels"]),
            }
            if self.is_encoder_decoder
            else {}
        )

        if self.aux_loss_enabled:
            model_kwargs["output_router_logits"] = True

        outputs = model(
            concatenated_batch["concatenated_input_ids"],
            attention_mask=concatenated_batch["concatenated_attention_mask"],
            use_cache=False,
            **model_kwargs,
        )
        all_logits = outputs.logits

        def cross_entropy_loss(logits, labels):
            if not self.is_encoder_decoder:
                # Shift so that tokens < n predict n
                logits = logits[..., :-1, :].contiguous()
                labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss()
            logits = logits.view(-1, logits.shape[-1])
            labels = labels.view(-1)
            # Enable model parallelism
            labels = labels.to(logits.device)
            loss = loss_fct(logits, labels)
            return loss

        if self.is_encoder_decoder:
            labels = concatenated_batch["concatenated_labels"].clone()
        else:
            labels = concatenated_batch["concatenated_input_ids"].clone()
            attention_mask = concatenated_batch["concatenated_attention_mask"]
            labels = torch.where(attention_mask == 1, labels, self.label_pad_token_id)
        # orpo chosen nll loss is computed over the full prompt and response
        chosen_nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])

        all_logps = self.get_batch_logps(
            all_logits,
            concatenated_batch["concatenated_labels"],
            average_log_prob=True,
            is_encoder_decoder=self.is_encoder_decoder,
            label_pad_token_id=self.label_pad_token_id,
        )

        chosen_logps = all_logps[:len_chosen]
        rejected_logps = all_logps[len_chosen:]

        if not self.is_encoder_decoder:
            chosen_logits = all_logits[:len_chosen, :-1, :]
            rejected_logits = all_logits[len_chosen:, :-1, :]
        else:
            chosen_logits = all_logits[:len_chosen]
            rejected_logits = all_logits[len_chosen:]

        if self.aux_loss_enabled:
            return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_nll_loss, outputs.aux_loss)

        return (chosen_logps, rejected_logps, chosen_logits, rejected_logits, chosen_nll_loss)

    def get_batch_loss_metrics(
        self,
        model,
        batch: dict[str, Union[list, torch.LongTensor]],
        train_eval: Literal["train", "eval"] = "train",
    ):
        """Compute the ORPO loss and other metrics for the given batch of inputs for train or test."""
        metrics = {}

        forward_output = self.concatenated_forward(model, batch)
        (
            policy_chosen_logps,
            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
            policy_nll_loss,
        ) = forward_output[:5]
        if self.aux_loss_enabled:
            aux_loss = forward_output[5]

        losses, chosen_rewards, rejected_rewards, log_odds_ratio, log_odds_chosen = self.odds_ratio_loss(
            policy_chosen_logps, policy_rejected_logps
        )
        # full ORPO loss
        loss = policy_nll_loss - losses.mean()

        reward_accuracies = (chosen_rewards > rejected_rewards).float()

        prefix = "eval_" if train_eval == "eval" else ""
        metrics[f"{prefix}rewards/chosen"] = self.accelerator.gather_for_metrics(chosen_rewards).mean()
        metrics[f"{prefix}rewards/rejected"] = self.accelerator.gather_for_metrics(rejected_rewards).mean()
        metrics[f"{prefix}rewards/accuracies"] = self.accelerator.gather_for_metrics(reward_accuracies).mean()
        metrics[f"{prefix}rewards/margins"] = self.accelerator.gather_for_metrics(
            chosen_rewards - rejected_rewards
        ).mean()
        metrics[f"{prefix}logps/rejected"] = self.accelerator.gather_for_metrics(policy_rejected_logps).detach().mean()
        metrics[f"{prefix}logps/chosen"] = self.accelerator.gather_for_metrics(policy_chosen_logps).detach().mean()
        metrics[f"{prefix}logits/rejected"] = (
            self.accelerator.gather_for_metrics(policy_rejected_logits).detach().mean()
        )
        metrics[f"{prefix}logits/chosen"] = self.accelerator.gather_for_metrics(policy_chosen_logits).detach().mean()
        metrics[f"{prefix}nll_loss"] = self.accelerator.gather_for_metrics(policy_nll_loss).detach().mean()
        metrics[f"{prefix}log_odds_ratio"] = self.accelerator.gather_for_metrics(log_odds_ratio).mean()
        metrics[f"{prefix}log_odds_chosen"] = self.accelerator.gather_for_metrics(log_odds_chosen).mean()
        if is_torch_xla_available():
            xm.mark_step()  # needed because .item() calls
        for k, v in metrics.items():
            metrics[k] = v.item()
        if self.aux_loss_enabled:
            loss += self.aux_loss_coef * aux_loss

        return loss, metrics

    def compute_loss(
        self,
        model: Union[PreTrainedModel, nn.Module],
        inputs: dict[str, Union[torch.Tensor, Any]],
        return_outputs=False,
        num_items_in_batch=None,
    ) -> Union[torch.Tensor, tuple[torch.Tensor, dict[str, torch.Tensor]]]:
        compute_loss_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()

        with compute_loss_context_manager:
            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="train")

        # Make sure to move the loss to the device the original accumulating loss is at back in the `Trainer` class:
        loss = loss.to(self.args.device)

        # force log the metrics
        self.store_metrics(metrics, train_eval="train")

        if return_outputs:
            return (loss, metrics)
        return loss

    def generate_from_model(self, model, batch: dict[str, torch.LongTensor]) -> str:
        """Generate samples from the model and reference model for the given batch of inputs."""

        # If one uses `generate_during_eval` with peft + bf16, we need to explicitly call generate with
        # the torch cuda amp context manager as some hidden states are silently casted to full precision.
        generate_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()

        with generate_context_manager:
            policy_output = model.generate(
                input_ids=batch["prompt_input_ids"],
                attention_mask=batch["prompt_attention_mask"],
                max_length=self.max_length,
                do_sample=True,
                pad_token_id=self.processing_class.pad_token_id,
            )

        policy_output = pad_to_length(policy_output, self.max_length, self.processing_class.pad_token_id)
        policy_output_decoded = self.processing_class.batch_decode(policy_output, skip_special_tokens=True)

        return policy_output_decoded

    def prediction_step(
        self,
        model: Union[PreTrainedModel, nn.Module],
        inputs: dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[list[str]] = None,
    ):
        if not self.use_dpo_data_collator:
            warnings.warn(
                "prediction_step is only implemented for DPODataCollatorWithPadding, and you passed a datacollator that is different than "
                "DPODataCollatorWithPadding - you might see unexpected behavior. Alternatively, you can implement your own prediction_step method if you are using a custom data collator"
            )
        if ignore_keys is None:
            if hasattr(model, "config"):
                ignore_keys = getattr(model.config, "keys_to_ignore_at_inference", [])
            else:
                ignore_keys = []

        prediction_context_manager = amp.autocast("cuda") if self._peft_has_been_casted_to_bf16 else nullcontext()

        with torch.no_grad(), prediction_context_manager:
            loss, metrics = self.get_batch_loss_metrics(model, inputs, train_eval="eval")

        # force log the metrics
        self.store_metrics(metrics, train_eval="eval")

        if prediction_loss_only:
            return (loss.detach(), None, None)

        # logits for the chosen and rejected samples from model
        logits_dict = {
            "eval_logits/chosen": metrics["eval_logits/chosen"],
            "eval_logits/rejected": metrics["eval_logits/rejected"],
        }
        logits = tuple(v.unsqueeze(dim=0) for k, v in logits_dict.items() if k not in ignore_keys)
        logits = torch.stack(logits).mean(axis=1).to(self.accelerator.device)
        labels = torch.zeros(logits.shape[0], device=self.accelerator.device)

        return (loss.detach(), logits, labels)

    def store_metrics(self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train") -> None:
        for key, value in metrics.items():
            self._stored_metrics[train_eval][key].append(value)

    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[list[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Overriding built-in evaluation loop to store metrics for each batch.
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """

        # Sample and save to game log if requested (for one batch to save time)
        if self.generate_during_eval:
            # Generate random indices within the range of the total number of samples
            num_samples = len(dataloader.dataset)
            random_indices = random.sample(range(num_samples), k=self.args.eval_batch_size)

            # Use dataloader.dataset.select to get the random batch without iterating over the DataLoader
            random_batch_dataset = dataloader.dataset.select(random_indices)
            random_batch = self.data_collator(random_batch_dataset)
            random_batch = self._prepare_inputs(random_batch)

            policy_output_decoded = self.generate_from_model(self.model, random_batch)

            table = pd.DataFrame(
                columns=["Prompt", "Policy"],
                data=[
                    [prompt, pol[len(prompt) :]] for prompt, pol in zip(random_batch["prompt"], policy_output_decoded)
                ],
            )
            if "wandb" in self.args.report_to:
                wandb.log({"game_log": wandb.Table(data=table)})

            if "comet_ml" in self.args.report_to:
                log_table_to_comet_experiment(
                    name="game_log.csv",
                    table=table,
                )

        # Base evaluation
        initial_output = super().evaluation_loop(
            dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix
        )

        return initial_output

    def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None:
        """
        Log `logs` on the various objects watching training, including stored metrics.

        Args:
            logs (`dict[str, float]`):
                The values to log.
            start_time (`float` or `None`, *optional*, defaults to `None`):
                Start time of the training.
        """
        # logs either has 'loss' or 'eval_loss'
        train_eval = "train" if "loss" in logs else "eval"
        # Add averaged stored metrics to logs
        for key, metrics in self._stored_metrics[train_eval].items():
            logs[key] = torch.tensor(metrics).mean().item()
        del self._stored_metrics[train_eval]

        if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
            return super().log(logs, start_time)
        else:  # transformers<=4.46
            return super().log(logs)

    def _shift_right(self, input_ids):
        if self.decoder_start_token_id is None:
            raise ValueError(
                "model.config.decoder_start_token_id has to be defined. It is usually set to the pad_token_id."
            )

        # shift inputs to the right
        if is_torch_fx_proxy(input_ids):
            # Item assignment is not supported natively for proxies.
            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), self.decoder_start_token_id)
            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
        else:
            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
            shifted_input_ids[..., 0] = self.decoder_start_token_id

        if self.pad_token_id is None:
            raise ValueError("model.config.pad_token_id has to be defined.")
        # replace possible -100 values in labels by `pad_token_id`
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, self.pad_token_id)

        return shifted_input_ids

    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

        tags = tags or []
        if isinstance(tags, str):
            tags = [tags]

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

        citation = textwrap.dedent("""\
        @article{hong2024orpo,
            title        = {{ORPO: Monolithic Preference Optimization without Reference Model}},
            author       = {Jiwoo Hong and Noah Lee and James Thorne},
            year         = 2024,
            eprint       = {arXiv:2403.07691}
        }""")

        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.get_url() if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="ORPO",
            trainer_citation=citation,
            paper_title="ORPO: Monolithic Preference Optimization without Reference Model",
            paper_id="2403.07691",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothORPOTrainer(_UnslothORPOTrainer):
    """
    
    Initialize ORPOTrainer.

    Args:
        model (`transformers.PreTrainedModel`):
            The model to train, preferably an `AutoModelForSequenceClassification`.
        args (`ORPOConfig`):
            The ORPO config arguments to use for training.
        data_collator (`transformers.DataCollator`):
            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
        train_dataset (`datasets.Dataset`):
            The dataset to use for training.
        eval_dataset (`datasets.Dataset`):
            The dataset to use for evaluation.
        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
            Processing class used to process the data. If provided, will be used to automatically process the inputs
            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
            reuse the fine-tuned model.
        model_init (`Callable[[], transformers.PreTrainedModel]`):
            The model initializer to use for training. If None is specified, the default model initializer will be used.
        callbacks (`list[transformers.TrainerCallback]`):
            The callbacks to use for training.
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.
        peft_config (`dict`, defaults to `None`):
            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function to use to compute the metrics. Must take a `EvalPrediction` and return
            a dictionary string to metric values.
    
    """
    def __init__(
        self,
        model = None,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        model_init = None,
        callbacks = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        compute_metrics = None,
        **kwargs
    ):
        if args is None: args = UnslothORPOConfig()
        use_bf16 = getattr(args, 'bf16', False)
        use_fp16 = getattr(args, 'fp16', False)
        dtype = 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 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 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 not use_bf16 and not use_fp16:
            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)
        bf16_full_eval = getattr(args, '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 not bf16_full_eval and not fp16_full_eval: args.bf16_full_eval = args.bf16; args.fp16_full_eval = args.fp16
        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
            pass
        else:
            model_max_seq_length = getattr(model, 'max_seq_length', None)
            args_max_seq_length  = getattr(args,  'max_seq_length', None)
            if args_max_seq_length is None and model_max_seq_length is not None:
                max_seq_length = model.max_seq_length
                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
        if model is not None and hasattr(model, 'for_training'):
            model.for_training()
        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
        if 'processing_class' in locals():
            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('orpo_trainer', other_metrics)
        
        super().__init__(
            model = model,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            model_init = model_init,
            callbacks = callbacks,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            peft_config = peft_config,
            compute_metrics = compute_metrics,**kwargs)
        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
        
pass