unsloth_deepseek/src/unsloth_compiled_cache/UnslothGKDTrainer.py

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from trl.trainer.gkd_trainer import (Any, AutoModelForCausalLM, BaseImageProcessor, Callable, DataCollator, DataCollatorForChatML, Dataset, EvalPrediction, F, FeatureExtractionMixin, GKDConfig, GKDTrainer, GenerationConfig, Optional, PeftConfig, PreTrainedModel, PreTrainedModelWrapper, PreTrainedTokenizerBase, ProcessorMixin, SFTTrainer, TrainerCallback, Union, deepcopy, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, is_wandb_available, nn, os, random, textwrap, torch, unwrap_model_for_generation)


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

    Args:
        temperature (`float`, *optional*, defaults to `0.9`):
            Temperature for sampling. The higher the temperature, the more random the completions.
        lmbda (`float`, *optional*, defaults to `0.5`):
            Lambda parameter that controls the student data fraction (i.e., the proportion of on-policy
            student-generated outputs).
        beta (`float`, *optional*, defaults to `0.5`):
            Interpolation coefficient between `0.0` and `1.0` of the Generalized Jensen-Shannon Divergence loss. When
            beta is `0.0`, the loss is the KL divergence. When beta is `1.0`, the loss is the Inverse KL Divergence.
        max_new_tokens (`int`, *optional*, defaults to `128`):
            Maximum number of tokens to generate per completion.
        teacher_model_name_or_path (`str` or `None`, *optional*, defaults to `None`):
            Model name or path of the teacher model. If `None`, the teacher model will be the same as the model
            being trained.
        teacher_model_init_kwargs (`dict[str, Any]]` or `None`, *optional*, defaults to `None`):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the teacher model
            from a string.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model.
        seq_kd (`bool`, *optional*, defaults to `False`):
            Seq_kd parameter that controls whether to perform Sequence-Level KD (can be viewed as supervised FT
            on teacher-generated output).
    
    """
    vllm_sampling_params: Optional[Any] = field(
        default = None,
        metadata = {'help': 'vLLM SamplingParams'},
    )
    unsloth_num_chunks : Optional[int] = field(
        default = -1,
        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
    )
    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,
        model_init_kwargs = None,
        use_liger = False,
        dataset_text_field = 'text',
        dataset_kwargs = None,
        dataset_num_proc = None,
        max_seq_length = 1024,
        packing = False,
        eval_packing = None,
        dataset_batch_size = None,
        num_of_sequences = None,
        chars_per_token = None,
        temperature = 0.9,
        lmbda = 0.5,
        beta = 0.5,
        max_new_tokens = 128,
        teacher_model_name_or_path = None,
        teacher_model_init_kwargs = None,
        disable_dropout = True,
        seq_kd = False,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        **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,
            model_init_kwargs = model_init_kwargs,
            use_liger = use_liger,
            dataset_text_field = dataset_text_field,
            dataset_kwargs = dataset_kwargs,
            dataset_num_proc = dataset_num_proc,
            max_seq_length = max_seq_length,
            packing = packing,
            eval_packing = eval_packing,
            dataset_batch_size = dataset_batch_size,
            num_of_sequences = num_of_sequences,
            chars_per_token = chars_per_token,
            temperature = temperature,
            lmbda = lmbda,
            beta = beta,
            max_new_tokens = max_new_tokens,
            teacher_model_name_or_path = teacher_model_name_or_path,
            teacher_model_init_kwargs = teacher_model_init_kwargs,
            disable_dropout = disable_dropout,
            seq_kd = seq_kd,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
pass

class _UnslothGKDTrainer(SFTTrainer):
    _tag_names = ["trl", "gkd"]

    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
        teacher_model: Union[PreTrainedModel, nn.Module, str] = None,
        args: Optional[GKDConfig] = None,
        data_collator: Optional[DataCollator] = None,  # type: ignore
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional["PeftConfig"] = None,
        formatting_func: Optional[Callable] = None,
    ):
        # add remove_unused_columns=False to the dataclass args
        args.remove_unused_columns = False
        data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_seq_length)

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

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

        if isinstance(teacher_model, str):
            if args.use_liger:
                teacher_model = AutoLigerKernelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs)
            else:
                teacher_model = AutoModelForCausalLM.from_pretrained(teacher_model, **teacher_model_init_kwargs)

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

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

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

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

    def _prepare_dataset(self, dataset, *args):
        # SFTTrainer._prepare_dataset() applies the chat template and rename the messages column to text. However, we
        # need to keep the messages column as it is. We use the following workaround to keep the messages column.
        dataset = dataset.add_column("_messages", dataset["messages"])
        dataset = super()._prepare_dataset(dataset, *args)
        dataset = dataset.rename_column("_messages", "messages")
        return dataset

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

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

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

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

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

        # Compute the log of the mixture distribution
        # log(a + b) = log(exp(log(a)) + exp(log(b))) -> for mixture
        beta = torch.tensor(beta, dtype=student_log_probs.dtype)
        mixture_log_probs = torch.logsumexp(
            torch.stack([student_log_probs + torch.log(beta), teacher_log_probs + torch.log(1 - beta)]),
            dim=0,
        )

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

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

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

        # Apply reduction
        if reduction == "batchmean":
            return jsd.sum() / mask.sum() if labels is not None else jsd.sum() / (jsd.size(0) * jsd.size(1))
        elif reduction == "sum":
            return jsd.sum()
        elif reduction == "mean":
            return jsd.mean()
        else:
            return jsd

    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
        # compute student output
        outputs_student = model(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
        )

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

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

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

        # empty cache
        empty_cache()

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

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

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

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

        return generated_tokens, new_attention_mask, new_labels

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

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

        loss = super().training_step(model, inputs, num_items_in_batch)
        return loss

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

        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="GKD",
            trainer_citation=citation,
            paper_title="On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes",
            paper_id="2306.13649",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothGKDTrainer(_UnslothGKDTrainer):
    """
    
    """
    def __init__(
        self,
        model = None,
        teacher_model = None,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        compute_metrics = None,
        callbacks = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        formatting_func = None,
        **kwargs
    ):
        if args is None: args = UnslothGKDConfig()
        use_bf16 = getattr(args, 'bf16', False)
        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('gkd_trainer', other_metrics)
        
        super().__init__(
            model = model,
            teacher_model = teacher_model,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            compute_metrics = compute_metrics,
            callbacks = callbacks,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            peft_config = peft_config,
            formatting_func = formatting_func,**kwargs)
        if 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