unsloth_deepseek/src/unsloth_compiled_cache/UnslothNashMDTrainer.py

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from trl.trainer.nash_md_trainer import (Any, BaseImageProcessor, BasePairwiseJudge, Callable, Dataset, EvalPrediction, F, FeatureExtractionMixin, GeometricMixtureWrapper, IterableDataset, NashMDConfig, NashMDTrainer, OnlineDPOTrainer, OptimizerNames, Optional, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SIMPLE_CHAT_TEMPLATE, TrainerCallback, Union, empty_cache, generate_model_card, get_comet_experiment_url, get_reward, is_conversational, is_wandb_available, jinja2, maybe_apply_chat_template, nn, os, textwrap, torch, truncate_right, 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 UnslothNashMDConfig(NashMDConfig):
    """
    
    Configuration class for the [`NashMDTrainer`].

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

    Parameters:
        mixture_coef (`float` or `list[float]`, *optional*, defaults to `0.5`):
            Logit mixture coefficient for the model and reference model. If a list of floats is provided then the
            mixture coefficient is selected for each new epoch and the last coefficient is used for the rest of the
            epochs.
    
    """
    vllm_sampling_params: Optional[Any] = field(
        default = None,
        metadata = {'help': 'vLLM SamplingParams'},
    )
    unsloth_num_chunks : Optional[int] = field(
        default = -1,
        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
    )
    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,
        reward_model_path = None,
        judge = None,
        max_new_tokens = 64,
        max_length = 512,
        temperature = 0.9,
        missing_eos_penalty = None,
        loss_type = 'sigmoid',
        dataset_num_proc = None,
        disable_dropout = True,
        use_vllm = False,
        ds3_gather_for_generation = True,
        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,
            reward_model_path = reward_model_path,
            judge = judge,
            max_new_tokens = max_new_tokens,
            max_length = max_length,
            temperature = temperature,
            missing_eos_penalty = missing_eos_penalty,
            loss_type = loss_type,
            dataset_num_proc = dataset_num_proc,
            disable_dropout = disable_dropout,
            use_vllm = use_vllm,
            ds3_gather_for_generation = ds3_gather_for_generation,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
pass

class _UnslothNashMDTrainer(OnlineDPOTrainer):
    r""""""

    _tag_names = ["trl", "nash-md"]

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module] = None,
        ref_model: Union[PreTrainedModel, nn.Module] = None,
        reward_model: Union[PreTrainedModel, nn.Module, None] = None,
        judge: Optional[BasePairwiseJudge] = None,
        args: Optional[NashMDConfig] = None,
        data_collator: Optional[Callable] = None,
        train_dataset: Optional[Union[Dataset, IterableDataset]] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        peft_config: Optional[dict] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
    ) -> None:
        super().__init__(
            model=model,
            ref_model=ref_model,
            reward_model=reward_model,
            judge=judge,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            reward_processing_class=processing_class,  # for now, NashMDTrainer can't use any reward model
            peft_config=peft_config,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        self._mixture_coef = self.args.mixture_coef

        # Overwrite the stats dictionary to include NashMD specific statistics
        self.stats = {
            # Remove "non_score_reward", "rlhf_reward", "scores_margin"
            # Add "mixture_coef"
            "loss/kl": [],
            "objective/entropy": [],
            "loss/score": [],
            "rewards/probabilities": [],
            "rewards/accuracies": [],
            "rewards/margins": [],
            "logps/chosen": [],
            "logps/rejected": [],
            "val/model_contain_eos_token": [],
            "val/ref_contain_eos_token": [],
            "beta": [],
            "mixture_coef": [],
        }
        if self.reward_model is not None:
            self.stats["rewards/chosen"] = []
            self.stats["rewards/rejected"] = []

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

    def _generate_completions(self, model, prompts):
        with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
            model_output = unwrapped_model.generate(
                input_ids=prompts["input_ids"],
                attention_mask=prompts["attention_mask"],
                generation_config=self.generation_config,
            )

            ref_model = model if self.ref_model is None else self.ref_model
            with torch.no_grad(), unwrap_model_for_generation(ref_model, self.accelerator) as unwrapped_ref_model:
                mixture_model = GeometricMixtureWrapper(
                    model=unwrapped_model,
                    ref_model=unwrapped_ref_model,
                    generation_config=self.generation_config,
                    mixture_coef=self.mixture_coef,
                    device=self.accelerator.device,
                )

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

        return model_output, mixture_output

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

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

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

        return model_data, mixture_data

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

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

        return model_scores, mixture_scores

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

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

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

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

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

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

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

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

        return (model_logprobs_model_data, ref_logprobs_model_data)

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

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

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

        return loss.mean(), score, kl_div_log

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        if self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            self.accelerator.backward(loss, **kwargs)

        return loss.detach() / self.args.gradient_accumulation_steps

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

        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="Nash-MD",
            trainer_citation=citation,
            paper_title="Nash Learning from Human Feedback",
            paper_id="2312.00886",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothNashMDTrainer(_UnslothNashMDTrainer):
    """
    
    Initialize NashMDTrainer as a subclass of [`OnlineDPOConfig`].

    Args:
        model (`transformers.PreTrainedModel`):
            The model to train, preferably an `AutoModelForCausalLM`.
        ref_model (`PreTrainedModelWrapper`):
            Hugging Face transformer model with a casual language modelling head. Used for implicit reward computation and loss. If no
            reference model is provided, the trainer will create a reference model with the same architecture as the model to be optimized.
        reward_model (`transformers.PreTrainedModel`):
            The reward model to score completions with, preferably an `AutoModelForSequenceClassification`.
        judge (`BasePairwiseJudge`):
            The judge to use for pairwise comparison of model completions.
        args (`NashMDConfig`):
            The NashMD config arguments to use for training.
        data_collator (`transformers.DataCollator`):
            The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used
            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
        train_dataset (`datasets.Dataset`):
            The dataset to use for training.
        eval_dataset (`datasets.Dataset`):
            The dataset to use for evaluation.
        processing_class (`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.
        peft_config (`dict`):
            The peft config to use for training.
        compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*):
            The function to use to compute the metrics. Must take a `EvalPrediction` and return
            a dictionary string to metric values.
        callbacks (`list[transformers.TrainerCallback]`):
            The callbacks to use for training.
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.
    
    """
    def __init__(
        self,
        model = None,
        ref_model = None,
        reward_model = None,
        judge = None,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        peft_config = None,
        compute_metrics = None,
        callbacks = None,
        preprocess_logits_for_metrics = None,
        **kwargs
    ):
        if args is None: args = UnslothNashMDConfig()
        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('nash_md_trainer', other_metrics)
        
        super().__init__(
            model = model,
            ref_model = ref_model,
            reward_model = reward_model,
            judge = judge,
            args = args,
            data_collator = data_collator,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            peft_config = peft_config,
            compute_metrics = compute_metrics,
            callbacks = callbacks,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,**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