unsloth_compiled_cache/UnslothDDPOTrainer.py

"""
2025.3.12
2025.3.14
4.48.3
0.15.2
__UNSLOTH_VERSIONING__
"""
from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from trl.trainer.ddpo_trainer import (Accelerator, Any, Callable, DDPOConfig, DDPOStableDiffusionPipeline, DDPOTrainer, Optional, PerPromptStatTracker, ProjectConfiguration, PyTorchModelHubMixin, Union, defaultdict, futures, generate_model_card, get_comet_experiment_url, is_wandb_available, logger, os, set_seed, textwrap, torch, wandb, warn)


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

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 UnslothDDPOConfig(DDPOConfig):
    """
    
    Configuration class for the [`DDPOTrainer`].

    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:
        exp_name (`str`, *optional*, defaults to `os.path.basename(sys.argv[0])[: -len(".py")]`):
            Name of this experiment (by default is the file name without the extension name).
        run_name (`str`, *optional*, defaults to `""`):
            Name of this run.
        seed (`int`, *optional*, defaults to `0`):
            Random seed.
        log_with (`Literal["wandb", "tensorboard"]]` or `None`, *optional*, defaults to `None`):
            Log with either 'wandb' or 'tensorboard', check
            https://huggingface.co/docs/accelerate/usage_guides/tracking for more details.
        tracker_kwargs (`Dict`, *optional*, defaults to `{}`):
            Keyword arguments for the tracker (e.g. wandb_project).
        accelerator_kwargs (`Dict`, *optional*, defaults to `{}`):
            Keyword arguments for the accelerator.
        project_kwargs (`Dict`, *optional*, defaults to `{}`):
            Keyword arguments for the accelerator project config (e.g. `logging_dir`).
        tracker_project_name (`str`, *optional*, defaults to `"trl"`):
            Name of project to use for tracking.
        logdir (`str`, *optional*, defaults to `"logs"`):
            Top-level logging directory for checkpoint saving.
        num_epochs (`int`, *optional*, defaults to `100`):
            Number of epochs to train.
        save_freq (`int`, *optional*, defaults to `1`):
            Number of epochs between saving model checkpoints.
        num_checkpoint_limit (`int`, *optional*, defaults to `5`):
            Number of checkpoints to keep before overwriting old ones.
        mixed_precision (`str`, *optional*, defaults to `"fp16"`):
            Mixed precision training.
        allow_tf32 (`bool`, *optional*, defaults to `True`):
            Allow `tf32` on Ampere GPUs.
        resume_from (`str`, *optional*, defaults to `""`):
            Resume training from a checkpoint.
        sample_num_steps (`int`, *optional*, defaults to `50`):
            Number of sampler inference steps.
        sample_eta (`float`, *optional*, defaults to `1.0`):
            Eta parameter for the DDIM sampler.
        sample_guidance_scale (`float`, *optional*, defaults to `5.0`):
            Classifier-free guidance weight.
        sample_batch_size (`int`, *optional*, defaults to `1`):
            Batch size (per GPU) to use for sampling.
        sample_num_batches_per_epoch (`int`, *optional*, defaults to `2`):
            Number of batches to sample per epoch.
        train_batch_size (`int`, *optional*, defaults to `1`):
            Batch size (per GPU) to use for training.
        train_use_8bit_adam (`bool`, *optional*, defaults to `False`):
            Use 8bit Adam optimizer from bitsandbytes.
        train_learning_rate (`float`, *optional*, defaults to `3e-4`):
            Learning rate.
        train_adam_beta1 (`float`, *optional*, defaults to `0.9`):
            Adam beta1.
        train_adam_beta2 (`float`, *optional*, defaults to `0.999`):
            Adam beta2.
        train_adam_weight_decay (`float`, *optional*, defaults to `1e-4`):
            Adam weight decay.
        train_adam_epsilon (`float`, *optional*, defaults to `1e-8`):
            Adam epsilon.
        train_gradient_accumulation_steps (`int`, *optional*, defaults to `1`):
            Number of gradient accumulation steps.
        train_max_grad_norm (`float`, *optional*, defaults to `1.0`):
            Maximum gradient norm for gradient clipping.
        train_num_inner_epochs (`int`, *optional*, defaults to `1`):
            Number of inner epochs per outer epoch.
        train_cfg (`bool`, *optional*, defaults to `True`):
            Whether to use classifier-free guidance during training.
        train_adv_clip_max (`float`, *optional*, defaults to `5.0`):
            Clip advantages to the range.
        train_clip_range (`float`, *optional*, defaults to `1e-4`):
            PPO clip range.
        train_timestep_fraction (`float`, *optional*, defaults to `1.0`):
            Fraction of timesteps to train on.
        per_prompt_stat_tracking (`bool`, *optional*, defaults to `False`):
            Whether to track statistics for each prompt separately.
        per_prompt_stat_tracking_buffer_size (`int`, *optional*, defaults to `16`):
            Number of reward values to store in the buffer for each prompt.
        per_prompt_stat_tracking_min_count (`int`, *optional*, defaults to `16`):
            Minimum number of reward values to store in the buffer.
        async_reward_computation (`bool`, *optional*, defaults to `False`):
            Whether to compute rewards asynchronously.
        max_workers (`int`, *optional*, defaults to `2`):
            Maximum number of workers to use for async reward computation.
        negative_prompts (`str`, *optional*, defaults to `""`):
            Comma-separated list of prompts to use as negative examples.
        push_to_hub (`bool`, *optional*, defaults to `False`):
            Whether to push the final model checkpoint to the Hub.
    
    """
    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,
        exp_name = 'colab_kernel_launcher',
        run_name = '',
        seed = 3407,
        log_with = None,
        tracker_project_name = 'trl',
        logdir = 'logs',
        num_epochs = 100,
        save_freq = 1,
        num_checkpoint_limit = 5,
        mixed_precision = 'fp16',
        allow_tf32 = True,
        resume_from = '',
        sample_num_steps = 50,
        sample_eta = 1.0,
        sample_guidance_scale = 5.0,
        sample_batch_size = 1,
        sample_num_batches_per_epoch = 2,
        train_batch_size = 1,
        train_use_8bit_adam = False,
        train_learning_rate = 5e-05,
        train_adam_beta1 = 0.9,
        train_adam_beta2 = 0.999,
        train_adam_weight_decay = 0.01,
        train_adam_epsilon = 1e-08,
        train_gradient_accumulation_steps = 2,
        train_max_grad_norm = 1.0,
        train_num_inner_epochs = 1,
        train_cfg = True,
        train_adv_clip_max = 5.0,
        train_clip_range = 0.0001,
        train_timestep_fraction = 1.0,
        per_prompt_stat_tracking = False,
        per_prompt_stat_tracking_buffer_size = 16,
        per_prompt_stat_tracking_min_count = 16,
        async_reward_computation = False,
        max_workers = 2,
        negative_prompts = '',
        push_to_hub = False,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        **kwargs,
    ):
        
        super().__init__(
            exp_name = exp_name,
            run_name = run_name,
            seed = seed,
            log_with = log_with,
            tracker_project_name = tracker_project_name,
            logdir = logdir,
            num_epochs = num_epochs,
            save_freq = save_freq,
            num_checkpoint_limit = num_checkpoint_limit,
            mixed_precision = mixed_precision,
            allow_tf32 = allow_tf32,
            resume_from = resume_from,
            sample_num_steps = sample_num_steps,
            sample_eta = sample_eta,
            sample_guidance_scale = sample_guidance_scale,
            sample_batch_size = sample_batch_size,
            sample_num_batches_per_epoch = sample_num_batches_per_epoch,
            train_batch_size = train_batch_size,
            train_use_8bit_adam = train_use_8bit_adam,
            train_learning_rate = train_learning_rate,
            train_adam_beta1 = train_adam_beta1,
            train_adam_beta2 = train_adam_beta2,
            train_adam_weight_decay = train_adam_weight_decay,
            train_adam_epsilon = train_adam_epsilon,
            train_gradient_accumulation_steps = train_gradient_accumulation_steps,
            train_max_grad_norm = train_max_grad_norm,
            train_num_inner_epochs = train_num_inner_epochs,
            train_cfg = train_cfg,
            train_adv_clip_max = train_adv_clip_max,
            train_clip_range = train_clip_range,
            train_timestep_fraction = train_timestep_fraction,
            per_prompt_stat_tracking = per_prompt_stat_tracking,
            per_prompt_stat_tracking_buffer_size = per_prompt_stat_tracking_buffer_size,
            per_prompt_stat_tracking_min_count = per_prompt_stat_tracking_min_count,
            async_reward_computation = async_reward_computation,
            max_workers = max_workers,
            negative_prompts = negative_prompts,
            push_to_hub = push_to_hub,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
pass

class _UnslothDDPOTrainer(PyTorchModelHubMixin):
    """"""

    _tag_names = ["trl", "ddpo"]

    def __init__(
        self,
        config: DDPOConfig,
        reward_function: Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor],
        prompt_function: Callable[[], tuple[str, Any]],
        sd_pipeline: DDPOStableDiffusionPipeline,
        image_samples_hook: Optional[Callable[[Any, Any, Any], Any]] = None,
    ):
        if image_samples_hook is None:
            warn("No image_samples_hook provided; no images will be logged")

        self.prompt_fn = prompt_function
        self.reward_fn = reward_function
        self.config = config
        self.image_samples_callback = image_samples_hook

        accelerator_project_config = ProjectConfiguration(**self.config.project_kwargs)

        if self.config.resume_from:
            self.config.resume_from = os.path.normpath(os.path.expanduser(self.config.resume_from))
            if "checkpoint_" not in os.path.basename(self.config.resume_from):
                # get the most recent checkpoint in this directory
                checkpoints = list(
                    filter(
                        lambda x: "checkpoint_" in x,
                        os.listdir(self.config.resume_from),
                    )
                )
                if len(checkpoints) == 0:
                    raise ValueError(f"No checkpoints found in {self.config.resume_from}")
                checkpoint_numbers = sorted([int(x.split("_")[-1]) for x in checkpoints])
                self.config.resume_from = os.path.join(
                    self.config.resume_from,
                    f"checkpoint_{checkpoint_numbers[-1]}",
                )

                accelerator_project_config.iteration = checkpoint_numbers[-1] + 1

        # number of timesteps within each trajectory to train on
        self.num_train_timesteps = int(self.config.sample_num_steps * self.config.train_timestep_fraction)

        self.accelerator = Accelerator(
            log_with=self.config.log_with,
            mixed_precision=self.config.mixed_precision,
            project_config=accelerator_project_config,
            # we always accumulate gradients across timesteps; we want config.train.gradient_accumulation_steps to be the
            # number of *samples* we accumulate across, so we need to multiply by the number of training timesteps to get
            # the total number of optimizer steps to accumulate across.
            gradient_accumulation_steps=self.config.train_gradient_accumulation_steps * self.num_train_timesteps,
            **self.config.accelerator_kwargs,
        )

        is_okay, message = self._config_check()
        if not is_okay:
            raise ValueError(message)

        is_using_tensorboard = config.log_with is not None and config.log_with == "tensorboard"

        if self.accelerator.is_main_process:
            self.accelerator.init_trackers(
                self.config.tracker_project_name,
                config=dict(ddpo_trainer_config=config.to_dict()) if not is_using_tensorboard else config.to_dict(),
                init_kwargs=self.config.tracker_kwargs,
            )

        logger.info(f"\n{config}")

        set_seed(self.config.seed, device_specific=True)

        self.sd_pipeline = sd_pipeline

        self.sd_pipeline.set_progress_bar_config(
            position=1,
            disable=not self.accelerator.is_local_main_process,
            leave=False,
            desc="Timestep",
            dynamic_ncols=True,
        )

        # For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
        # as these weights are only used for inference, keeping weights in full precision is not required.
        if self.accelerator.mixed_precision == "fp16":
            inference_dtype = torch.float16
        elif self.accelerator.mixed_precision == "bf16":
            inference_dtype = torch.bfloat16
        else:
            inference_dtype = torch.float32

        self.sd_pipeline.vae.to(self.accelerator.device, dtype=inference_dtype)
        self.sd_pipeline.text_encoder.to(self.accelerator.device, dtype=inference_dtype)
        self.sd_pipeline.unet.to(self.accelerator.device, dtype=inference_dtype)

        trainable_layers = self.sd_pipeline.get_trainable_layers()

        self.accelerator.register_save_state_pre_hook(self._save_model_hook)
        self.accelerator.register_load_state_pre_hook(self._load_model_hook)

        # Enable TF32 for faster training on Ampere GPUs,
        # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
        if self.config.allow_tf32:
            torch.backends.cuda.matmul.allow_tf32 = True

        self.optimizer = self._setup_optimizer(
            trainable_layers.parameters() if not isinstance(trainable_layers, list) else trainable_layers
        )

        self.neg_prompt_embed = self.sd_pipeline.text_encoder(
            self.sd_pipeline.tokenizer(
                [""] if self.config.negative_prompts is None else self.config.negative_prompts,
                return_tensors="pt",
                padding="max_length",
                truncation=True,
                max_length=self.sd_pipeline.tokenizer.model_max_length,
            ).input_ids.to(self.accelerator.device)
        )[0]

        if config.per_prompt_stat_tracking:
            self.stat_tracker = PerPromptStatTracker(
                config.per_prompt_stat_tracking_buffer_size,
                config.per_prompt_stat_tracking_min_count,
            )

        # NOTE: for some reason, autocast is necessary for non-lora training but for lora training it isn't necessary and it uses
        # more memory
        self.autocast = self.sd_pipeline.autocast or self.accelerator.autocast

        if hasattr(self.sd_pipeline, "use_lora") and self.sd_pipeline.use_lora:
            unet, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer)
            self.trainable_layers = list(filter(lambda p: p.requires_grad, unet.parameters()))
        else:
            self.trainable_layers, self.optimizer = self.accelerator.prepare(trainable_layers, self.optimizer)

        if self.config.async_reward_computation:
            self.executor = futures.ThreadPoolExecutor(max_workers=config.max_workers)

        if config.resume_from:
            logger.info(f"Resuming from {config.resume_from}")
            self.accelerator.load_state(config.resume_from)
            self.first_epoch = int(config.resume_from.split("_")[-1]) + 1
        else:
            self.first_epoch = 0

    def compute_rewards(self, prompt_image_pairs, is_async=False):
        if not is_async:
            rewards = []
            for images, prompts, prompt_metadata in prompt_image_pairs:
                reward, reward_metadata = self.reward_fn(images, prompts, prompt_metadata)
                rewards.append(
                    (
                        torch.as_tensor(reward, device=self.accelerator.device),
                        reward_metadata,
                    )
                )
        else:
            rewards = self.executor.map(lambda x: self.reward_fn(*x), prompt_image_pairs)
            rewards = [
                (torch.as_tensor(reward.result(), device=self.accelerator.device), reward_metadata.result())
                for reward, reward_metadata in rewards
            ]

        return zip(*rewards)

    def step(self, epoch: int, global_step: int):
        """
        Perform a single step of training.

        Args:
            epoch (int): The current epoch.
            global_step (int): The current global step.

        Side Effects:
            - Model weights are updated
            - Logs the statistics to the accelerator trackers.
            - If `self.image_samples_callback` is not None, it will be called with the prompt_image_pairs, global_step, and the accelerator tracker.

        Returns:
            global_step (int): The updated global step.

        """
        samples, prompt_image_data = self._generate_samples(
            iterations=self.config.sample_num_batches_per_epoch,
            batch_size=self.config.sample_batch_size,
        )

        # collate samples into dict where each entry has shape (num_batches_per_epoch * sample.batch_size, ...)
        samples = {k: torch.cat([s[k] for s in samples]) for k in samples[0].keys()}
        rewards, rewards_metadata = self.compute_rewards(
            prompt_image_data, is_async=self.config.async_reward_computation
        )

        for i, image_data in enumerate(prompt_image_data):
            image_data.extend([rewards[i], rewards_metadata[i]])

        if self.image_samples_callback is not None:
            self.image_samples_callback(prompt_image_data, global_step, self.accelerator.trackers[0])

        rewards = torch.cat(rewards)
        rewards = self.accelerator.gather(rewards).cpu().numpy()

        self.accelerator.log(
            {
                "reward": rewards,
                "epoch": epoch,
                "reward_mean": rewards.mean(),
                "reward_std": rewards.std(),
            },
            step=global_step,
        )

        if self.config.per_prompt_stat_tracking:
            # gather the prompts across processes
            prompt_ids = self.accelerator.gather(samples["prompt_ids"]).cpu().numpy()
            prompts = self.sd_pipeline.tokenizer.batch_decode(prompt_ids, skip_special_tokens=True)
            advantages = self.stat_tracker.update(prompts, rewards)
        else:
            advantages = (rewards - rewards.mean()) / (rewards.std() + 1e-8)

        # ungather advantages;  keep the entries corresponding to the samples on this process
        samples["advantages"] = (
            torch.as_tensor(advantages)
            .reshape(self.accelerator.num_processes, -1)[self.accelerator.process_index]
            .to(self.accelerator.device)
        )

        del samples["prompt_ids"]

        total_batch_size, num_timesteps = samples["timesteps"].shape

        for inner_epoch in range(self.config.train_num_inner_epochs):
            # shuffle samples along batch dimension
            perm = torch.randperm(total_batch_size, device=self.accelerator.device)
            samples = {k: v[perm] for k, v in samples.items()}

            # shuffle along time dimension independently for each sample
            # still trying to understand the code below
            perms = torch.stack(
                [torch.randperm(num_timesteps, device=self.accelerator.device) for _ in range(total_batch_size)]
            )

            for key in ["timesteps", "latents", "next_latents", "log_probs"]:
                samples[key] = samples[key][
                    torch.arange(total_batch_size, device=self.accelerator.device)[:, None],
                    perms,
                ]

            original_keys = samples.keys()
            original_values = samples.values()
            # rebatch them as user defined train_batch_size is different from sample_batch_size
            reshaped_values = [v.reshape(-1, self.config.train_batch_size, *v.shape[1:]) for v in original_values]

            # Transpose the list of original values
            transposed_values = zip(*reshaped_values)
            # Create new dictionaries for each row of transposed values
            samples_batched = [dict(zip(original_keys, row_values)) for row_values in transposed_values]

            self.sd_pipeline.unet.train()
            global_step = self._train_batched_samples(inner_epoch, epoch, global_step, samples_batched)
            # ensure optimization step at the end of the inner epoch
            if not self.accelerator.sync_gradients:
                raise ValueError(
                    "Optimization step should have been performed by this point. Please check calculated gradient accumulation settings."
                )

        if epoch != 0 and epoch % self.config.save_freq == 0 and self.accelerator.is_main_process:
            self.accelerator.save_state()

        return global_step

    def calculate_loss(self, latents, timesteps, next_latents, log_probs, advantages, embeds):
        """
        Calculate the loss for a batch of an unpacked sample

        Args:
            latents (torch.Tensor):
                The latents sampled from the diffusion model, shape: [batch_size, num_channels_latents, height, width]
            timesteps (torch.Tensor):
                The timesteps sampled from the diffusion model, shape: [batch_size]
            next_latents (torch.Tensor):
                The next latents sampled from the diffusion model, shape: [batch_size, num_channels_latents, height, width]
            log_probs (torch.Tensor):
                The log probabilities of the latents, shape: [batch_size]
            advantages (torch.Tensor):
                The advantages of the latents, shape: [batch_size]
            embeds (torch.Tensor):
                The embeddings of the prompts, shape: [2*batch_size or batch_size, ...]
                Note: the "or" is because if train_cfg is True, the expectation is that negative prompts are concatenated to the embeds

        Returns:
            loss (torch.Tensor), approx_kl (torch.Tensor), clipfrac (torch.Tensor)
            (all of these are of shape (1,))
        """
        with self.autocast():
            if self.config.train_cfg:
                noise_pred = self.sd_pipeline.unet(
                    torch.cat([latents] * 2),
                    torch.cat([timesteps] * 2),
                    embeds,
                ).sample
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + self.config.sample_guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )
            else:
                noise_pred = self.sd_pipeline.unet(
                    latents,
                    timesteps,
                    embeds,
                ).sample
            # compute the log prob of next_latents given latents under the current model

            scheduler_step_output = self.sd_pipeline.scheduler_step(
                noise_pred,
                timesteps,
                latents,
                eta=self.config.sample_eta,
                prev_sample=next_latents,
            )

            log_prob = scheduler_step_output.log_probs

        advantages = torch.clamp(
            advantages,
            -self.config.train_adv_clip_max,
            self.config.train_adv_clip_max,
        )

        ratio = torch.exp(log_prob - log_probs)

        loss = self.loss(advantages, self.config.train_clip_range, ratio)

        approx_kl = 0.5 * torch.mean((log_prob - log_probs) ** 2)

        clipfrac = torch.mean((torch.abs(ratio - 1.0) > self.config.train_clip_range).float())

        return loss, approx_kl, clipfrac

    def loss(
        self,
        advantages: torch.Tensor,
        clip_range: float,
        ratio: torch.Tensor,
    ):
        unclipped_loss = -advantages * ratio
        clipped_loss = -advantages * torch.clamp(
            ratio,
            1.0 - clip_range,
            1.0 + clip_range,
        )
        return torch.mean(torch.maximum(unclipped_loss, clipped_loss))

    def _setup_optimizer(self, trainable_layers_parameters):
        if self.config.train_use_8bit_adam:
            import bitsandbytes

            optimizer_cls = bitsandbytes.optim.AdamW8bit
        else:
            optimizer_cls = torch.optim.AdamW

        return optimizer_cls(
            trainable_layers_parameters,
            lr=self.config.train_learning_rate,
            betas=(self.config.train_adam_beta1, self.config.train_adam_beta2),
            weight_decay=self.config.train_adam_weight_decay,
            eps=self.config.train_adam_epsilon,
        )

    def _save_model_hook(self, models, weights, output_dir):
        self.sd_pipeline.save_checkpoint(models, weights, output_dir)
        weights.pop()  # ensures that accelerate doesn't try to handle saving of the model

    def _load_model_hook(self, models, input_dir):
        self.sd_pipeline.load_checkpoint(models, input_dir)
        models.pop()  # ensures that accelerate doesn't try to handle loading of the model

    def _generate_samples(self, iterations, batch_size):
        """
        Generate samples from the model

        Args:
            iterations (int): Number of iterations to generate samples for
            batch_size (int): Batch size to use for sampling

        Returns:
            samples (list[dict[str, torch.Tensor]]), prompt_image_pairs (list[list[Any]])
        """
        samples = []
        prompt_image_pairs = []
        self.sd_pipeline.unet.eval()

        sample_neg_prompt_embeds = self.neg_prompt_embed.repeat(batch_size, 1, 1)

        for _ in range(iterations):
            prompts, prompt_metadata = zip(*[self.prompt_fn() for _ in range(batch_size)])

            prompt_ids = self.sd_pipeline.tokenizer(
                prompts,
                return_tensors="pt",
                padding="max_length",
                truncation=True,
                max_length=self.sd_pipeline.tokenizer.model_max_length,
            ).input_ids.to(self.accelerator.device)
            prompt_embeds = self.sd_pipeline.text_encoder(prompt_ids)[0]

            with self.autocast():
                sd_output = self.sd_pipeline(
                    prompt_embeds=prompt_embeds,
                    negative_prompt_embeds=sample_neg_prompt_embeds,
                    num_inference_steps=self.config.sample_num_steps,
                    guidance_scale=self.config.sample_guidance_scale,
                    eta=self.config.sample_eta,
                    output_type="pt",
                )

                images = sd_output.images
                latents = sd_output.latents
                log_probs = sd_output.log_probs

            latents = torch.stack(latents, dim=1)  # (batch_size, num_steps + 1, ...)
            log_probs = torch.stack(log_probs, dim=1)  # (batch_size, num_steps, 1)
            timesteps = self.sd_pipeline.scheduler.timesteps.repeat(batch_size, 1)  # (batch_size, num_steps)

            samples.append(
                {
                    "prompt_ids": prompt_ids,
                    "prompt_embeds": prompt_embeds,
                    "timesteps": timesteps,
                    "latents": latents[:, :-1],  # each entry is the latent before timestep t
                    "next_latents": latents[:, 1:],  # each entry is the latent after timestep t
                    "log_probs": log_probs,
                    "negative_prompt_embeds": sample_neg_prompt_embeds,
                }
            )
            prompt_image_pairs.append([images, prompts, prompt_metadata])

        return samples, prompt_image_pairs

    def _train_batched_samples(self, inner_epoch, epoch, global_step, batched_samples):
        """
        Train on a batch of samples. Main training segment

        Args:
            inner_epoch (int): The current inner epoch
            epoch (int): The current epoch
            global_step (int): The current global step
            batched_samples (list[dict[str, torch.Tensor]]): The batched samples to train on

        Side Effects:
            - Model weights are updated
            - Logs the statistics to the accelerator trackers.

        Returns:
            global_step (int): The updated global step
        """
        info = defaultdict(list)
        for _i, sample in enumerate(batched_samples):
            if self.config.train_cfg:
                # concat negative prompts to sample prompts to avoid two forward passes
                embeds = torch.cat([sample["negative_prompt_embeds"], sample["prompt_embeds"]])
            else:
                embeds = sample["prompt_embeds"]

            for j in range(self.num_train_timesteps):
                with self.accelerator.accumulate(self.sd_pipeline.unet):
                    loss, approx_kl, clipfrac = self.calculate_loss(
                        sample["latents"][:, j],
                        sample["timesteps"][:, j],
                        sample["next_latents"][:, j],
                        sample["log_probs"][:, j],
                        sample["advantages"],
                        embeds,
                    )
                    info["approx_kl"].append(approx_kl)
                    info["clipfrac"].append(clipfrac)
                    info["loss"].append(loss)

                    self.accelerator.backward(loss)
                    if self.accelerator.sync_gradients:
                        self.accelerator.clip_grad_norm_(
                            self.trainable_layers.parameters()
                            if not isinstance(self.trainable_layers, list)
                            else self.trainable_layers,
                            self.config.train_max_grad_norm,
                        )
                    self.optimizer.step()
                    self.optimizer.zero_grad()

                # Checks if the accelerator has performed an optimization step behind the scenes
                if self.accelerator.sync_gradients:
                    # log training-related stuff
                    info = {k: torch.mean(torch.stack(v)) for k, v in info.items()}
                    info = self.accelerator.reduce(info, reduction="mean")
                    info.update({"epoch": epoch, "inner_epoch": inner_epoch})
                    self.accelerator.log(info, step=global_step)
                    global_step += 1
                    info = defaultdict(list)
        return global_step

    def _config_check(self) -> tuple[bool, str]:
        samples_per_epoch = (
            self.config.sample_batch_size * self.accelerator.num_processes * self.config.sample_num_batches_per_epoch
        )
        total_train_batch_size = (
            self.config.train_batch_size
            * self.accelerator.num_processes
            * self.config.train_gradient_accumulation_steps
        )

        if not self.config.sample_batch_size >= self.config.train_batch_size:
            return (
                False,
                f"Sample batch size ({self.config.sample_batch_size}) must be greater than or equal to the train batch size ({self.config.train_batch_size})",
            )
        if not self.config.sample_batch_size % self.config.train_batch_size == 0:
            return (
                False,
                f"Sample batch size ({self.config.sample_batch_size}) must be divisible by the train batch size ({self.config.train_batch_size})",
            )
        if not samples_per_epoch % total_train_batch_size == 0:
            return (
                False,
                f"Number of samples per epoch ({samples_per_epoch}) must be divisible by the total train batch size ({total_train_batch_size})",
            )
        return True, ""

    def train(self, epochs: Optional[int] = None):
        """
        Train the model for a given number of epochs
        """
        global_step = 0
        if epochs is None:
            epochs = self.config.num_epochs
        for epoch in range(self.first_epoch, epochs):
            global_step = self.step(epoch, global_step)

    def _save_pretrained(self, save_directory):
        self.sd_pipeline.save_pretrained(save_directory)
        self.create_model_card()

    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{black2024training,
            title        = {{Training Diffusion Models with Reinforcement Learning}},
            author       = {Kevin Black and Michael Janner and Yilun Du and Ilya Kostrikov and Sergey Levine},
            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=YCWjhGrJFD},
        }""")

        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="DDPO",
            trainer_citation=citation,
            paper_title="Training Diffusion Models with Reinforcement Learning",
            paper_id="2305.13301",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothDDPOTrainer(_UnslothDDPOTrainer):
    """
    
    The DDPOTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models.
    Note, this trainer is heavily inspired by the work here: https://github.com/kvablack/ddpo-pytorch
    As of now only Stable Diffusion based pipelines are supported

    Attributes:
        **config** (`DDPOConfig`) -- Configuration object for DDPOTrainer. Check the documentation of `PPOConfig` for more
         details.
        **reward_function** (Callable[[torch.Tensor, tuple[str], tuple[Any]], torch.Tensor]) -- Reward function to be used
        **prompt_function** (Callable[[], tuple[str, Any]]) -- Function to generate prompts to guide model
        **sd_pipeline** (`DDPOStableDiffusionPipeline`) -- Stable Diffusion pipeline to be used for training.
        **image_samples_hook** (Optional[Callable[[Any, Any, Any], Any]]) -- Hook to be called to log images
    
    """
    def __init__(
        self,
        config,
        reward_function,
        prompt_function,
        sd_pipeline,
        image_samples_hook = None,
        **kwargs
    ):
        if args is None: args = UnslothDDPOConfig()
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('ddpo_trainer', other_metrics)
        
        super().__init__(
            config = config,
            reward_function = reward_function,
            prompt_function = prompt_function,
            sd_pipeline = sd_pipeline,
            image_samples_hook = image_samples_hook,**kwargs)
        
pass