train.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 MaPO authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import math
import os
import shutil
from pathlib import Path

import torch
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from tqdm.auto import tqdm
from transformers import AutoTokenizer

import diffusers
from args import parse_args
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils.import_utils import is_xformers_available
from utils import (
    collate_fn,
    compute_loss,
    compute_time_ids,
    encode_prompt,
    get_dataset_preprocessor,
    get_wandb_url,
    import_model_class_from_model_name_or_path,
    log_validation,
)


logger = get_logger(__name__)


def main(args):
    logging_dir = Path(args.output_dir, args.logging_dir)

    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with=args.report_to,
        project_config=accelerator_project_config,
    )

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

        if args.push_to_hub:
            repo_id = create_repo(
                repo_id=args.hub_model_id or Path(args.output_dir).name,
                exist_ok=True,
            ).repo_id

    # Load the tokenizers
    tokenizer_one = AutoTokenizer.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="tokenizer",
        revision=args.revision,
        use_fast=False,
        cache_dir=args.cache_dir,
    )
    tokenizer_two = AutoTokenizer.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="tokenizer_2",
        revision=args.revision,
        use_fast=False,
        cache_dir=args.cache_dir,
    )

    # import correct text encoder classes
    text_encoder_cls_one = import_model_class_from_model_name_or_path(
        args.pretrained_model_name_or_path, args.revision
    )
    text_encoder_cls_two = import_model_class_from_model_name_or_path(
        args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
    )

    # Load scheduler and models
    noise_scheduler = DDPMScheduler.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="scheduler", cache_dir=args.cache_dir
    )

    text_encoder_one = text_encoder_cls_one.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="text_encoder",
        revision=args.revision,
        variant=args.variant,
        cache_dir=args.cache_dir,
    )
    text_encoder_two = text_encoder_cls_two.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="text_encoder_2",
        revision=args.revision,
        variant=args.variant,
        cache_dir=args.cache_dir,
    )
    vae_path = (
        args.pretrained_model_name_or_path
        if args.pretrained_vae_model_name_or_path is None
        else args.pretrained_vae_model_name_or_path
    )
    vae = AutoencoderKL.from_pretrained(
        vae_path,
        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
        revision=args.revision,
        variant=args.variant,
        cache_dir=args.cache_dir,
    )
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="unet",
        revision=args.revision,
        variant=args.variant,
        cache_dir=args.cache_dir,
    )

    # Set the grad requirements.
    vae.requires_grad_(False)
    text_encoder_one.requires_grad_(False)
    text_encoder_two.requires_grad_(False)
    unet.requires_grad_(True)

    # For mixed precision training we cast all non-trainable weights to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
    weight_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    # Move text_encoders to device and cast to weight_dtype
    text_encoder_one.to(accelerator.device, dtype=weight_dtype)
    text_encoder_two.to(accelerator.device, dtype=weight_dtype)

    # The VAE is always in float32 to avoid NaN losses.
    vae.to(accelerator.device, dtype=torch.float32)

    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warning(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            unet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()

    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
    def save_model_hook(models, weights, output_dir):
        if accelerator.is_main_process:
            for model in models:
                if isinstance(model, type(accelerator.unwrap_model(unet))):
                    model.save_pretrained(os.path.join(output_dir, "unet"))
                else:
                    raise ValueError(f"unexpected save model: {model.__class__}")

                # make sure to pop weight so that corresponding model is not saved again
                weights.pop()

    def load_model_hook(models, input_dir):
        unet_ = None

        while len(models) > 0:
            model = models.pop()

            if isinstance(model, type(accelerator.unwrap_model(unet))):
                unet_ = model
            else:
                raise ValueError(f"unexpected save model: {model.__class__}")

        load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
        unet_.register_to_config(**load_model.config)

        unet_.load_state_dict(load_model.state_dict())
        del load_model

    accelerator.register_save_state_pre_hook(save_model_hook)
    accelerator.register_load_state_pre_hook(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 args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
        )

    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
            )

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

    # Optimizer creation
    params_to_optimize = list(filter(lambda p: p.requires_grad, unet.parameters()))
    optimizer = optimizer_class(
        params_to_optimize,
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    # Dataset and DataLoaders creation:
    train_dataset = load_dataset(
        args.dataset_name,
        cache_dir=args.cache_dir,
        split=args.dataset_split_name,
    )

    # Preprocessing the datasets.
    preprocess_train_fn = get_dataset_preprocessor(args, tokenizer_one, tokenizer_two)

    with accelerator.main_process_first():
        if args.max_train_samples is not None:
            train_dataset = train_dataset.shuffle(seed=args.seed).select(range(args.max_train_samples))
        # Set the training transforms
        if args.remove_ties:
            train_dataset = train_dataset.filter(lambda example: example["label_0"] != example["label_1"])
        else:
            pass
        train_dataset = train_dataset.with_transform(preprocess_train_fn)

    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=args.train_batch_size,
        shuffle=True,
        collate_fn=collate_fn,
        num_workers=args.dataloader_num_workers,
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps * accelerator.num_processes,
        num_cycles=args.lr_num_cycles,
        power=args.lr_power,
    )

    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        unet, optimizer, train_dataloader, lr_scheduler
    )

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers(args.tracker_name, config=vars(args))

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    global_step = 0
    first_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the mos recent checkpoint
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
            initial_global_step = 0
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            initial_global_step = global_step
            first_epoch = global_step // num_update_steps_per_epoch
    else:
        initial_global_step = 0

    progress_bar = tqdm(
        range(0, args.max_train_steps),
        initial=initial_global_step,
        desc="Steps",
        # Only show the progress bar once on each machine.
        disable=not accelerator.is_local_main_process,
    )

    unet.train()
    for epoch in range(first_epoch, args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(unet):
                # (batch_size, 2*channels, h, w) -> (2*batch_size, channels, h, w)
                pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
                feed_pixel_values = torch.cat(pixel_values.chunk(2, dim=1))

                latents = []
                for i in range(0, feed_pixel_values.shape[0], args.vae_encode_batch_size):
                    latents.append(
                        vae.encode(feed_pixel_values[i : i + args.vae_encode_batch_size]).latent_dist.sample()
                    )
                latents = torch.cat(latents, dim=0)
                latents = latents * vae.config.scaling_factor
                if args.pretrained_vae_model_name_or_path is None:
                    latents = latents.to(weight_dtype)

                # Sample noise that we'll add to the latents
                noise = torch.randn_like(latents).chunk(2)[0].repeat(2, 1, 1, 1)

                # Sample a random timestep for each image
                bsz = latents.shape[0] // 2
                timesteps = torch.randint(
                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device, dtype=torch.long
                ).repeat(2)

                # Add noise to the model input according to the noise magnitude at each timestep
                # (this is the forward diffusion process)
                noisy_model_input = noise_scheduler.add_noise(latents, noise, timesteps)

                # time ids
                add_time_ids = torch.cat(
                    [
                        compute_time_ids(args, accelerator, weight_dtype, s, c)
                        for s, c in zip(batch["original_sizes"], batch["crop_top_lefts"])
                    ]
                ).repeat(2, 1)

                # Get the text embedding for conditioning
                prompt_embeds, pooled_prompt_embeds = encode_prompt(
                    [text_encoder_one, text_encoder_two], [batch["input_ids_one"], batch["input_ids_two"]]
                )
                prompt_embeds = prompt_embeds.repeat(2, 1, 1)
                pooled_prompt_embeds = pooled_prompt_embeds.repeat(2, 1)

                # Predict the noise residual
                model_pred = unet(
                    noisy_model_input,
                    timesteps,
                    prompt_embeds,
                    added_cond_kwargs={"time_ids": add_time_ids, "text_embeds": pooled_prompt_embeds},
                ).sample

                # Get the target for loss depending on the prediction type
                if noise_scheduler.config.prediction_type == "epsilon":
                    target = noise
                elif noise_scheduler.config.prediction_type == "v_prediction":
                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
                else:
                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

                # Full ORPO loss
                loss, model_losses_w, model_losses_l, ratio_losses = compute_loss(
                    args=args, noise_scheduler=noise_scheduler, model_pred=model_pred, target=target
                )

                # Backprop.
                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1

                if accelerator.is_main_process:
                    if global_step % args.checkpointing_steps == 0:
                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                                removing_checkpoints = checkpoints[0:num_to_remove]

                                logger.info(
                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                )
                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

                                for removing_checkpoint in removing_checkpoints:
                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
                                    shutil.rmtree(removing_checkpoint)

                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                        accelerator.save_state(save_path)
                        logger.info(f"Saved state to {save_path}")

                    if args.run_validation and global_step % args.validation_steps == 0:
                        log_validation(
                            args, unet=unet, vae=vae, accelerator=accelerator, weight_dtype=weight_dtype, epoch=epoch
                        )

            logs = {
                "epoch": args.num_train_epochs * progress_bar.n / args.max_train_steps,
                "total loss": loss.detach().item(),
                "Win Score": ((args.snr_value * model_losses_w) / (torch.exp(args.snr_value * model_losses_w) - 1))
                .mean()
                .detach()
                .item(),
                "Lose Score": ((args.snr_value * model_losses_l) / (torch.exp(args.snr_value * model_losses_l) - 1))
                .mean()
                .detach()
                .item(),
                "lr": lr_scheduler.get_last_lr()[0],
                "OR loss": -ratio_losses.mean().detach().item(),
                "model_losses_w": model_losses_w.mean().detach().item(),
                "model_losses_l": model_losses_l.mean().detach().item(),
            }
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unet = accelerator.unwrap_model(unet)
        unet.save_pretrained(args.output_dir)

        # Final validation?
        if args.run_validation:
            log_validation(
                args,
                unet=None,
                vae=vae,
                accelerator=accelerator,
                weight_dtype=weight_dtype,
                epoch=epoch,
                is_final_validation=True,
            )

        if args.push_to_hub:
            wandb_info = get_wandb_url()
            with open(os.path.join(args.output_dir, "README.md"), "w") as f:
                f.write(wandb_info)

            upload_folder(
                repo_id=repo_id,
                folder_path=args.output_dir,
                commit_message="End of training",
                ignore_patterns=["step_*", "epoch_*"],
            )

    accelerator.end_training()


if __name__ == "__main__":
    args = parse_args()
    main(args)