unsloth_compiled_cache/UnslothAlignPropTrainer.py

"""
2025.9.14
2025.9.11
4.56.2
0.23.0
__UNSLOTH_VERSIONING__
"""

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

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.alignprop_trainer import (Accelerator, AlignPropConfig, AlignPropTrainer, Any, Callable, DDPOStableDiffusionPipeline, Optional, Path, ProjectConfiguration, PyTorchModelHubMixin, Union, defaultdict, generate_model_card, get_comet_experiment_url, is_wandb_available, logger, logging, os, set_seed, textwrap, torch, wandb, warnings)


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

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

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

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

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

    prompt_section = input_ids[:, :-logits_to_keep]

    padding_mask = (prompt_section == pad_token_id)

    pad_token_counts = padding_mask.sum(dim=1)

    return pad_token_counts

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

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

    num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt

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

    non_padding_mask = (completion_input_ids != pad_token_id)

    final_mask = shift_mask & non_padding_mask

    return final_mask

def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
    """
    Moves all padding tokens in each sequence of a batch to the right.
    """
    mask = (tensor != pad_id)
    # Must do stable=True since binary mark is unordered
    sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
    packed_tensor = torch.gather(tensor, 1, sorted_indices)
    return packed_tensor
@dataclass
class UnslothAlignPropConfig(AlignPropConfig):
    """
    
    Configuration class for the [`AlignPropTrainer`].

    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 (defaults to the file name without the extension).
        run_name (`str`, *optional*, defaults to `""`):
            Name of this run.
        seed (`int`, *optional*, defaults to `0`):
            Random seed for reproducibility.
        log_with (`str` or `None`, *optional*, defaults to `None`):
            Log with either `"wandb"` or `"tensorboard"`. Check
            [tracking](https://huggingface.co/docs/accelerate/usage_guides/tracking) for more details.
        log_image_freq (`int`, *optional*, defaults to `1`):
            Frequency for logging images.
        tracker_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`):
            Keyword arguments for the tracker (e.g., `wandb_project`).
        accelerator_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`):
            Keyword arguments for the accelerator.
        project_kwargs (`dict[str, Any]`, *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 `""`):
            Path 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.
        train_batch_size (`int`, *optional*, defaults to `1`):
            Batch size for training.
        train_use_8bit_adam (`bool`, *optional*, defaults to `False`):
            Whether to use the 8bit Adam optimizer from `bitsandbytes`.
        train_learning_rate (`float`, *optional*, defaults to `1e-3`):
            Learning rate.
        train_adam_beta1 (`float`, *optional*, defaults to `0.9`):
            Beta1 for Adam optimizer.
        train_adam_beta2 (`float`, *optional*, defaults to `0.999`):
            Beta2 for Adam optimizer.
        train_adam_weight_decay (`float`, *optional*, defaults to `1e-4`):
            Weight decay for Adam optimizer.
        train_adam_epsilon (`float`, *optional*, defaults to `1e-8`):
            Epsilon value for Adam optimizer.
        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.
        negative_prompts (`str` or `None`, *optional*, defaults to `None`):
            Comma-separated list of prompts to use as negative examples.
        truncated_backprop_rand (`bool`, *optional*, defaults to `True`):
            If `True`, randomized truncation to different diffusion timesteps is used.
        truncated_backprop_timestep (`int`, *optional*, defaults to `49`):
            Absolute timestep to which the gradients are backpropagated. Used only if `truncated_backprop_rand=False`.
        truncated_rand_backprop_minmax (`tuple[int, int]`, *optional*, defaults to `(0, 50)`):
            Range of diffusion timesteps for randomized truncated backpropagation.
        push_to_hub (`bool`, *optional*, defaults to `False`):
            Whether to push the final model 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,
        log_image_freq = 1,
        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,
        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,
        negative_prompts = None,
        truncated_backprop_rand = True,
        truncated_backprop_timestep = 49,
        push_to_hub = False,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        
        **kwargs,
    ):
        if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
        if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
        
        super().__init__(
            exp_name = exp_name,
            run_name = run_name,
            seed = seed,
            log_with = log_with,
            log_image_freq = log_image_freq,
            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,
            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,
            negative_prompts = negative_prompts,
            truncated_backprop_rand = truncated_backprop_rand,
            truncated_backprop_timestep = truncated_backprop_timestep,
            push_to_hub = push_to_hub,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
        
pass

class _UnslothAlignPropTrainer(PyTorchModelHubMixin):
    """"""

    _tag_names = ["trl", "alignprop"]

    def __init__(
        self,
        config: AlignPropConfig,
        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,
    ):
        warnings.warn(
            "AlignPropTrainer is deprecated and will be removed in version 0.23.0.",
            DeprecationWarning,
        )
        if image_samples_hook is None:
            logger.warning("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

        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.config.accelerator_kwargs,
        )

        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(alignprop_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 and torch.cuda.is_available():
            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]

        # 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 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):
        reward, reward_metadata = self.reward_fn(
            prompt_image_pairs["images"], prompt_image_pairs["prompts"], prompt_image_pairs["prompt_metadata"]
        )
        return reward

    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.
        """
        info = defaultdict(list)

        self.sd_pipeline.unet.train()

        for _ in range(self.config.train_gradient_accumulation_steps):
            with self.accelerator.accumulate(self.sd_pipeline.unet), self.autocast(), torch.enable_grad():
                prompt_image_pairs = self._generate_samples(
                    batch_size=self.config.train_batch_size,
                )

                rewards = self.compute_rewards(prompt_image_pairs)

                prompt_image_pairs["rewards"] = rewards

                rewards_vis = self.accelerator.gather(rewards).detach().cpu().numpy()

                loss = self.calculate_loss(rewards)

                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()

            info["reward_mean"].append(rewards_vis.mean())
            info["reward_std"].append(rewards_vis.std())
            info["loss"].append(loss.item())

        # 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.tensor(v)) for k, v in info.items()}
            info = self.accelerator.reduce(info, reduction="mean")
            info.update({"epoch": epoch})
            self.accelerator.log(info, step=global_step)
            global_step += 1
            info = defaultdict(list)
        else:
            raise ValueError(
                "Optimization step should have been performed by this point. Please check calculated gradient accumulation settings."
            )
        # Logs generated images
        if self.image_samples_callback is not None and global_step % self.config.log_image_freq == 0:
            self.image_samples_callback(prompt_image_pairs, global_step, self.accelerator.trackers[0])

        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, rewards):
        """
        Calculate the loss for a batch of an unpacked sample

        Args:
            rewards (torch.Tensor):
                Differentiable reward scalars for each generated image, shape: [batch_size]

        Returns:
            loss (torch.Tensor) (all of these are of shape (1,))
        """
        #  Loss is specific to Aesthetic Reward function used in AlignProp (https://huggingface.co/papers/2310.03739)
        loss = 10.0 - (rewards).mean()
        return loss

    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, batch_size, with_grad=True, prompts=None):
        """
        Generate samples from the model

        Args:
            batch_size (int): Batch size to use for sampling
            with_grad (bool): Whether the generated RGBs should have gradients attached to it.
            prompts (list[str], *optional*): If provided, use these prompts instead of generating new ones.

        Returns:
            prompt_image_pairs (dict[Any])
        """
        prompt_image_pairs = {}

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

        if prompts is None:
            prompts, prompt_metadata = zip(*[self.prompt_fn() for _ in range(batch_size)])
        else:
            prompt_metadata = [{} 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]

        if with_grad:
            sd_output = self.sd_pipeline.rgb_with_grad(
                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,
                truncated_backprop_rand=self.config.truncated_backprop_rand,
                truncated_backprop_timestep=self.config.truncated_backprop_timestep,
                truncated_rand_backprop_minmax=self.config.truncated_rand_backprop_minmax,
                output_type="pt",
            )
        else:
            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

        prompt_image_pairs["images"] = images
        prompt_image_pairs["prompts"] = prompts
        prompt_image_pairs["prompt_metadata"] = prompt_metadata

        return prompt_image_pairs

    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()

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

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

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

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

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

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

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

        tags.update(self._tag_names)

        # docstyle-ignore
        citation = textwrap.dedent("""\
        @article{prabhudesai2024aligning,
            title        = {{Aligning Text-to-Image Diffusion Models with Reward Backpropagation}},
            author       = {Mihir Prabhudesai and Anirudh Goyal and Deepak Pathak and Katerina Fragkiadaki},
            year         = 2024,
            eprint       = {arXiv:2310.03739}
        }""")

        model_card = generate_model_card(
            base_model=base_model,
            model_name=model_name,
            hub_model_id=self.hub_model_id,
            dataset_name=dataset_name,
            tags=tags,
            wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None,
            comet_url=get_comet_experiment_url(),
            trainer_name="AlignProp",
            trainer_citation=citation,
            paper_title="Aligning Text-to-Image Diffusion Models with Reward Backpropagation",
            paper_id="2310.03739",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))
class UnslothAlignPropTrainer(_UnslothAlignPropTrainer):
    """
    
    The AlignPropTrainer uses Deep Diffusion Policy Optimization to optimise diffusion models. Note, this trainer is
    heavily inspired by the work here: https://github.com/mihirp1998/AlignProp/ As of now only Stable Diffusion based
    pipelines are supported

    Args:
        config (`AlignPropConfig`):
            Configuration object for AlignPropTrainer. 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 = UnslothAlignPropConfig()
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('alignprop_trainer', other_metrics)
        
        # [TODO] Fix up DataParallel multiplying batch sizes
        # [TODO] DDP works, but DP seems to not work? [TODO]
        if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
            if getattr(args, "_n_gpu", 1) != 1:
                args._n_gpu = 1
        if "model" in locals() and hasattr(model, "for_training"):
            model.for_training()
        super().__init__(
            config = config,
            reward_function = reward_function,
            prompt_function = prompt_function,
            sd_pipeline = sd_pipeline,
            image_samples_hook = image_samples_hook,**kwargs)
        if "model" in locals() and hasattr(model, "for_inference"):
            model.for_inference()
        
pass


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