diff --git a/.gitignore b/.gitignore index 34ee705..cd275a9 100644 --- a/.gitignore +++ b/.gitignore @@ -27,8 +27,6 @@ share/python-wheels/ MANIFEST # PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec @@ -83,36 +81,12 @@ notebooks/ profile_default/ ipython_config.py -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - # pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control .pdm.toml .pdm-python .pdm-build/ -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +# PEP 582 __pypackages__/ # Celery stuff @@ -155,13 +129,6 @@ dmypy.json # Cython debug symbols cython_debug/ -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - # running logs examples/wandb outputs/ diff --git a/README.md b/README.md index edf70ed..c48d345 100644 --- a/README.md +++ b/README.md @@ -63,10 +63,10 @@ Here is a To-Do list, feel welcome to help to any point along this list. The alr - [ ] add some more context embedders - [ ] add reflow algorithm - [ ] add EDM -- [ ] think about how to add a sampler abstraction and use it in the different Diffusion classes (generalist class with euler, heuns etc) +- [x] think about how to add a sampler abstraction and use it in the different Diffusion classes (generalist class with euler, heuns etc) - [ ] Train our models on toy datasets for different tasks (conditional generation, Image to Image ...) - [ ] Add possibility to train LORA/DORA -- [ ] add different sampler +- [x] add different sampler - [ ] Try out Differential Transformers - [ ] Check to add https://arxiv.org/pdf/2406.02507 - [ ] inject lessons learned from nvidia https://developer.nvidia.com/blog/rethinking-how-to-train-diffusion-models/ diff --git a/configs/dataset/imagenet_repa.yaml b/configs/dataset/imagenet_latents.yaml similarity index 67% rename from configs/dataset/imagenet_repa.yaml rename to configs/dataset/imagenet_latents.yaml index e4622b3..ed103eb 100644 --- a/configs/dataset/imagenet_repa.yaml +++ b/configs/dataset/imagenet_latents.yaml @@ -1,13 +1,13 @@ # CIFAR10 dataset configuration train: - _target_: diffulab.datasets.ImageNetLatentREPA + _target_: diffulab.datasets.ImageNetLatent data_path: "data/imagenet" local: true batch_size: 128 split: "train" val: - _target_: diffulab.datasets.ImageNetLatentREPA + _target_: diffulab.datasets.ImageNetLatent data_path: "data/imagenet" local: true batch_size: 128 diff --git a/configs/dataset/imagenet_noised_latents.yaml b/configs/dataset/imagenet_noised_latents.yaml new file mode 100644 index 0000000..e64ff2d --- /dev/null +++ b/configs/dataset/imagenet_noised_latents.yaml @@ -0,0 +1,16 @@ +# CIFAR10 dataset configuration +train: + _target_: diffulab.datasets.ImageNetNoisyLatent + data_path: "data/imagenet" + local: true + batch_size: 128 + split: "train" + noise_tau: 0.8 + +val: + _target_: diffulab.datasets.ImageNetNoisyLatent + data_path: "data/imagenet" + local: true + batch_size: 128 + split: "val" + noise_tau : 0 diff --git a/configs/discriminator/rae.yaml b/configs/discriminator/rae.yaml new file mode 100644 index 0000000..15bd64d --- /dev/null +++ b/configs/discriminator/rae.yaml @@ -0,0 +1,3 @@ +_target_: diffulab.networks.disc.RAEDiscriminator +model_name: facebook/dino-vits8 +features_depth: [2, 5, 8, 11] diff --git a/configs/optimizer/adam.yaml b/configs/optimizer/adam.yaml new file mode 100644 index 0000000..74efe62 --- /dev/null +++ b/configs/optimizer/adam.yaml @@ -0,0 +1,5 @@ +_target_: torch.optim.Adam +lr: 1e-4 +weight_decay: 0 +betas: [0.9, 0.999] +eps: 1e-8 diff --git a/configs/train_imagenet_flow_matching_repa.yaml b/configs/train_imagenet_flow_matching_repa.yaml index 23d422c..e7c3e8c 100644 --- a/configs/train_imagenet_flow_matching_repa.yaml +++ b/configs/train_imagenet_flow_matching_repa.yaml @@ -1,10 +1,10 @@ -# configs/train_cifar10_flow_matching.yaml +# configs/train_imagenet_flow_matching_repa.yaml # @package _global_ defaults: - model: dit - diffuser: rectified_flow - trainer: default - - dataset: imagenet_repa + - dataset: imagenet_latents - dataloader: default - optimizer: adamw - vision_tower: dcae diff --git a/configs/train_imagenet_rae_decoder.yaml b/configs/train_imagenet_rae_decoder.yaml new file mode 100644 index 0000000..5476353 --- /dev/null +++ b/configs/train_imagenet_rae_decoder.yaml @@ -0,0 +1,41 @@ +defaults: + - vision_tower: rae + - discriminator: rae + - dataloader: default + - dataset: imagenet_noised_latents + - /optimizer@optimizer.rae_decoder: adam + - /optimizer@optimizer.discriminator: adam + - trainer: default + +vision_tower: + load_encoder: false + +optimizer: + rae_decoder: + betas: [0.5, 0.9] + lr : 2e-4 + discriminator: + betas: [0.5, 0.9] + lr : 2e-4 + +dataloader: + batch_size: 32 + +dataset: + train: + batch_size: 32 + val: + batch_size: 32 + +trainer: + project_name: imagenet_rae_decoder + n_epoch: 16 + precision_type: "bf16" + per_batch_scheduler: true + disc_epoch_start: 6 + gan_epoch_start: 8 + lpips_epoch_start: 1 + lambda_lpips: 1 + lambda_gan: 0.75 + use_adaptive_weight_loss: true + gradient_accumulation_step: 16 diff --git a/configs/vision_tower/rae.yaml b/configs/vision_tower/rae.yaml new file mode 100644 index 0000000..838a959 --- /dev/null +++ b/configs/vision_tower/rae.yaml @@ -0,0 +1,19 @@ +_target_: diffulab.networks.vision_towers.RAE +decoder: + _target_: diffulab.networks.vision_towers.RAEDecoder + out_size: [256, 256] + out_channels: 3 + encoder_dim: 768 + input_dim: 1024 + hidden_dim: 1024 + num_heads: 16 + mlp_ratio: 4 + patch_size: 16 + depth: 24 + partial_rotary_factor: 1 + use_checkpoint: False + dropout_attn: 0 + dropout_mlp: 0 +dinov3_id: facebook/dinov3-vitb16-pretrain-lvd1689m +load_encoder: true +encoder_patch_size: 16 diff --git a/examples/train_diffusion.py b/examples/train_diffusion.py index ba28f2d..d5d5a0e 100644 --- a/examples/train_diffusion.py +++ b/examples/train_diffusion.py @@ -5,7 +5,7 @@ from torch.utils.data import DataLoader from diffulab.diffuse import Diffuser -from diffulab.training import Trainer +from diffulab.training import BaseTrainer @hydra.main(version_base=None, config_path="../configs", config_name="train_mnist_flow_matching") @@ -52,8 +52,7 @@ def count_parameters(model: torch.nn.Module) -> int: params=denoiser.parameters(), ) - # TODO: add a run name for wandb - trainer = Trainer( + trainer = BaseTrainer( n_epoch=cfg.trainer.n_epoch, gradient_accumulation_step=cfg.trainer.gradient_accumulation_step, precision_type=cfg.trainer.precision_type, @@ -62,6 +61,10 @@ def count_parameters(model: torch.nn.Module) -> int: ema_update_after_step=cfg.trainer.get("ema_update_after_step", 0), ema_update_every=cfg.trainer.get("ema_update_every", 10), run_config=OmegaConf.to_container(cfg, resolve=True), # type: ignore[reportArgumentType] + compile=cfg.trainer.get("compile", False), + init_kwargs={ + "wandb": cfg.trainer.get("wandb", {}), + }, ) trainer.train( diff --git a/examples/train_rae_decoder.py b/examples/train_rae_decoder.py new file mode 100644 index 0000000..518c2c3 --- /dev/null +++ b/examples/train_rae_decoder.py @@ -0,0 +1,125 @@ +import math + +import hydra +import torch +from hydra.utils import instantiate +from omegaconf import DictConfig, OmegaConf +from torch.optim.lr_scheduler import LambdaLR +from torch.utils.data import DataLoader + +from diffulab.datasets.imagenet import ImageNetLatent +from diffulab.networks.disc import RAEDiscriminator +from diffulab.networks.vision_towers.rae import RAE +from diffulab.training.trainers.extra.rae_trainer import RAETrainer + + +def cosine_with_warmup_and_min_lr_lambda( + current_step: int, num_warmup_steps: int, num_training_steps: int, min_lr_factor: float = 0.1 +) -> float: + if current_step < num_warmup_steps: + return float(current_step) / float(max(1, num_warmup_steps)) + progress = (current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) + cosine = 0.5 * (1.0 + math.cos(math.pi * progress)) + # interpolate between 1 and min_lr_factor + return min_lr_factor + (1 - min_lr_factor) * cosine + + +def get_cosine_schedule_with_warmup_and_min_lr( + optimizer: torch.optim.Optimizer, num_warmup_steps: int, num_training_steps: int, min_lr_factor: float = 0.1 +) -> LambdaLR: + lr_lambda = lambda step: cosine_with_warmup_and_min_lr_lambda( # type: ignore + step, # type: ignore + num_warmup_steps, + num_training_steps, + min_lr_factor, # type: ignore + ) + return LambdaLR(optimizer, lr_lambda) # type: ignore + + +@hydra.main(version_base=None, config_path="../configs", config_name="train_imagenet_rae_decoder") +def train(cfg: DictConfig) -> None: + print(OmegaConf.to_yaml(cfg)) + + train_dataset: ImageNetLatent = instantiate(cfg.dataset.train) + val_dataset: ImageNetLatent = instantiate(cfg.dataset.val) + + train_dataset.set_latent_scale(1) + val_dataset.set_latent_scale(1) + + dl_cfg = cfg.get("dataloader", {}) + train_loader = DataLoader( + dataset=train_dataset, + batch_size=dl_cfg.get("batch_size", 32), + shuffle=dl_cfg.get("shuffle", True), + num_workers=dl_cfg.get("num_workers", 0), + pin_memory=dl_cfg.get("pin_memory", False), + ) + + val_loader = DataLoader( + dataset=val_dataset, + batch_size=dl_cfg.get("batch_size", 32), + shuffle=dl_cfg.get("shuffle", False), + num_workers=dl_cfg.get("num_workers", 0), + pin_memory=dl_cfg.get("pin_memory", False), + ) + + rae: RAE = instantiate(cfg.vision_tower) + discriminator: RAEDiscriminator = instantiate(cfg.discriminator) + + def count_parameters(model: torch.nn.Module) -> int: + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + print(f"Number of trainable parameters in the rae decoder: {count_parameters(rae.decoder):,}") + print(f"Number of trainable parameters in the discriminator: {count_parameters(discriminator):,}") + + rae_optimizer = instantiate(cfg.optimizer.rae_decoder, params=rae.decoder.parameters()) + disc_optimizer = instantiate(cfg.optimizer.discriminator, params=discriminator.parameters()) + + rae_scheduler = get_cosine_schedule_with_warmup_and_min_lr( + optimizer=rae_optimizer, + num_warmup_steps=len(train_loader), + num_training_steps=cfg.trainer.n_epoch * len(train_loader), + ) + disc_scheduler = get_cosine_schedule_with_warmup_and_min_lr( + optimizer=disc_optimizer, + num_warmup_steps=len(train_loader), + num_training_steps=(cfg.trainer.n_epoch - cfg.trainer.disc_epoch_start) * len(train_loader), + ) + + rae_trainer = RAETrainer( + n_epoch=cfg.trainer.n_epoch, + gradient_accumulation_step=cfg.trainer.gradient_accumulation_step, + precision_type=cfg.trainer.precision_type, + project_name=cfg.trainer.project_name, + use_ema=cfg.trainer.use_ema, + ema_update_after_step=cfg.trainer.get("ema_update_after_step", 0), + ema_update_every=cfg.trainer.get("ema_update_every", 10), + run_config=OmegaConf.to_container(cfg, resolve=True), # type: ignore[reportArgumentType] + compile=cfg.trainer.get("compile", False), + init_kwargs={ + "wandb": cfg.trainer.get("wandb", {}), + }, + ) + + rae_trainer.train( + rae=rae, + disc=discriminator, + rae_optimizer=rae_optimizer, + disc_optimizer=disc_optimizer, + train_dataloader=train_loader, + val_dataloader=val_loader, + rae_scheduler=rae_scheduler, + disc_scheduler=disc_scheduler, + per_batch_scheduler=cfg.trainer.get("per_batch_scheduler", True), + log_validation_images=cfg.trainer.get("log_validation_images", True), + disc_epoch_start=cfg.trainer.get("disc_epoch_start", 6), + gan_epoch_start=cfg.trainer.get("gan_epoch_start", 8), + lpips_epoch_start=cfg.trainer.get("lpips_epoch_start", 1), + lambda_lpips=cfg.trainer.get("lambda_lpips", 1.0), + lambda_gan=cfg.trainer.get("lambda_gan", 0.75), + use_adaptive_weight_loss=cfg.trainer.get("use_adaptive_weight_loss", True), + ) + + +if __name__ == "__main__": + train() diff --git a/examples/train_repa.py b/examples/train_repa.py index 724c74f..28d362a 100644 --- a/examples/train_repa.py +++ b/examples/train_repa.py @@ -7,7 +7,7 @@ from torch.utils.data import DataLoader from diffulab.diffuse import Diffuser -from diffulab.training import Trainer +from diffulab.training import BaseTrainer from diffulab.training.losses.repa import RepaLoss @@ -77,7 +77,7 @@ def count_parameters(model: torch.nn.Module) -> int: + list(repa_loss.resampler.parameters() if repa_loss.resampler else []), ) - trainer = Trainer( + trainer = BaseTrainer( n_epoch=cfg.trainer.n_epoch, gradient_accumulation_step=cfg.trainer.gradient_accumulation_step, precision_type=cfg.trainer.precision_type, diff --git a/pyproject.toml b/pyproject.toml index 6d05a0e..1a2a060 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,22 +1,25 @@ [project] name = "diffulab" version = "0.1.0" -description = "Add your description here" +description = "DiffuLab is designed to provide a simple and flexible way to train diffusion models while allowing full customization of its core components" readme = "README.md" requires-python = ">=3.10" dependencies = [ "accelerate>=1.4.0", + "blobfile>=3.1.0", "diffusers>=0.33.1", "einops>=0.8.1", "ema-pytorch>=0.7.7", "hydra-core>=1.3.2", "jaxtyping>=0.3.0", + "loguru>=0.7.3", "mosaicml-streaming>=0.12.0", "omegaconf>=2.3.0", - "open-clip-torch>=2.30.0", "pyopenssl==23.2.0", + "sentencepiece>=0.2.1", + "tiktoken>=0.11.0", "torch>=2.6.0", - "transformers>=4.49.0", + "transformers==4.57.1", "wandb>=0.19.6", ] @@ -31,6 +34,9 @@ dev = [ repa = [ "timm>=1.0.15", ] +prefgrpo = [ + "qwen-vl-utils>=0.0.11", +] [tool.uv.sources] diffulab = {workspace = true} diff --git a/src/diffulab/__init__.py b/src/diffulab/__init__.py index 67b4ea5..1a27ee2 100644 --- a/src/diffulab/__init__.py +++ b/src/diffulab/__init__.py @@ -1,12 +1,25 @@ -from .datasets import BaseDataset, CIFAR10Dataset, ImageNetLatentREPA, MNISTDataset +from .datasets import BaseDataset, CIFAR10Dataset, ImageNetLatent, ImageNetNoisyLatent, MNISTDataset from .diffuse import Diffuser, Flow, GaussianDiffusion -from .networks import DCAE, REPA, Denoiser, DinoV2, MMDiT, PerceiverResampler, SD3TextEmbedder, UNetModel, VisionTower -from .training import LossFunction, RepaLoss, Trainer +from .networks import ( + DCAE, + DDT, + REPA, + Denoiser, + DinoV2, + MMDiT, + PerceiverResampler, + QwenTextEmbedder, + SD3TextEmbedder, + UNetModel, + VisionTower, +) +from .training import BaseTrainer, GRPOTrainer, LossFunction, RepaLoss, Trainer __all__ = [ "BaseDataset", "CIFAR10Dataset", - "ImageNetLatentREPA", + "ImageNetLatent", + "ImageNetNoisyLatent", "MNISTDataset", "Diffuser", "Flow", @@ -16,11 +29,15 @@ "Denoiser", "DinoV2", "MMDiT", + "DDT", "PerceiverResampler", "SD3TextEmbedder", + "QwenTextEmbedder", "UNetModel", "VisionTower", "LossFunction", "RepaLoss", + "BaseTrainer", + "GRPOTrainer", "Trainer", ] diff --git a/src/diffulab/datasets/__init__.py b/src/diffulab/datasets/__init__.py index 35fa822..ef0761e 100644 --- a/src/diffulab/datasets/__init__.py +++ b/src/diffulab/datasets/__init__.py @@ -1,6 +1,6 @@ from .base import BaseDataset from .cifar10 import CIFAR10Dataset -from .imagenet import ImageNetLatentREPA +from .imagenet import ImageNetLatent, ImageNetNoisyLatent from .mnist import MNISTDataset -__all__ = ["BaseDataset", "MNISTDataset", "CIFAR10Dataset", "ImageNetLatentREPA"] +__all__ = ["BaseDataset", "MNISTDataset", "CIFAR10Dataset", "ImageNetLatent", "ImageNetNoisyLatent"] diff --git a/src/diffulab/datasets/base.py b/src/diffulab/datasets/base.py index 2146243..a8e0144 100644 --- a/src/diffulab/datasets/base.py +++ b/src/diffulab/datasets/base.py @@ -7,12 +7,17 @@ from torch import Tensor from torch.utils.data import Dataset -from diffulab.networks.denoisers.common import ModelInput +from diffulab.networks.denoisers.common import ExtraInputGRPO, ModelInput, ModelInputGRPO class BatchData(TypedDict, total=False): model_inputs: Required[ModelInput] - extra: NotRequired[dict[str, Tensor | None]] + extra: NotRequired[dict[str, Tensor | list[str] | None]] + + +class BatchDataGRPO(TypedDict, total=False): + model_inputs: Required[ModelInputGRPO] + extra: Required[ExtraInputGRPO] class BaseDataset(Dataset[BatchData], ABC): diff --git a/src/diffulab/datasets/imagenet.py b/src/diffulab/datasets/imagenet.py index d1c4291..2d6f684 100644 --- a/src/diffulab/datasets/imagenet.py +++ b/src/diffulab/datasets/imagenet.py @@ -1,3 +1,4 @@ +import random from pathlib import Path import torch @@ -8,7 +9,7 @@ from diffulab.datasets.base import BatchData -class ImageNetLatentREPA(Dataset[BatchData]): +class ImageNetLatent(Dataset[BatchData]): """ImageNet dataset for diffusion models with latent and DINOv2 features.""" def __init__( @@ -55,25 +56,61 @@ def __getitem__(self, idx: int) -> BatchData: """ assert self.latent_scale is not None, "Latent scale must be set before getting items" sample = self.dataset[idx] - assert "latent" in sample, "Batch must contain 'latent' key, please precompute the latents before training" - assert "label" in sample, "Batch must contain 'y' key, please add labels to the dataset" + assert "latents" in sample, "Batch must contain 'latent' key, please precompute the latents before training" + latent = torch.tensor(sample["latents"], dtype=torch.float32) + batch_data: BatchData = {"model_inputs": {"x": latent * self.latent_scale}, "extra": {}} - latent = torch.tensor(sample["latent"], dtype=torch.float32) - y = torch.tensor(sample["label"], dtype=torch.long) + if "label" in sample: + y = torch.tensor(sample["label"], dtype=torch.long) + batch_data["model_inputs"]["y"] = y - batch_data: BatchData = { - "model_inputs": {"x": latent * self.latent_scale, "y": y}, - "extra": {}, - } - - x0: torch.Tensor | None = None - dst_features: torch.Tensor | None = None - if "dst_features" not in sample: - assert "image" in sample, "Batch must contain either 'dst_features' or 'image' key" + if "image" in sample: x0 = self.transform(sample["image"]) batch_data["extra"]["x0"] = x0 - else: + + if "dst_features" in sample: dst_features = torch.tensor(sample["dst_features"], dtype=torch.float32) batch_data["extra"]["dst_features"] = dst_features return batch_data + + +class ImageNetNoisyLatent(ImageNetLatent): + """ImageNet dataset for diffusion models with latent and DINOv2 features and added noise.""" + + def __init__( + self, + data_path: str, + local: bool = True, + batch_size: int = 64, + split: str = "train", + noise_tau: float = 0.8, + ) -> None: + """Initialize the MNIST dataset with noise. + + Args: + data_path: Path to the dataset leading to MDS shard files + local: Whether to use local files or remote files + batch_size: Batch size for optimized streaming dataset and future data loading + latent_scale: Scale factor for latent features + split: Dataset split to use (train, val, test) + noise_std: Standard deviation of the Gaussian noise to add to latents + """ + super().__init__(data_path, local, batch_size, split) + self.noise_tau = noise_tau + + def __getitem__(self, idx: int) -> BatchData: + """Get a sample from the dataset with added noise. + Args: + idx: Index of the sample to retrieve + Returns: + BatchData: A dictionary containing model inputs and extra features + """ + batch_data = super().__getitem__(idx) + latent = batch_data["model_inputs"]["x"] + if self.noise_tau > 0: + sigma = abs(random.gauss(0, self.noise_tau)) + noise = torch.randn_like(latent) * sigma + latent = latent + noise + batch_data["model_inputs"]["x"] = latent + return batch_data diff --git a/src/diffulab/datasets/mnist.py b/src/diffulab/datasets/mnist.py index ebec548..d23269b 100644 --- a/src/diffulab/datasets/mnist.py +++ b/src/diffulab/datasets/mnist.py @@ -24,7 +24,12 @@ def __init__(self, data_path: str, train: bool = True): self.images, self.labels = self.load_data() def load_data(self) -> tuple[NDArray[np.float32], NDArray[np.int64]]: - """Load MNIST data from files.""" + """ + Load MNIST data from files. + + Returns: + A tuple of images and labels arrays. + """ if self.train: images_file = self.data_path / "train-images-idx3-ubyte" labels_file = self.data_path / "train-labels-idx1-ubyte" @@ -38,7 +43,13 @@ def load_data(self) -> tuple[NDArray[np.float32], NDArray[np.int64]]: return images, labels def _load_images(self, file: Path) -> NDArray[np.float32]: - """Load and preprocess MNIST images.""" + """ + Load and preprocess MNIST images. + Args: + file: Path to the MNIST images file. + Returns: + A numpy array of shape (num_images, 1, 32, 32) containing the resized images. + """ with open(file, "rb") as f: _, num_images, rows, cols = struct.unpack(">IIII", f.read(16)) images = np.frombuffer(f.read(), dtype=np.uint8).reshape(num_images, 1, rows, cols) @@ -52,12 +63,24 @@ def _load_images(self, file: Path) -> NDArray[np.float32]: return resized_images def _load_labels(self, file: Path) -> NDArray[np.int64]: - """Load MNIST labels.""" + """ + Load MNIST labels. + Args: + file: Path to the MNIST labels file. + Returns: + A numpy array of shape (num_labels,) containing the labels. + """ with open(file, "rb") as f: _, _ = struct.unpack(">II", f.read(8)) labels = np.frombuffer(f.read(), dtype=np.uint8) return labels.astype(np.int64) def preprocess_image(self, image: NDArray[Any]) -> NDArray[np.float32]: - """Normalize the image to [-1, 1] range.""" + """ + Normalize the image to [-1, 1] range. + Args: + image: A numpy array representing the image. + Returns: + A normalized numpy array. + """ return ((image.astype(np.float32) / 255.0) - 0.5) / 0.5 diff --git a/src/diffulab/diffuse/diffuser.py b/src/diffulab/diffuse/diffuser.py index c715cc8..c55b9a8 100644 --- a/src/diffulab/diffuse/diffuser.py +++ b/src/diffulab/diffuse/diffuser.py @@ -5,6 +5,7 @@ from diffulab.diffuse.modelizations.diffusion import Diffusion from diffulab.diffuse.modelizations.flow import Flow from diffulab.diffuse.modelizations.gaussian_diffusion import GaussianDiffusion +from diffulab.diffuse.utils import SamplingOutput from diffulab.networks.denoisers.common import Denoiser, ModelInput from diffulab.networks.vision_towers.common import VisionTower from diffulab.training.losses import LossFunction @@ -23,12 +24,20 @@ class Diffuser: - "rectified_flow": Flow-based diffusion model. - "gaussian_diffusion": Gaussian diffusion model. n_steps (int, optional): Number of diffusion steps. Defaults to 1000. + vision_tower (VisionTower | None, optional): Vision tower for latent diffusion. + If provided, the diffuser will operate in the latent space defined by the vision tower. + Defaults to None. extra_args (dict[str, Any], optional): Additional arguments to pass to the diffusion model. Defaults to {}. + extra_losses (list[LossFunction], optional): Additional loss functions to compute during training. + Defaults to []. Attributes: model_type (str): Type of diffusion model used. denoiser (Denoiser): The neural network model used for denoising. n_steps (int): Number of diffusion steps. + vision_tower (VisionTower | None): Vision tower for latent diffusion. + extra_losses (list[LossFunction]): Additional loss functions for training. + latent_scale (float): Scale factor for the latent space when using a vision tower. diffusion (Diffusion): The diffusion model implementation. Methods: @@ -96,9 +105,11 @@ def draw_timesteps(self, batch_size: int) -> Tensor: def compute_loss( self, model_inputs: ModelInput, - timesteps: Tensor, + timesteps: Tensor | None = None, noise: Tensor | None = None, extra_args: dict[str, Any] = {}, + grpo: bool = False, + grpo_args: dict[str, Any] = {}, ) -> dict[str, Tensor]: """ Compute the loss for the diffusion model using the denoiser and diffusion process. @@ -106,17 +117,28 @@ def compute_loss( diffusion implementation by forwarding the loss computation to the diffusion model. Args: model_inputs (ModelInput): A dictionary containing the model inputs, - including the data tensor keyed as 'x' and any conditional information. + including the data tensor keyed as 'x' and any conditional information. For GRPO, + x is not needed in the model inputs. timesteps (Tensor): A tensor of timesteps for the batch. noise (Tensor | None, optional): Pre-defined noise to add to the input. If None, random noise will be generated. Defaults to None. extra_args (dict[str, Any], optional): Additional arguments for the additional losses computation. + grpo (bool, optional): Whether to compute the GRPO loss. Defaults to False. + grpo_args (dict[str, Any], optional): Additional arguments for GRPO loss computation Returns: dict[str, Tensor]: A dictionary containing the loss value and any additional losses """ + if grpo: + assert isinstance(self.diffusion, Flow), "GRPO loss computation is only available for Flow-based models" + return self.diffusion.compute_loss_grpo( + self.denoiser, + model_inputs, + **grpo_args, + ) + assert timesteps is not None, "timesteps must be provided for loss computation" return self.diffusion.compute_loss(self.denoiser, model_inputs, timesteps, noise, self.extra_losses, extra_args) - def set_steps(self, n_steps: int, **extra_args: dict[str, Any]) -> None: + def set_steps(self, n_steps: int, schedule: str = "linear") -> None: """ Update the number of diffusion steps and related parameters. This method allows changing the number of steps used in the diffusion process @@ -124,43 +146,48 @@ def set_steps(self, n_steps: int, **extra_args: dict[str, Any]) -> None: model's set_steps method. Args: n_steps (int): The new number of diffusion steps to use. - **extra_args (dict[str, Any]): Additional arguments to pass to the diffusion - model's set_steps method. These may include parameters like 'schedule' - or 'section_counts' depending on the diffusion model implementation. + schedule (str, optional): The schedule to use for the timesteps. Defaults to "linear". Example: ``` diffuser = Diffuser(denoiser, sampling_method="ddpm", n_steps=1000) # Later, change to use fewer steps for faster sampling - diffuser.set_steps(100, schedule="ddim") + diffuser.set_steps(100, schedule="linear") ``` """ - self.diffusion.set_steps(n_steps, **extra_args) # type: ignore + self.diffusion.set_steps(n_steps, schedule=schedule) def generate( self, - data_shape: tuple[int, ...], model_inputs: ModelInput, + data_shape: tuple[int, ...] | None = None, use_tqdm: bool = True, clamp_x: bool = False, guidance_scale: float = 0, - **kwargs: dict[str, Any], - ) -> Tensor: + sampler_args: dict[str, Any] = {}, + return_intermediates: bool = False, + return_latents: bool = False, + ) -> SamplingOutput: """ Generates a new sample using the diffusion model. This method delegates to the underlying diffusion model's denoise method to generate a sample from the diffusion process. It can handle conditional generation with guidance when appropriate settings are provided. Args: - data_shape (tuple[int, ...]): Shape of the data to generate, typically (batch_size, channels, height, width). - If a vision tower is used, this should be the shape of the latent space. model_inputs (ModelInput): A dictionary containing inputs for the model, such as initial noise, conditional information, or labels. If 'x' is not provided, random noise will be generated. + data_shape (tuple[int, ...], optional): The shape of the data to generate. If a vision tower is used, + this shape should correspond to the latent space shape. + Required if 'x' is not in model_inputs. Defaults to None. use_tqdm (bool, optional): Whether to display a progress bar during generation. Defaults to True. - clamp_x (bool, optional): Whether to clamp the generated values to [-1, 1] range. Defaults to False. + clamp_x (bool, optional): Whether to clamp the tensor generated to [-1, 1] range (before eventual decoding). + Defaults to False. guidance_scale (float, optional): Scale for classifier or classifier-free guidance. Values greater than 0 enable guidance. Defaults to 0. - **kwargs (dict[str, Any]): Additional arguments to pass to the diffusion model's denoise method. - These may include parameters like 'classifier', 'classifier_free', etc. + sampler_args (dict[str, Any], optional): Additional arguments to pass to the sampler. Defaults to {}. + return_intermediates (bool, optional): Whether to return intermediate samples and extra sampler output + during the denoising process (mean, std etc...). Defaults to False. + return_latents (bool, optional): Whether to return the latent representation when using a + vision tower instead of decoded data. Defaults to False. Returns: Tensor: The generated data tensor. Example: @@ -177,24 +204,27 @@ def generate( ``` """ if self.vision_tower: - z = self.diffusion.denoise( + sampling_output = self.diffusion.denoise( self.denoiser, - data_shape, - model_inputs, + model_inputs=model_inputs, + data_shape=data_shape, use_tqdm=use_tqdm, clamp_x=clamp_x, guidance_scale=guidance_scale, - **kwargs, + sampler_args=sampler_args, + return_intermediates=return_intermediates, ) - z = z / self.latent_scale - return self.vision_tower.decode(z) + if not return_latents: + sampling_output["x"] = self.vision_tower.decode(sampling_output["x"] / self.latent_scale) + return sampling_output return self.diffusion.denoise( self.denoiser, - data_shape, - model_inputs, + model_inputs=model_inputs, + data_shape=data_shape, use_tqdm=use_tqdm, clamp_x=clamp_x, guidance_scale=guidance_scale, - **kwargs, + sampler_args=sampler_args, + return_intermediates=return_intermediates, ) diff --git a/src/diffulab/diffuse/modelizations/diffusion.py b/src/diffulab/diffuse/modelizations/diffusion.py index 48b74b8..332e2b1 100644 --- a/src/diffulab/diffuse/modelizations/diffusion.py +++ b/src/diffulab/diffuse/modelizations/diffusion.py @@ -3,6 +3,9 @@ from torch import Tensor +from diffulab.diffuse.samplers import StepResult +from diffulab.diffuse.samplers.common import Sampler +from diffulab.diffuse.utils import SamplingOutput from diffulab.networks.denoisers.common import Denoiser, ModelInput from diffulab.training.losses import LossFunction @@ -19,11 +22,17 @@ class Diffusion(ABC): Defaults to "euler". schedule (str, optional): Schedule for time discretization in the diffusion process. Defaults to "linear". + latent_diffusion (bool, optional): Whether the diffusion operates in a latent space. + Defaults to False. + sampler_parameters (dict[str, Any], optional): Additional parameters for the sampler. + Defaults to an empty dictionary. Attributes: timesteps (list[float]): List of timestep values for the diffusion process. steps (int): Number of steps in the diffusion process. sampling_method (str): Method used for sampling/reverse process. schedule (str): Schedule type used for timestep spacing. + latent_diffusion (bool): Whether the diffusion operates in a latent space. + sampler (Sampler): The sampler instance used for the reverse diffusion process. Methods: set_steps: Configure the timestep sequence for the diffusion process. one_step_denoise: Perform a single step of the reverse diffusion process. @@ -33,20 +42,26 @@ class Diffusion(ABC): draw_timesteps: Sample random timesteps for training. """ + sampler_registry: dict[str, type[Sampler]] + def __init__( self, n_steps: int, sampling_method: str = "euler", schedule: str = "linear", latent_diffusion: bool = False, - **kwargs: Any, + sampler_parameters: dict[str, Any] = {}, ): + assert sampling_method in self.sampler_registry, ( + f"Unknown sampling method '{sampling_method}'. Available methods: {list(self.sampler_registry.keys())}" + ) + self.sampler = self.sampler_registry[sampling_method](**sampler_parameters) self.timesteps: list[float] = [] self.steps: int = n_steps self.sampling_method = sampling_method self.schedule = schedule self.latent_diffusion = latent_diffusion - self.set_steps(n_steps, schedule=schedule, **kwargs) + self.set_steps(n_steps, schedule=schedule) @abstractmethod def set_steps(self, n_steps: int, schedule: str) -> None: @@ -70,7 +85,7 @@ def one_step_denoise( guidance_scale: float, *args: Any, **kwargs: Any, - ) -> Tensor: + ) -> StepResult: """ Perform a single step of the reverse diffusion process. This method implements a single step of denoising in the reverse diffusion process, @@ -85,7 +100,8 @@ def one_step_denoise( *args (Any): Additional positional arguments specific to the diffusion implementation. **kwargs (Any): Additional keyword arguments specific to the diffusion implementation. Returns: - Tensor: The updated data tensor after one step of denoising. + StepResult: An object containing the results of the denoising step, which may + include the updated data tensor and any intermediate values. Note: The specific implementation details, such as how the model prediction is used to update the state, depend on the concrete diffusion model subclass. @@ -152,29 +168,56 @@ def add_noise(self, x: Tensor, timesteps: Tensor, noise: Tensor | None = None) - def denoise( self, model: Denoiser, - data_shape: tuple[int, ...], model_inputs: ModelInput, + data_shape: tuple[int, ...] | None = None, use_tqdm: bool = True, clamp_x: bool = False, guidance_scale: float = 0, - ) -> Tensor: + sampler_args: dict[str, Any] = {}, + return_intermediates: bool = False, + ) -> SamplingOutput: """ Generate samples by running the reverse diffusion process. This method implements the complete reverse diffusion process to generate new samples, starting from random noise and iteratively denoising until reaching the final output. Args: model (Denoiser): The neural network model used for denoising. - data_shape (tuple[int, ...]): Shape of data to generate (batch_size, channels, height, width). model_inputs (ModelInput): A dictionary containing model inputs, such as initial noise or conditional information. If 'x' is not provided, random noise will be generated. + data_shape (tuple[int, ...] | None): Shape of data to generate (batch_size, channels, height, width). + if x is provided in model_inputs, data_shape can be set to None. + Defaults to None. use_tqdm (bool, optional): Whether to show a progress bar during generation. Defaults to True. clamp_x (bool, optional): Whether to clamp output values to [-1, 1] range. Defaults to False. guidance_scale (float, optional): Scale factor for classifier or classifier-free guidance. Values greater than 0 enable guidance. Defaults to 0. + sampler_args (dict[str, Any], optional): Additional arguments specific to the sampling method. + Defaults to an empty dictionary. + return_intermediates (bool, optional): Whether to return intermediate results at each step. + Defaults to False. Returns: - Tensor: The generated data tensor after completing the reverse diffusion process. + SamplingOutput: A dictionary that may contain (depending on sampler and return_intermediates option) + the following keys: + - x (Tensor): The final generated sample tensor. + - xt (Tensor, optional): If `return_intermediates` is True, a tensor of shape + (batch_size, steps+1, ...) containing the sample at each timestep. + - estimated_x0 (Tensor, optional): If `return_intermediates` is True, a tensor + of shape (batch_size, steps, ...) containing the estimated original data at + each timestep. + - xt_mean (Tensor, optional): If `return_intermediates` is True and the sampler + provides mean estimates, a tensor of shape (batch_size, steps, ...) containing + the mean at each timestep. + - xt_std (Tensor, optional): If `return_intermediates` is True and the sampler + provides std estimates, a tensor of shape (batch_size, steps, ...) containing + the standard deviation at each timestep. + - logprob (Tensor, optional): If `return_intermediates` is True and the sampler + provides log probabilities, a tensor of shape (batch_size, steps, ...) containing + the log probabilities at each timestep. + Note: + The specific implementation details depend on the concrete diffusion model subclass. + Different samplers may provide different intermediate outputs. """ pass @@ -195,8 +238,7 @@ def draw_timesteps(self, batch_size: int) -> Tensor: Note: Different diffusion model implementations may use different distributions for sampling timesteps. For example: - - Gaussian diffusion typically samples integers from [0, num_diffusion_steps-1] + - Discrete gaussian diffusion typically samples integers from [0, num_diffusion_steps-1] - Flow-based diffusion typically samples continuous values from [0, 1] """ - pass diff --git a/src/diffulab/diffuse/modelizations/flow.py b/src/diffulab/diffuse/modelizations/flow.py index 9608450..6778cf2 100644 --- a/src/diffulab/diffuse/modelizations/flow.py +++ b/src/diffulab/diffuse/modelizations/flow.py @@ -1,3 +1,4 @@ +import random from typing import Any, cast import torch @@ -5,6 +6,9 @@ from tqdm import tqdm from diffulab.diffuse.modelizations.diffusion import Diffusion +from diffulab.diffuse.samplers import StepResult +from diffulab.diffuse.samplers.flow import Euler, EulerMaruyama +from diffulab.diffuse.utils import SamplingOutput from diffulab.networks.denoisers.common import Denoiser, ModelInput, ModelOutput from diffulab.training.losses.common import LossFunction @@ -17,31 +21,43 @@ class Flow(Diffusion): transformation defined by a neural network. Args: n_steps (int, optional): Number of steps for the diffusion process. Defaults to 50. - sampling_method (str, optional): Method used for sampling. Currently only "euler" - is supported. Defaults to "euler". + sampling_method (str, optional): Method used for sampling. Defaults to "euler". schedule (str, optional): Schedule for time discretization. Currently only "linear" is supported. Defaults to "linear". + latent_diffusion (bool, optional): Whether the diffusion operates in a latent space. + Defaults to False. logits_normal (bool, optional): Whether to use logit-normal distribution for timestep sampling during training. When True, timesteps are drawn from a sigmoid-transformed normal distribution. Defaults to False. + sampler_parameters (dict[str, Any], optional): Additional parameters for the sampler. + Defaults to an empty dictionary. + alpha_t (float, optional): Factor for shifting timestep strategy during training. + Methods: set_steps(n_steps, schedule): Sets the number of timesteps and their spacing. at(timesteps): Computes the "a(t)" coefficient for noise mixing. bt(timesteps): Computes the "b(t)" coefficient for noise mixing. draw_timesteps(batch_size): Samples random timesteps for training. get_v(model, model_inputs, t_curr): Computes the velocity field at current time. - one_step_denoise(model, model_inputs, t_prev, t_curr, guidance_scale): Performs one step + one_step_denoise(model, model_inputs, t_prev, t_curr, guidance_scale, sampler_args): Performs one step of the reverse diffusion process. - compute_loss(model, model_inputs, timesteps, noise): Computes the loss for training. + compute_loss(model, model_inputs, timesteps, noise, extra_losses, extra_args): Computes the loss for training. + compute_loss_grpo(model, model_inputs, sampling, advantages, kl_beta, eps, timestep_fraction, guidance_scale): + Computes the GRPO loss for reinforcement learning. add_noise(x, timesteps, noise): Adds noise to the input according to the timesteps. - denoise(model, data_shape, model_inputs, use_tqdm, clamp_x, guidance_scale): Generates - samples by running the reverse diffusion process. + denoise(model, data_shape, model_inputs, use_tqdm, clamp_x, guidance_scale, sampler_args, return_intermediates): + Generates samples by running the reverse diffusion process. References: Lipman, Y., et al. (2022). "Flow Matching for Generative Modeling." https://arxiv.org/abs/2210.02747 """ + sampler_registry = { + "euler": Euler, + "euler_maruyama": EulerMaruyama, + } + def __init__( self, n_steps: int = 50, @@ -49,11 +65,20 @@ def __init__( schedule: str = "linear", latent_diffusion: bool = False, logits_normal: bool = False, + sampler_parameters: dict[str, Any] = {}, + alpha_t: float = 1, ) -> None: super().__init__( - n_steps=n_steps, sampling_method=sampling_method, schedule=schedule, latent_diffusion=latent_diffusion + n_steps=n_steps, + sampling_method=sampling_method, + schedule=schedule, + latent_diffusion=latent_diffusion, + sampler_parameters=sampler_parameters, ) self.logits_normal = logits_normal + if alpha_t != 1: + assert not logits_normal, "alpha_t setting is not compatible with logits normal sampling" + self.alpha_t = alpha_t def set_steps(self, n_steps: int, schedule: str = "linear") -> None: """ @@ -85,6 +110,8 @@ def set_steps(self, n_steps: int, schedule: str = "linear") -> None: else: raise NotImplementedError("Only linear schedule is supported for the moment") + self.sampler.set_steps(self.timesteps) + def at(self, timesteps: Tensor) -> Tensor: """ Computes the coefficient a(t) for the flow model equation xt = a(t) * x0 + b(t) * eps. @@ -100,7 +127,7 @@ def at(self, timesteps: Tensor) -> Tensor: evaluated as (1 - timesteps). """ - return 1 - timesteps + return torch.ones_like(timesteps) - timesteps def bt(self, timesteps: Tensor) -> Tensor: """ @@ -129,14 +156,14 @@ def draw_timesteps(self, batch_size: int) -> Tensor: Tensor: A tensor of shape (batch_size,) containing timestep values in the range [0, 1]. If `logits_normal` is False, timesteps are drawn from a uniform distribution. If `logits_normal` is True, timesteps are drawn from a sigmoid-transformed - normal distribution, which concentrates more samples near 0 and 1. + normal distribution which concentrates samples around 0.5. """ if self.logits_normal: nt = torch.randn((batch_size), dtype=torch.float32) return torch.sigmoid(nt) - - return torch.rand((batch_size), dtype=torch.float32) + nt = torch.rand((batch_size), dtype=torch.float32) + return (self.alpha_t * nt) / (1 + (self.alpha_t - 1) * nt) def get_v(self, model: Denoiser, model_inputs: ModelInput, t_curr: float) -> Tensor: """ @@ -166,7 +193,8 @@ def one_step_denoise( t_prev: float, t_curr: float, guidance_scale: float, - ) -> Tensor: + sampler_args: dict[str, Any] = {}, + ) -> StepResult: """ Performs one step of denoising in the reverse diffusion process. This method implements a single step of the reverse flow-based diffusion process, @@ -182,6 +210,8 @@ def one_step_denoise( guidance_scale (float): Scale factor for classifier-free guidance. If greater than 0, the method computes both conditional and unconditional predictions and interpolates between them, with higher values emphasizing the conditional prediction. + sampler_args (dict[str, Any], optional): Additional arguments to pass to the sampler's + step method. Defaults to an empty dictionary. Returns: Tensor: The updated state tensor after one step of the reverse diffusion process. Note: @@ -193,11 +223,7 @@ def one_step_denoise( if guidance_scale > 0: v_dropped = self.get_v(model, {**model_inputs, "p": 1}, t_curr) v = v_dropped + guidance_scale * (v - v_dropped) - if self.sampling_method == "euler": - x_t_minus_one: Tensor = model_inputs["x"] - v * (t_curr - t_prev) - else: # different methods to be implemented maybe in the generic class instead - raise NotImplementedError - return x_t_minus_one + return self.sampler.step(model_inputs["x"], v, t_curr, t_prev, **sampler_args) def compute_loss( self, @@ -250,6 +276,71 @@ def compute_loss( loss_dict[extra_loss.name] = e_loss return loss_dict + def compute_loss_grpo( + self, + model: Denoiser, + model_inputs: ModelInput, + sampling: SamplingOutput, + advantages: Tensor, + kl_beta: float = 0, + eps: float = 1e-4, + timestep_fraction: float = 0.6, + guidance_scale: float = 4, + ) -> dict[str, Tensor]: + """ + Computes the Preference Reward-based GRPO loss for preference alignment with flow-based diffusion models. + reference : https://arxiv.org/abs/2508.20751. The sampler needs to be set to Euler-Maruyama for GRPO. + Args: + model (Denoiser): The neural network model used for denoising. + model_inputs (ModelInput): A dictionary containing the model inputs, including the current state tensor + keyed as 'x' and any conditional information. + sampling (SamplingOutput): The output from the sampling process, containing intermediate samples + transitions distribution related parameters + advantages (Tensor): A tensor of shape (batch_size,) containing the advantage values for each sample in the batch. + kl_beta (float, optional): Coefficient for the KL divergence term in the loss. Defaults to 0. + eps (float, optional): Clipping parameter for the policy loss. Defaults to 1e-4. + timestep_fraction (float, optional): Fraction of timesteps to consider for loss computation. Defaults to 0.6. + guidance_scale (float, optional): Scale for classifier-free guidance during denoising. Defaults to 4. + Returns: + dict[str, Tensor]: A dictionary containing the computed GRPO loss. + """ + assert isinstance(self.sampler, EulerMaruyama), "GRPO only works with the Euler-Maruyama sampler" + assert "xt" in sampling, "sampling output should contain all intermediate samples" + assert "logprob" in sampling, "sampling output should contain all logprobs" + assert "xt_mean" in sampling, "sampling output should contain all intermediate means" + + indices = random.sample(range(0, self.steps), k=round(self.steps * timestep_fraction)) + losses: list[Tensor] = [] + for idx in indices: + model_inputs["x"] = sampling["xt"][:, idx] + step_result = self.one_step_denoise( + model=model, + model_inputs=cast(ModelInput, model_inputs), # We added "x" to model_inputs + t_curr=self.timesteps[idx], + t_prev=self.timesteps[idx + 1], + guidance_scale=guidance_scale, + sampler_args={"x_prev": sampling["xt"][:, idx + 1]}, + ) + + assert "logprob" in step_result, "logprob should be returned by the sampler" + assert "x_prev_mean" in step_result, "x_prev_mean should be returned by the sampler" + assert "x_prev_std" in step_result, "std should be returned by the GRPO sampling output" + + prob_ratios = torch.exp(step_result["logprob"] - sampling["logprob"][:, idx]) + unclipped_objective = advantages * prob_ratios + clipped_objective = advantages * torch.clamp(prob_ratios, 1 - eps, 1 + eps) + policy_loss = -torch.min(unclipped_objective, clipped_objective).mean() + + diff = (step_result["x_prev_mean"] - sampling["xt_mean"][:, idx]) ** 2 + kl_loss = diff.mean(dim=tuple(range(1, diff.dim()))) / (2 * step_result["x_prev_std"] ** 2) + kl_loss = kl_loss.mean() + + step_loss = policy_loss + kl_beta * kl_loss + losses.append(step_loss) + + loss = torch.stack(losses).mean() + return {"loss": loss} + def add_noise(self, x: Tensor, timesteps: Tensor, noise: Tensor | None = None) -> tuple[Tensor, Tensor]: """ Adds noise to the input data according to the flow diffusion process. @@ -282,12 +373,14 @@ def add_noise(self, x: Tensor, timesteps: Tensor, noise: Tensor | None = None) - def denoise( self, model: Denoiser, - data_shape: tuple[int, ...], model_inputs: ModelInput, + data_shape: tuple[int, ...] | None = None, use_tqdm: bool = True, clamp_x: bool = False, guidance_scale: float = 0, - ) -> Tensor: + sampler_args: dict[str, Any] = {}, + return_intermediates: bool = False, + ) -> SamplingOutput: """ Generates samples by running the reverse diffusion process. This method implements the sample generation process for flow-based diffusion models @@ -296,11 +389,12 @@ def denoise( by the velocity field predicted by the neural network. Args: model (Denoiser): The neural network model used for denoising. - data_shape (tuple[int, ...]): Shape of the data to generate, typically - (batch_size, channels, height, width) for images. model_inputs (ModelInput): A dictionary containing inputs for the model, such as conditional information or labels. The function will update this dictionary with the current sample state during generation. + data_shape (tuple[int, ...] | None): Shape of the data to generate, typically + (batch_size, channels, height, width) for images. Needed if 'x' is not in model_inputs. + Defaults to None. use_tqdm (bool, optional): Whether to display a progress bar during generation. Defaults to True. clamp_x (bool, optional): Whether to clamp the generated values to [-1, 1] range. @@ -308,8 +402,27 @@ def denoise( guidance_scale (float, optional): Scale for classifier-free guidance. Values greater than 0 enable guidance, with higher values giving stronger adherence to the conditioning. Defaults to 0. + sampler_args (dict[str, Any], optional): Additional arguments to pass to the sampler's + step method. Defaults to an empty dictionary. + return_intermediates (bool, optional): Whether to return intermediate results at each step. + Defaults to False. Returns: - Tensor: The generated data tensor. + SamplingOutput: A dictionary containing: + - x (Tensor): The final generated sample tensor. + - xt (Tensor, optional): If `return_intermediates` is True, a tensor of shape + (batch_size, steps+1, ...) containing the sample at each timestep. + - estimated_x0 (Tensor, optional): If `return_intermediates` is True, a tensor + of shape (batch_size, steps, ...) containing the estimated original data at + each timestep. + - xt_mean (Tensor, optional): If `return_intermediates` is True and the sampler + provides mean estimates, a tensor of shape (batch_size, steps, ...) containing + the mean at each timestep. + - xt_std (Tensor, optional): If `return_intermediates` is True and the sampler + provides std estimates, a tensor of shape (batch_size, steps, ...) containing + the standard deviation at each timestep. + - logprob (Tensor, optional): If `return_intermediates` is True and the sampler + provides log probabilities, a tensor of shape (batch_size, steps, ...) containing + the log probabilities at each timestep. Note: The function works by starting with random noise and iteratively updating it using the velocity field predicted by the model at each timestep. The process @@ -319,8 +432,17 @@ def denoise( """ device = next(model.parameters()).device dtype = next(model.parameters()).dtype + if "x" not in model_inputs: + assert data_shape is not None, "'data_shape' must be provided if 'x' is not in model_inputs" model_inputs["x"] = torch.randn(data_shape, device=device, dtype=dtype) + + all_x0: list[Tensor] = [] + all_xt: list[Tensor] = [model_inputs["x"]] + all_xt_mean: list[Tensor] = [] + all_xt_std: list[Tensor] = [] + all_logprobs: list[Tensor] = [] + for t_curr, t_prev in tqdm( zip(self.timesteps[:-1], self.timesteps[1:]), desc="generating image", @@ -328,13 +450,37 @@ def denoise( disable=not use_tqdm, leave=False, ): - model_inputs["x"] = self.one_step_denoise( + step_output = self.one_step_denoise( model, model_inputs, t_curr=t_curr, t_prev=t_prev, guidance_scale=guidance_scale, + sampler_args=sampler_args, ) + model_inputs["x"] = step_output["x_prev"] + if return_intermediates: + all_xt.append(step_output["x_prev"]) + all_x0.append(step_output["estimated_x0"]) + if "x_prev_mean" in step_output: + all_xt_mean.append(step_output["x_prev_mean"]) + if "x_prev_std" in step_output: + all_xt_std.append(step_output["x_prev_std"]) + if "logprob" in step_output: + all_logprobs.append(step_output["logprob"]) + if clamp_x: model_inputs["x"] = model_inputs["x"].clamp(-1, 1) - return model_inputs["x"] + + out: SamplingOutput = {"x": model_inputs["x"]} + if return_intermediates: + out["xt"] = torch.stack(all_xt, dim=1) + out["estimated_x0"] = torch.stack(all_x0, dim=1) + if len(all_xt_mean) > 0: + out["xt_mean"] = torch.stack(all_xt_mean, dim=1) + if len(all_xt_std) > 0: + out["xt_std"] = torch.stack(all_xt_std, dim=0) + if len(all_logprobs) > 0: + out["logprob"] = torch.stack(all_logprobs, dim=1) + + return out diff --git a/src/diffulab/diffuse/modelizations/gaussian_diffusion.py b/src/diffulab/diffuse/modelizations/gaussian_diffusion.py index a2ed1f7..77ca509 100644 --- a/src/diffulab/diffuse/modelizations/gaussian_diffusion.py +++ b/src/diffulab/diffuse/modelizations/gaussian_diffusion.py @@ -1,4 +1,3 @@ -import enum import math from typing import Any, Callable, cast @@ -9,24 +8,13 @@ from diffulab.diffuse.modelizations.diffusion import Diffusion from diffulab.diffuse.modelizations.utils import space_timesteps -from diffulab.diffuse.utils import extract_into_tensor +from diffulab.diffuse.samplers.common import StepResult +from diffulab.diffuse.samplers.gaussian_diffusion import DDIM, DDPM +from diffulab.diffuse.utils import SamplingOutput, extract_into_tensor from diffulab.networks.denoisers.common import Denoiser, ModelInput from diffulab.training.losses.common import LossFunction -class MeanType(enum.Enum): - EPSILON = "epsilon" - XSTART = "xstart" - XPREV = "xprev" - - -class ModelVarType(enum.Enum): - LEARNED = "learned" - FIXED_SMALL = "fixed_small" - FIXED_LARGE = "fixed_large" - LEARNED_RANGE = "learned_range" - - class GaussianDiffusion(Diffusion): """ Gaussian Diffusion model implementation. @@ -37,35 +25,15 @@ class GaussianDiffusion(Diffusion): https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/gaussian_diffusion.py under MIT LICENSE as of 2025-03-02 Args: - n_steps (int, optional): Number of diffusion steps. Default is 1000. + n_steps (int, optional): Number of diffusion steps. Default is 1000. Set to match training steps. sampling_method (str, optional): Method used for sampling. Currently supports "ddpm" (standard diffusion) and "ddim" (deterministic diffusion). Default is "ddpm". schedule (str, optional): Noise schedule to use. Options include "linear" and "cosine". Default is "linear". - mean_type (str, optional): Type of parameterization used for the model's output. - Options include: - - "epsilon": Model predicts the noise added. - - "xstart": Model predicts the clean data directly. - - "xprev": Model predicts the previous timestep. - Default is "epsilon". - variance_type (str, optional): Type of variance computation to use. Options include: - - "fixed_small": Use the exact posterior variance. - - "fixed_large": Use larger variance - - "learned": Model learns the variance. - - "learned_range": Model learns interpolation between min/max variance. - Default is "fixed_small". - Attributes: - mean_type (str): The selected mean parameterization type. - var_type (str): The selected variance type. - training_steps (int): Number of steps used for training. - timestep_map (list[int]): Mapping from sampling steps to training steps when using - fewer sampling steps than training steps. - betas (Tensor): Beta schedule for noise levels. - alphas (Tensor): 1 - betas. - alphas_bar (Tensor): Cumulative product of alphas. - sqrt_alphas_bar (Tensor): Square root of alphas_bar. - posterior_variance (Tensor): Variance of the posterior distribution. - posterior_log_variance_clipped (Tensor): Log of the posterior variance, clipped for numerical stability. + latent_diffusion (bool, optional): Whether the diffusion operates in a latent space. + Defaults to False. + sampler_parameters (dict[str, Any], optional): Additional parameters for the sampler. + Defaults to an empty dictionary. Methods: set_steps(n_steps, schedule, section_counts): Sets the number and spacing of diffusion steps. draw_timesteps(batch_size): Samples random timesteps for training. @@ -75,55 +43,46 @@ class GaussianDiffusion(Diffusion): add_noise(x, timesteps, noise): Adds noise to inputs according to the forward process. """ + sampler_registry = { + "ddpm": DDPM, + "ddim": DDIM, + } + def __init__( self, n_steps: int = 1000, sampling_method: str = "ddpm", schedule: str = "linear", latent_diffusion: bool = False, - mean_type: str = "epsilon", - variance_type: str = "fixed_small", + sampler_parameters: dict[str, Any] = {}, ): - if mean_type not in MeanType._value2member_map_: - raise ValueError(f"mean_type must be one of {[e.value for e in MeanType]}") - if variance_type not in ModelVarType._value2member_map_: - raise ValueError(f"variance_type must be one of {[e.value for e in ModelVarType]}") if sampling_method not in ["ddpm", "ddim"]: raise ValueError("sampling method must be one of ['ddpm', 'ddim']") - self.mean_type = mean_type - self.var_type = variance_type self.training_steps = n_steps super().__init__( - n_steps=n_steps, sampling_method=sampling_method, schedule=schedule, latent_diffusion=latent_diffusion + n_steps=self.training_steps, + sampling_method=sampling_method, + schedule=schedule, + latent_diffusion=latent_diffusion, + sampler_parameters=sampler_parameters, ) def set_diffusion_parameters(self, betas: Tensor) -> None: """ - Sets up the diffusion parameters for the Gaussian diffusion process. - This method computes various coefficients and parameters needed for the diffusion process - after the noise schedule (betas) has been defined. It calculates alpha values, their - cumulative products, and various coefficients needed for the forward and reverse processes. + Sets the diffusion parameters based on the provided beta schedule. + This method initializes the diffusion process parameters, including alphas and their + cumulative products, based on the given beta values. It also configures the sampler + to use this betas schedule. Args: - betas (Tensor): The noise schedule tensor defining beta values for each timestep - in the diffusion process. + betas (Tensor): A 1D tensor containing beta values for each timestep, + with shape (n_steps,). """ self.betas = betas - self.alphas = 1 - self.betas + self.alphas = torch.ones_like(self.betas) - self.betas self.alphas_bar = self.alphas.cumprod(dim=0) - self.alphas_bar_prev = torch.cat([torch.tensor([1.0], dtype=torch.float64), self.alphas_bar[:-1]]) - self.alphas_bar_next = torch.cat([self.alphas_bar[1:], torch.tensor([0.0], dtype=torch.float64)]) - - # utils for computation self.sqrt_alphas_bar = self.alphas_bar.sqrt() - self.posterior_variance = self.betas * (1.0 - self.alphas_bar_prev) / (1.0 - self.alphas_bar) - # log calculation clipped because the posterior variance is 0 at the - # beginning of the diffusion chain. - self.posterior_log_variance_clipped = torch.log( - torch.cat([self.posterior_variance[1:2], self.posterior_variance[1:]]) - ) - self.posterior_mean_coef1 = self.betas * (self.alphas_bar_prev).sqrt() / (1.0 - self.alphas_bar) - self.posterior_mean_coef2 = (1.0 - self.alphas_bar_prev) * self.alphas.sqrt() / (1.0 - self.alphas_bar) + self.sampler.set_steps(betas) def set_steps( self, @@ -168,7 +127,7 @@ def set_steps( new_betas: list[Tensor] = [] for i, alpha_bar in enumerate(self.alphas_bar): if i in timesteps_to_use: - new_betas.append(1 - alpha_bar / last_alpha_bar) + new_betas.append(torch.ones_like(alpha_bar) - alpha_bar / last_alpha_bar) last_alpha_bar = alpha_bar self.timestep_map.append(i) self.set_diffusion_parameters(torch.tensor(new_betas)) @@ -234,251 +193,6 @@ def _betas_for_alpha_bar( betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) return torch.tensor(betas, dtype=torch.float64, requires_grad=False) - def _get_x_start_from_x_prev(self, x_prev: Tensor, x: Tensor, t: Tensor) -> Tensor: - """ - Computes the initial state (x_0) given the previous state (x_{t-1}) and current state (x_t). - It uses precomputed coefficients to solve for x_0 based on the Gaussian diffusion model's - posterior distribution parameters. - Args: - x_prev (Tensor): The state at the previous timestep (x_{t-1}). - x (Tensor): The state at the current timestep (x_t). - t (Tensor): The current timestep indices as a tensor. - Returns: - Tensor: The inferred initial clean state (x_0) based on the provided states. - """ - x_start = (1.0 / extract_into_tensor(self.posterior_mean_coef1, t, x_prev.shape)) * x_prev + ( - 1.0 / extract_into_tensor(self.posterior_mean_coef2, t, x.shape) - ) * x - return x_start - - def _get_x_start_from_eps(self, eps: Tensor, x: Tensor, t: Tensor) -> Tensor: - """ - Computes the initial state (x_0) from the current state (x_t) and the predicted noise (epsilon). - This method uses the diffusion model's forward process parameters to invert the noise - addition and recover the original clean data, given the current noisy state and the - predicted noise component. - Args: - eps (Tensor): The predicted noise component (epsilon). - x (Tensor): The current noisy state at timestep t (x_t). - t (Tensor): The current timestep indices as a tensor. - Returns: - Tensor: The inferred initial clean state (x_0) based on the current state and predicted noise. - """ - - x_start = (1.0 / extract_into_tensor(self.sqrt_alphas_bar, t, x.shape)) * x - ( - (1.0 - extract_into_tensor(self.alphas_bar, t, eps.shape)).sqrt() - / extract_into_tensor(self.sqrt_alphas_bar, t, x.shape) - ) * eps - return x_start - - def _get_mean_from_x_start(self, x: Tensor, x_start: Tensor, t: Tensor) -> Tensor: - """ - Computes the mean of the posterior distribution q(x_{t-1} | x_t, x_0) given the current state and inferred initial state. - This function calculates the mean of the posterior distribution using pre-computed coefficients that - depend on the noise schedule. The posterior mean is used during the reverse diffusion process to sample - from q(x_{t-1} | x_t, x_0). - Args: - x (Tensor): The current state at timestep t (x_t). - x_start (Tensor): The inferred or predicted initial clean state (x_0). - t (Tensor): The current timestep indices as a tensor. - Returns: - Tensor: The mean of the posterior distribution q(x_{t-1} | x_t, x_0). - """ - - mean = ( - extract_into_tensor(self.posterior_mean_coef1, t, x_start.shape) * x_start - + extract_into_tensor(self.posterior_mean_coef2, t, x.shape) * x - ) - return mean - - def _get_x_prev_from_mean_var(self, mean: Tensor, log_var: Tensor, t: Tensor) -> Tensor: - """ - Computes the previous state (x_{t-1}) from the posterior mean and variance. - This method samples the previous state from the posterior distribution p(x_{t-1}|x_t) - using the provided mean and variance parameters. For all timesteps except the final one (t=0), - it adds random Gaussian noise scaled by the square root of the variance. For the final timestep, - it returns the mean without adding noise. - Args: - mean (Tensor): The posterior mean of p(x_{t-1}|x_t). - var (Tensor): The posterior variance of p(x_{t-1}|x_t). - t (Tensor): The current timestep indices as a tensor. - Returns: - Tensor: The sampled previous state (x_{t-1}) from the posterior distribution. - """ - # Create a mask where t > 0 is True, and t == 0 is False - t_mask = (t > 0).float().view(-1, *([1] * (mean.dim() - 1))) - - # Generate noise with the same shape as mean - noise = torch.randn_like(mean) - - # Apply noise only where t > 0, using the mask to conditionally add noise - return mean + t_mask * noise * torch.exp(0.5 * log_var) - - def _get_eps_from_xstart(self, x_start: Tensor, x: Tensor, t: Tensor) -> Tensor: - """ - Computes the noise component (epsilon) from the initial state and current state. - This method is the inverse of _get_x_start_from_eps, calculating the noise that would - have been added to x_start to produce the current noisy state x at timestep t. - Args: - x_start (Tensor): The inferred or known initial clean state (x_0). - x (Tensor): The current noisy state at timestep t (x_t). - t (Tensor): The current timestep indices as a tensor. - Returns: - Tensor: The noise component (epsilon) that was added to x_start to produce x. - """ - eps = (1.0 / (1 - extract_into_tensor(self.alphas_bar, t, x.shape)).sqrt()) * ( - x - extract_into_tensor(self.sqrt_alphas_bar, t, x_start.shape) * x_start - ) - return eps - - def _get_p_mean_var( - self, prediction: Tensor, x: Tensor, t: Tensor, clamp_x: bool = False - ) -> tuple[Tensor, Tensor, Tensor, Tensor]: - """ - Computes the posterior mean and variance for the reverse diffusion step. - This core method calculates the parameters of the posterior distribution - p(x_{t-1} | x_t) used in the reverse diffusion process. It processes the model's - prediction and current noisy state to extract the mean, variance, log variance, - and the predicted clean state. - Args: - prediction (Tensor): The raw output from the denoising model. Could be noise prediction, - x_start prediction, or x_prev prediction depending on self.mean_type. - x (Tensor): The current noisy state at timestep t (x_t). - t (Tensor): The current timestep indices as a tensor. - clamp_x (bool, optional): Whether to clamp the predicted x_start to [-1, 1] range - for numerical stability. Defaults to False. - Returns: - tuple[Tensor, Tensor, Tensor, Tensor]: A tuple containing: - - mean (Tensor): The mean of the posterior distribution p(x_{t-1} | x_t). - - var (Tensor): The variance of the posterior distribution. - - log_var (Tensor): The log variance of the posterior distribution. - - x_start (Tensor): The predicted clean data (x_0) based on current state and prediction. - """ - if self.var_type in [ModelVarType.FIXED_SMALL.value, ModelVarType.FIXED_LARGE.value]: - model_output = prediction - elif self.var_type in [ModelVarType.LEARNED.value, ModelVarType.LEARNED_RANGE.value]: - assert prediction.shape[1] % 2 == 0 - model_output, log_var = torch.chunk(prediction, 2, dim=1) - else: - raise ValueError(f"Unknown model var type: {self.var_type}") - - # extract mean - if self.mean_type == MeanType.XPREV.value: - x_start = self._get_x_start_from_x_prev(model_output, x, t) - elif self.mean_type == MeanType.XSTART.value: - x_start = model_output - elif self.mean_type == MeanType.EPSILON.value: - x_start = self._get_x_start_from_eps(model_output, x, t) - else: - raise ValueError(f"Unknown mean type: {self.mean_type}") - if clamp_x: - x_start = torch.clamp(x_start, -1, 1) - mean = self._get_mean_from_x_start(x, x_start, t) - - # extract variance - if self.var_type == ModelVarType.FIXED_SMALL.value: - var, log_var = ( - extract_into_tensor(self.posterior_variance, t, x.shape), - extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape), - ) - elif self.var_type == ModelVarType.FIXED_LARGE.value: - var, log_var = ( - torch.cat([self.posterior_variance[1:2], self.betas[1:]]), - torch.cat([self.posterior_log_variance_clipped[1:2], self.betas[1:]]), - ) - var, log_var = extract_into_tensor(var, t, x.shape), extract_into_tensor(log_var, t, x.shape) - elif self.var_type == ModelVarType.LEARNED.value: - var = log_var.exp() # type: ignore - elif self.var_type == ModelVarType.LEARNED_RANGE.value: - min_log = extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape) - max_log = extract_into_tensor(self.betas, t, x.shape).log() - log_var = (log_var + 1) / 2 # type: ignore - log_var = log_var * max_log + (1 - log_var) * min_log - var = log_var.exp() - else: - raise ValueError(f"Unknown model var type: {self.var_type}") - - return mean, var, log_var, x_start # type: ignore - - def _get_mean_for_ddim_guidance( - self, x: Tensor, x_start: Tensor, t: Tensor, grad: Tensor - ) -> tuple[Tensor, Tensor, Tensor]: - """ - Calculates the adjusted mean for DDIM with gradient guidance. - This method computes a modified mean for the next timestep in the reverse diffusion process - when using classifier guidance with DDIM sampling. It adjusts the predicted noise (epsilon) - based on the gradient input, then recalculates the predicted clean image and mean accordingly. - Args: - x (Tensor): The current noisy state at timestep t. - x_start (Tensor): The predicted initial clean state. - t (Tensor): The current timestep indices as a tensor. - grad (Tensor): The gradient used for guidance, typically from a classifier. - Returns: - tuple[Tensor, Tensor, Tensor]: A tuple containing: - - mean (Tensor): The adjusted mean for the next state in the reverse diffusion process. - - x_start (Tensor): The updated predicted clean state based on the guided noise. - - eps (Tensor): The adjusted noise prediction after applying the guidance. - """ - eps = self._get_eps_from_xstart(x, x_start, t) - eps = eps - (1 - extract_into_tensor(self.alphas_bar, t, grad.shape)).sqrt() * grad - - x_start = self._get_x_start_from_eps(eps, x, t) - mean = self._get_mean_from_x_start(x, x_start, t) - - return mean, x_start, eps - - def _sample_x_prev_ddim(self, x: Tensor, eps: Tensor, t: Tensor) -> Tensor: - """ - Performs a deterministic DDIM update from state x_t to x_{t-1}. - This method implements the Denoising Diffusion Implicit Models (DDIM) update rule, - which allows for deterministic sampling from the diffusion model. It computes x_{t-1} - given x_t and the predicted noise (epsilon) using coefficients from the diffusion process. - Args: - x (Tensor): The current state x_t at timestep t. - eps (Tensor): The predicted noise component (epsilon) at timestep t. - t (Tensor): The current timestep indices as a tensor. - Returns: - Tensor: The previous state x_{t-1} after the deterministic DDIM update. - References: - Song, J. et al. "Denoising Diffusion Implicit Models" (2020) - https://arxiv.org/abs/2010.02502 - """ - x_prev = ( - extract_into_tensor(self.alphas_bar_prev, t, x.shape).sqrt() - * ( - (x - (1 - extract_into_tensor(self.alphas_bar, t, x.shape)).sqrt() * eps) - / extract_into_tensor(self.sqrt_alphas_bar, t, x.shape) - ) - + (1 - extract_into_tensor(self.alphas_bar_prev, t, eps.shape)).sqrt() * eps - ) - return x_prev - - def classifier_grad( - self, x: Tensor, y: Tensor, t: Tensor, classifier: Callable[[Tensor, Tensor], Tensor] - ) -> Tensor: - """ - Computes the gradient of a classifier's output with respect to its input. - This function calculates the gradient of the log probability of the target class - with respect to the input. This gradient can be used for classifier guidance in - diffusion models, steering the generation process toward samples that the classifier - associates with the target class. - Args: - x (Tensor): The input tensor for which to compute gradients. Will be detached - from its computation graph and have requires_grad set to True. - y (Tensor): The target class indices, with shape (batch_size,). - t (Tensor): The timestep tensor passed to the classifier. - classifier (Callable[[Tensor, Tensor], Tensor]): A function that takes the input x - and timestep t, and returns logits for each class. - Returns: - Tensor: The gradient of the log probability of the target class with respect to - the input x, with the same shape as x. - """ - x = x.detach().requires_grad_() - logits = classifier(x, t) - logprobs = torch.nn.functional.log_softmax(logits, dim=-1) - logprobs_y = logprobs[torch.arange(y.size(0)), y] - return torch.autograd.grad(logprobs_y.sum(), x)[0] - def draw_timesteps(self, batch_size: int) -> Tensor: """ Draws random timesteps for training the diffusion model. @@ -502,9 +216,8 @@ def one_step_denoise( t: int, clamp_x: bool = False, guidance_scale: float = 0.0, - classifier_free: bool = True, - classifier: Callable[[Tensor, Tensor], Tensor] | None = None, - ) -> Tensor: + sampler_args: dict[str, Any] = {}, + ) -> StepResult: """ Performs one step of the reverse diffusion process. This method denoises the input data by one timestep using the trained model. @@ -520,17 +233,11 @@ def one_step_denoise( guidance_scale (float, optional): Scale factor for guidance. Values greater than 0 enable guidance, with higher values emphasizing the conditional prediction more. Defaults to 0.0 (no guidance). - classifier_free (bool, optional): Whether to use classifier-free guidance (True) or - classifier-based guidance (False). Defaults to True. - classifier (Callable[[Tensor, Tensor], Tensor] | None, optional): A classifier function - for gradient-based guidance when classifier_free is False. Defaults to None. + sampler_args (dict[str, Any], optional): Additional arguments to pass to the sampler's step method. + Defaults to an empty dictionary. Returns: Tensor: The denoised data tensor after taking one reverse diffusion step. Notes: - - When using classifier-free guidance, the model is run twice: once with and once - without conditioning inputs, and the results are interpolated. - - When using classifier-based guidance, gradients from a classifier are used to - guide the sampling process toward the desired class or condition. - The timestep mapping is applied if a different number of sampling steps than training steps is being used. """ @@ -543,97 +250,21 @@ def one_step_denoise( timesteps_model = timesteps prediction = model(**{**model_inputs, "p": 0}, timesteps=timesteps_model)["x"] - if classifier_free and guidance_scale > 0: + if guidance_scale > 0: prediction_uncond = model(**{**model_inputs, "p": 1}, timesteps=timesteps_model)["x"] prediction = prediction_uncond + guidance_scale * (prediction - prediction_uncond) - mean, _, log_var, x_start = self._get_p_mean_var(prediction, model_inputs["x"], timesteps, clamp_x) - - if not classifier_free and guidance_scale > 0: - assert classifier is not None - if "y" in model_inputs: - grad = guidance_scale * self.classifier_grad(prediction, model_inputs["y"], timesteps_model, classifier) - elif "context" in model_inputs: - grad = guidance_scale * self.classifier_grad( - prediction, model_inputs["context"], timesteps_model, classifier - ) - else: - raise ValueError("No context or label provided for the classifier") - - if self.sampling_method == "ddpm": - mean = mean + grad - x_prev = self._get_x_prev_from_mean_var(mean, log_var, timesteps) - elif self.sampling_method == "ddim": - mean, x_start, eps = self._get_mean_for_ddim_guidance(model_inputs["x"], x_start, timesteps, grad) - x_prev = self._sample_x_prev_ddim(model_inputs["x"], eps, timesteps) - else: - raise NotImplementedError( - f"Classifier guidance not implemented for sampling method: {self.sampling_method}" - ) - else: - x_prev = self._get_x_prev_from_mean_var(mean, log_var, timesteps) + step_output = self.sampler.step( + model_prediction=prediction, + timesteps=timesteps, + xt=model_inputs["x"], + clamp_x=clamp_x, + **sampler_args, + ) - return x_prev + return step_output - # maybe change it to avoid inplace change of the dict - def denoise( - self, - model: Denoiser, - data_shape: tuple[int, ...], - model_inputs: ModelInput, - use_tqdm: bool = True, - clamp_x: bool = False, - guidance_scale: float = 0, - classifier_free: bool = True, - classifier: Callable[[Tensor, Tensor], Tensor] | None = None, - ) -> Tensor: - """ - Generates a sample by running the entire reverse diffusion process. - This method implements the full reverse process of the diffusion model, iteratively - applying the one_step_denoise function from the most noisy state to progressively - cleaner states. It can generate samples unconditionally or with guidance. - Args: - model (Denoiser): The neural network model used for denoising. - data_shape (tuple[int, ...]): Shape of the data to generate, typically (batch_size, channels, height, width). - model_inputs (ModelInput): A dictionary containing the model inputs, potentially including: - - 'x': Initial noise tensor. If not provided, random noise will be generated. - - conditional inputs like context embeddings, class labels, etc. - use_tqdm (bool, optional): Whether to display a progress bar during generation. Defaults to True. - clamp_x (bool, optional): Whether to clamp the generated values to a valid range (typically [-1, 1]). - Defaults to True. - guidance_scale (float, optional): Scale factor for guidance. If greater than 0, applies guidance - to steer the generation. Defaults to 0. - classifier_free (bool, optional): Whether to use classifier-free guidance. If True, uses the conditional - and unconditional model outputs for guidance. If False, uses a separate classifier. Defaults to True. - classifier (Callable[[Tensor, Tensor], Tensor] | None, optional): External classifier function to use - for guidance when classifier_free is False. Accepts input tensor and timesteps, returns gradients. - Defaults to None. - Notes: - The model_inputs dictionary is modified in place with the current sample state. - """ - if "x" not in model_inputs: - model_inputs["x"] = torch.randn( - data_shape, device=next(model.parameters()).device, dtype=next(model.parameters()).dtype - ) - for t in tqdm( - list(range(self.steps))[::-1], - desc="generating image", - total=self.steps, - disable=not use_tqdm, - leave=False, - ): - model_inputs["x"] = self.one_step_denoise( - model=model, - model_inputs=model_inputs, - t=t, - clamp_x=clamp_x, - guidance_scale=guidance_scale, - classifier_free=classifier_free, - classifier=classifier, - ) - return model_inputs["x"] - - ### Need to add compute loss for different parameterization + variance learned + ### TODO: Need to add compute loss for different parameterization + variance learned def compute_loss( self, model: Denoiser, @@ -656,6 +287,10 @@ def compute_loss( timesteps (Tensor): A tensor of shape (batch_size,) containing timestep indices. noise (Tensor | None, optional): Pre-generated noise to add to the inputs. If None, random noise will be generated. Defaults to None. + extra_losses (list[LossFunction], optional): Additional loss functions to compute. + Defaults to an empty list. + extra_args (dict[str, Any], optional): Additional arguments to pass to extra loss functions. + Defaults to an empty dictionary. Returns: dict[str, Tensor]: A dictionary containing the loss value and any additional losses Note: @@ -702,6 +337,111 @@ def add_noise(self, x: Tensor, timesteps: Tensor, noise: Tensor | None = None) - assert timesteps.shape[0] == x.shape[0] x_t = ( extract_into_tensor(self.sqrt_alphas_bar, timesteps, x.shape) * x - + (1 - extract_into_tensor(self.alphas_bar, timesteps, noise.shape)).sqrt() * noise + + (torch.ones_like(noise) - extract_into_tensor(self.alphas_bar, timesteps, noise.shape)).sqrt() * noise ) return x_t, noise + + def denoise( + self, + model: Denoiser, + model_inputs: ModelInput, + data_shape: tuple[int, ...] | None = None, + use_tqdm: bool = True, + clamp_x: bool = False, + guidance_scale: float = 0, + sampler_args: dict[str, Any] = {}, + return_intermediates: bool = False, + ) -> SamplingOutput: + """ + Generates a sample by running the entire reverse diffusion process. + This method implements the full reverse process of the diffusion model, iteratively + applying the one_step_denoise function from the most noisy state to progressively + cleaner states. It can generate samples unconditionally or with guidance. + Args: + model (Denoiser): The neural network model used for denoising. + model_inputs (ModelInput): A dictionary containing the model inputs, potentially including: + - 'x': Initial noise tensor. If not provided, random noise will be generated. + - conditional inputs like context embeddings, class labels, etc. + data_shape (tuple[int, ...] | None): Shape of the data to generate, typically (batch_size, channels, height, width). + If 'x' is provided in model_inputs, data_shape can be set to None. + Defaults to None. + use_tqdm (bool, optional): Whether to display a progress bar during generation. Defaults to True. + clamp_x (bool, optional): Whether to clamp the generated values to a valid range (typically [-1, 1]). + Defaults to True. + guidance_scale (float, optional): Scale factor for guidance. If greater than 0, applies guidance + to steer the generation. Defaults to 0. + sampler_args (dict[str, Any], optional): Additional arguments to pass to the sampler's step method. + Defaults to an empty dictionary. + return_intermediates (bool, optional): Whether to return intermediate states during denoising. + Defaults to False. + Returns: + SamplingOutput: A dictionary containing: + - x (Tensor): The final generated sample tensor. + - xt (Tensor, optional): If `return_intermediates` is True, a tensor of shape + (batch_size, steps+1, ...) containing the sample at each timestep. + - estimated_x0 (Tensor, optional): If `return_intermediates` is True, a tensor + of shape (batch_size, steps, ...) containing the estimated original data at + each timestep. + - xt_mean (Tensor, optional): If `return_intermediates` is True and the sampler + provides mean estimates, a tensor of shape (batch_size, steps, ...) containing + the mean at each timestep. + - xt_std (Tensor, optional): If `return_intermediates` is True and the sampler + provides std estimates, a tensor of shape (batch_size, steps, ...) containing + the standard deviation at each timestep. + - logprob (Tensor, optional): If `return_intermediates` is True and the sampler + provides log probabilities, a tensor of shape (batch_size, steps, ...) containing + the log probabilities at each timestep. + Notes: + The model_inputs dictionary is modified in place with the current sample state. + """ + if "x" not in model_inputs: + assert data_shape is not None, "'data_shape' must be provided if 'x' is not in model_inputs" + model_inputs["x"] = torch.randn( + data_shape, device=next(model.parameters()).device, dtype=next(model.parameters()).dtype + ) + + all_x0: list[Tensor] = [] + all_xt: list[Tensor] = [model_inputs["x"]] + all_xt_mean: list[Tensor] = [] + all_xt_std: list[Tensor] = [] + all_logprobs: list[Tensor] = [] + + for t in tqdm( + list(range(self.steps))[::-1], + desc="generating image", + total=self.steps, + disable=not use_tqdm, + leave=False, + ): + step_output = self.one_step_denoise( + model=model, + model_inputs=model_inputs, + t=t, + clamp_x=clamp_x, + guidance_scale=guidance_scale, + sampler_args=sampler_args, + ) + + model_inputs["x"] = step_output["x_prev"] + if return_intermediates: + all_xt.append(step_output["x_prev"]) + all_x0.append(step_output["estimated_x0"]) + if "x_prev_mean" in step_output: + all_xt_mean.append(step_output["x_prev_mean"]) + if "x_prev_std" in step_output: + all_xt_std.append(step_output["x_prev_std"]) + if "logprob" in step_output: + all_logprobs.append(step_output["logprob"]) + + out: SamplingOutput = {"x": model_inputs["x"]} + if return_intermediates: + out["xt"] = torch.stack(all_xt, dim=1) + out["estimated_x0"] = torch.stack(all_x0, dim=1) + if len(all_xt_mean) > 0: + out["xt_mean"] = torch.stack(all_xt_mean, dim=1) + if len(all_xt_std) > 0: + out["xt_std"] = torch.stack(all_xt_std, dim=1) + if len(all_logprobs) > 0: + out["logprob"] = torch.stack(all_logprobs, dim=1) + + return out diff --git a/src/diffulab/diffuse/samplers/__init__.py b/src/diffulab/diffuse/samplers/__init__.py new file mode 100644 index 0000000..36f0b16 --- /dev/null +++ b/src/diffulab/diffuse/samplers/__init__.py @@ -0,0 +1,3 @@ +from .common import Sampler, StepResult + +__all__ = ["StepResult", "Sampler"] diff --git a/src/diffulab/diffuse/samplers/common.py b/src/diffulab/diffuse/samplers/common.py new file mode 100644 index 0000000..b71e48a --- /dev/null +++ b/src/diffulab/diffuse/samplers/common.py @@ -0,0 +1,32 @@ +from abc import ABC, abstractmethod +from typing import Any, NotRequired, Required, TypedDict + +from torch import Tensor + + +class StepResult(TypedDict): + x_prev: Required[Tensor] + estimated_x0: Required[Tensor] + x_prev_mean: NotRequired[Tensor] + x_prev_std: NotRequired[Tensor] + logprob: NotRequired[Tensor] + + +class Sampler(ABC): + name: str + + def __init__(self) -> None: + pass + + @abstractmethod + def set_steps(self, *args: Any, **kwargs: Any) -> None: + """ + Set parameters based on the provided schedule. + """ + + @abstractmethod + def step(self, *args: Any, **kwargs: Any) -> StepResult: + """ + Perform one step of the reverse process + """ + pass diff --git a/src/diffulab/diffuse/samplers/flow/__init__.py b/src/diffulab/diffuse/samplers/flow/__init__.py new file mode 100644 index 0000000..768e2a2 --- /dev/null +++ b/src/diffulab/diffuse/samplers/flow/__init__.py @@ -0,0 +1,5 @@ +from .common import FlowSampler +from .euler import Euler +from .euler_meruyama import EulerMaruyama + +__all__ = ["FlowSampler", "Euler", "EulerMaruyama"] diff --git a/src/diffulab/diffuse/samplers/flow/common.py b/src/diffulab/diffuse/samplers/flow/common.py new file mode 100644 index 0000000..2bdb5e7 --- /dev/null +++ b/src/diffulab/diffuse/samplers/flow/common.py @@ -0,0 +1,43 @@ +from abc import ABC, abstractmethod +from typing import Any + +from torch import Tensor + +from diffulab.diffuse.samplers import Sampler, StepResult + + +class FlowSampler(Sampler, ABC): + name: str + + @abstractmethod + def set_steps(self, timesteps: list[float]) -> None: + """ + Set the sampler timesteps for eventual parameters computation. + + Args: + timesteps (list[float]): A list of timesteps + """ + pass + + @abstractmethod + def step(self, x_t: Tensor, v: Tensor, t_curr: float, t_prev: float, *args: Any, **kwargs: Any) -> StepResult: + """ + Perform one step of the reverse diffusion process. + + Args: + x_t (Tensor): The current state tensor at time t_curr. + v (Tensor): The velocity field tensor at time t_curr inferred by the model. + t_curr (float): The current timestep in the diffusion process. + t_prev (float): The previous timestep in the diffusion process. + *args: Additional positional arguments for specific sampler implementations. + **kwargs: Additional keyword arguments for specific sampler implementations. + + Returns: + StepResult: A dictionary containing the results of the step, including: + - x_prev (Tensor): The updated state tensor at time t_prev. + - x_prev_mean (Tensor, optional): The mean of the updated state before adding noise. + - x_prev_std (Tensor, optional): The standard deviation of the noise added. + - logprobs (Tensor, optional): Log probabilities if applicable. + - estimated_x0 (Tensor, optional): Estimated original data if applicable. + """ + pass diff --git a/src/diffulab/diffuse/samplers/flow/euler.py b/src/diffulab/diffuse/samplers/flow/euler.py new file mode 100644 index 0000000..35d408f --- /dev/null +++ b/src/diffulab/diffuse/samplers/flow/euler.py @@ -0,0 +1,41 @@ +from torch import Tensor + +from diffulab.diffuse.samplers import StepResult +from diffulab.diffuse.samplers.flow.common import FlowSampler + + +class Euler(FlowSampler): + name = "euler" + + def __init__(self) -> None: + super().__init__() + + def set_steps(self, timesteps: list[float]) -> None: + """ + Set the sampler timesteps for eventual parameters computation. + + Args: + timesteps (list[float]): A list of timesteps + """ + pass + + def step(self, x_t: Tensor, v: Tensor, t_curr: float, t_prev: float) -> StepResult: + """ + Perform one step of the reverse flow matching process using the Euler method. + + Args: + x_t (Tensor): The current state tensor at time t_curr. + v (Tensor): The velocity field tensor at time t_curr. + t_curr (float): The current timestep in the diffusion process. + t_prev (float): The previous timestep in the diffusion process. + + Returns: + StepResult: A dictionary containing the results of the step, including: + - x_prev (Tensor): The updated state tensor at time t_prev. + - estimated_x0 (Tensor, optional): Estimated original data + """ + dt = t_curr - t_prev # positive dt + x_prev = x_t - v * dt + estimated_x0 = x_t - v * t_curr + + return StepResult(x_prev=x_prev, estimated_x0=estimated_x0) diff --git a/src/diffulab/diffuse/samplers/flow/euler_meruyama.py b/src/diffulab/diffuse/samplers/flow/euler_meruyama.py new file mode 100644 index 0000000..fa2457e --- /dev/null +++ b/src/diffulab/diffuse/samplers/flow/euler_meruyama.py @@ -0,0 +1,57 @@ +import torch +from torch import Tensor + +from diffulab.diffuse.samplers import StepResult +from diffulab.diffuse.samplers.flow.common import FlowSampler + + +class EulerMaruyama(FlowSampler): + name = "euler_maruyama" + + def __init__(self, eta: float = 0.7) -> None: + super().__init__() + self.eta = eta + self.tmax = None + + def set_steps(self, timesteps: list[float]): + """ + Set tmax based on the provided timesteps. + Args: + timesteps (list[float]): The list of timesteps + """ + self.tmax = timesteps[1] + + def step(self, x_t: Tensor, v: Tensor, t_curr: float, t_prev: float, x_prev: Tensor | None = None) -> StepResult: + """ + Perform one step of the reverse flow matching process using the Euler-Maruyama method. + + Args: + x_t (Tensor): The current state tensor at time t_curr. + v (Tensor): The velocity field tensor at time t_curr. + t_curr (float): The current timestep in the diffusion process. + t_prev (float): The previous timestep in the diffusion process. + x_prev (Tensor, optional): If provided, this tensor will be used as the previous state instead of sampling a new one. + + Returns: + StepResult: A dictionary containing the results of the step, including: + + """ + assert self.tmax is not None, "set_steps must be called before step" + sigma: float = ((t_curr / (1 - min(t_curr, self.tmax))) ** 0.5) * self.eta + x_prev_mean = x_t - (v + sigma**2 / (2 * t_curr) * (x_t + (1 - t_curr) * v)) * (t_curr - t_prev) + x_prev_std = torch.tensor(sigma * (t_curr - t_prev) ** 0.5, device=x_t.device) + if x_prev is None: + noise = torch.randn_like(x_t) + x_prev = x_prev_mean + x_prev_std * noise + + assert x_prev is not None # for python type checking + estimated_x0 = x_t - v * t_curr + logprob = -( + (x_prev.detach() - x_prev_mean) ** 2 / (2 * x_prev_std**2) + + torch.log(x_prev_std) + + 0.5 * torch.log(torch.tensor(2 * torch.pi, device=x_prev.device)) + ) + + return StepResult( + x_prev=x_prev, x_prev_mean=x_prev_mean, x_prev_std=x_prev_std, estimated_x0=estimated_x0, logprob=logprob + ) diff --git a/src/diffulab/diffuse/samplers/gaussian_diffusion/__init__.py b/src/diffulab/diffuse/samplers/gaussian_diffusion/__init__.py new file mode 100644 index 0000000..55d71c3 --- /dev/null +++ b/src/diffulab/diffuse/samplers/gaussian_diffusion/__init__.py @@ -0,0 +1,5 @@ +from .common import GaussianSampler +from .ddim import DDIM +from .ddpm import DDPM + +__all__ = ["GaussianSampler", "DDPM", "DDIM"] diff --git a/src/diffulab/diffuse/samplers/gaussian_diffusion/common.py b/src/diffulab/diffuse/samplers/gaussian_diffusion/common.py new file mode 100644 index 0000000..a14d617 --- /dev/null +++ b/src/diffulab/diffuse/samplers/gaussian_diffusion/common.py @@ -0,0 +1,42 @@ +from abc import ABC, abstractmethod +from typing import Any + +from torch import Tensor + +from diffulab.diffuse.samplers import Sampler, StepResult + + +class GaussianSampler(Sampler, ABC): + name: str + + @abstractmethod + def set_steps(self, betas: Tensor) -> None: + """ + Set the diffusion parameters based on the provided beta schedule. + + Args: + betas (Tensor): A 1D tensor containing the beta values for each timestep in the diffusion process. + """ + pass + + @abstractmethod + def step( + self, model_prediction: Tensor, timesteps: Tensor, xt: Tensor, clamp_x: bool = False, *args: Any, **kwargs: Any + ) -> StepResult: + """ + Perform one step of the reverse diffusion process. + + Args: + model_prediction (Tensor): The model's prediction at the current timestep. + timesteps (Tensor): The current timestep in the diffusion process. + xt (Tensor): The current state tensor at the current timestep. + clamp_x (bool, optional): Whether to clamp the predicted x0 to a valid range + *args: Additional positional arguments specific to the sampler. + **kwargs: Additional keyword arguments specific to the sampler. + + Returns: + StepResult: A dictionary containing the results of the step, including: + - x_prev (Tensor): The updated state tensor at the previous timestep. + - estimated_x0 (Tensor, optional): Estimated original data. + """ + pass diff --git a/src/diffulab/diffuse/samplers/gaussian_diffusion/ddim.py b/src/diffulab/diffuse/samplers/gaussian_diffusion/ddim.py new file mode 100644 index 0000000..40dcfe0 --- /dev/null +++ b/src/diffulab/diffuse/samplers/gaussian_diffusion/ddim.py @@ -0,0 +1,103 @@ +import torch +from torch import Tensor + +from diffulab.diffuse.samplers.common import StepResult +from diffulab.diffuse.samplers.gaussian_diffusion.ddpm import DDPM +from diffulab.diffuse.utils import extract_into_tensor + + +class DDIM(DDPM): + name = "ddim" + + def __init__(self, mean_type: str = "epsilon", var_type: str = "fixed_small") -> None: + """ + Denoising Diffusion Implicit Models (DDIM) sampler. + This class implements the DDIM sampling method, which allows for deterministic sampling + from a diffusion model. It extends the DDPM sampler and overrides the step method to + perform the DDIM update. + Args: + betas (Tensor): A 1D tensor containing the beta values for each timestep in the diffusion process. + mean_type (str, optional): The type of mean prediction used by the model. Defaults to "epsilon". + var_type (str, optional): The type of variance prediction used by the model. Defaults to "fixed_small". + References: + Song, J. et al. "Denoising Diffusion Implicit Models" (2020) + https://arxiv.org/abs/2010.02502 + """ + super().__init__(mean_type, var_type) + + def _sample_x_prev_ddim( + self, xt: Tensor, eps: Tensor, x_start: Tensor, t: Tensor, eta: float = 0.0 + ) -> tuple[Tensor, Tensor, Tensor]: + """ + Performs a deterministic DDIM update from state x_t to x_{t-1}. + This method implements the Denoising Diffusion Implicit Models (DDIM) update rule, + which allows for deterministic sampling from the diffusion model. It computes x_{t-1} + given x_t and the predicted noise (epsilon) using coefficients from the diffusion process. + Args: + x (Tensor): The current state x_t at timestep t. + eps (Tensor): The predicted noise component (epsilon) at timestep t. + t (Tensor): The current timestep indices as a tensor. + Returns: + tuple[Tensor, Tensor, Tensor]: A tuple containing: + - x_prev (Tensor): The updated state tensor at the previous timestep x_{t-1}. + - mean_pred (Tensor): The mean prediction before adding noise. + - sigma (Tensor): The standard deviation of the noise added. + """ + sigma = ( + eta + * ( + (torch.ones_like(xt) - extract_into_tensor(self.alphas_bar_prev, t, xt.shape)) + / (torch.ones_like(xt) - extract_into_tensor(self.alphas_bar, t, xt.shape)) + ).sqrt() + * ( + torch.ones_like(xt) + - extract_into_tensor(self.alphas_bar, t, xt.shape) + / extract_into_tensor(self.alphas_bar_prev, t, xt.shape) + ).sqrt() + ) + mean_pred = ( + x_start * extract_into_tensor(self.alphas_bar_prev, t, xt.shape).sqrt() + + (torch.ones_like(xt) - extract_into_tensor(self.alphas_bar_prev, t, xt.shape) - sigma**2).sqrt() * eps + ) + + noise = torch.randn_like(mean_pred) + t_mask = (t > 0).float().view(-1, *([1] * (mean_pred.dim() - 1))) + x_prev = mean_pred + t_mask * sigma * noise + return x_prev, mean_pred, sigma + + def step( + self, model_prediction: Tensor, timesteps: Tensor, xt: Tensor, clamp_x: bool = False, eta: float = 0.0 + ) -> StepResult: + """ + Perform one step of the reverse diffusion process using the DDIM update rule. + Args: + model_prediction (Tensor): The model's prediction at the current timestep. + timesteps (Tensor): The current timestep in the diffusion process. + xt (Tensor): The current state tensor at the current timestep. + clamp_x (bool, optional): Whether to clamp the predicted x0 to a valid range + eta (float, optional): The noise scale for the DDIM update. Defaults to 0.0 for deterministic sampling. + *args: Additional positional arguments specific to the sampler. + **kwargs: Additional keyword arguments specific to the sampler. + Returns: + StepResult: A dictionary containing the results of the step, including: + - x_prev (Tensor): The updated state tensor at the previous timestep. + - estimated_x0 (Tensor, optional): Estimated original data. + - x_prev_mean (Tensor, optional): The mean of the updated state before adding noise. + - x_prev_std (Tensor, optional): The standard deviation of the noise added. + - logprob (Tensor, optional): The log probability of the transition. + """ + _, _, _, x_start = self._get_p_mean_var(model_prediction, xt, timesteps, clamp_x) + eps = self._get_eps_from_xstart(x_start, xt, timesteps) + x_prev, ddim_mean, ddim_std = self._sample_x_prev_ddim(xt, eps, x_start, timesteps, eta) + + out = StepResult(x_prev=x_prev, estimated_x0=x_start, x_prev_mean=ddim_mean) + if eta > 0: + logprob = -( + (x_prev.detach() - ddim_mean) ** 2 / (2 * ddim_std**2) + + torch.log(ddim_std) + + 0.5 * torch.log(torch.tensor(2 * torch.pi, device=x_prev.device)) + ) + out["x_prev_std"] = ddim_std + out["logprob"] = logprob + + return out diff --git a/src/diffulab/diffuse/samplers/gaussian_diffusion/ddpm.py b/src/diffulab/diffuse/samplers/gaussian_diffusion/ddpm.py new file mode 100644 index 0000000..ecd17f9 --- /dev/null +++ b/src/diffulab/diffuse/samplers/gaussian_diffusion/ddpm.py @@ -0,0 +1,363 @@ +import enum +from typing import cast + +import torch +from torch import Tensor + +from diffulab.diffuse.samplers.common import StepResult +from diffulab.diffuse.samplers.gaussian_diffusion.common import GaussianSampler +from diffulab.diffuse.utils import extract_into_tensor + + +class MeanType(enum.Enum): + EPSILON = "epsilon" + XSTART = "xstart" + XPREV = "xprev" + + +class ModelVarType(enum.Enum): + LEARNED = "learned" + FIXED_SMALL = "fixed_small" + FIXED_LARGE = "fixed_large" + LEARNED_RANGE = "learned_range" + + +class DDPM(GaussianSampler): + name = "ddpm" + + def __init__(self, mean_type: str = "epsilon", var_type: str = "fixed_small") -> None: + """ + Denoising Diffusion Probabilistic Model (DDPM) sampler. + + Args: + mean_type (str, optional): Type of mean prediction. Options are "epsilon", "xstart", or "xprev". + Defaults to "epsilon". + var_type (str, optional): Type of variance prediction. Options are "fixed_small", "fixed_large", + "learned", or "learned_range". Defaults to "fixed_small". + Raises: + ValueError: If an unknown mean_type or var_type is provided. + """ + super().__init__() + if mean_type not in MeanType._value2member_map_: + raise ValueError(f"mean_type must be one of {[e.value for e in MeanType]}") + if var_type not in ModelVarType._value2member_map_: + raise ValueError(f"variance_type must be one of {[e.value for e in ModelVarType]}") + + self.mean_type = mean_type + self.var_type = var_type + + def set_steps(self, betas: Tensor) -> None: + """ + Precomputes and sets various parameters required for the DDPM sampling process based on the provided + beta schedule. This includes calculations for alphas, cumulative products of alphas, posterior variance, + and coefficients used in the reverse diffusion steps. + Args: + betas (Tensor): A 1D tensor representing the beta schedule for the diffusion process. + Raises: + ValueError: If the provided betas tensor is not 1-dimensional. + """ + self.betas = betas + self.alphas = torch.ones_like(self.betas) - self.betas + self.alphas_bar = self.alphas.cumprod(dim=0) + self.alphas_bar_prev = torch.cat([torch.tensor([1.0], dtype=torch.float64), self.alphas_bar[:-1]]) + self.alphas_bar_next = torch.cat([self.alphas_bar[1:], torch.tensor([0.0], dtype=torch.float64)]) + + # utils for computation + self.sqrt_alphas_bar = self.alphas_bar.sqrt() + self.posterior_variance = ( + self.betas + * (torch.ones_like(self.alphas_bar_prev) - self.alphas_bar_prev) + / (torch.ones_like(self.alphas_bar) - self.alphas_bar) + ) + + # log calculation clipped because the posterior variance is 0 at the + # beginning of the diffusion chain. + self.posterior_log_variance_clipped = torch.log( + torch.cat([self.posterior_variance[1:2], self.posterior_variance[1:]]) + ) + self.posterior_mean_coef1 = ( + self.betas * (self.alphas_bar_prev).sqrt() / (torch.ones_like(self.alphas_bar) - self.alphas_bar) + ) + self.posterior_mean_coef2 = ( + (torch.ones_like(self.alphas_bar_prev) - self.alphas_bar_prev) + * self.alphas.sqrt() + / (torch.ones_like(self.alphas_bar) - self.alphas_bar) + ) + + def _get_x_start_from_x_prev(self, x_prev: Tensor, xt: Tensor, t: Tensor) -> Tensor: + """ + Computes the initial state (x_0) given the previous state (x_{t-1}) and current state (x_t). + It uses precomputed coefficients to solve for x_0 based on the Gaussian diffusion model's + posterior distribution parameters. + Args: + x_prev (Tensor): The state at the previous timestep (x_{t-1}). + x (Tensor): The state at the current timestep (x_t). + t (Tensor): The current timestep indices as a tensor. + Returns: + Tensor: The inferred initial clean state (x_0) based on the provided states. + """ + x_start = (1.0 / extract_into_tensor(self.posterior_mean_coef1, t, x_prev.shape)) * x_prev - ( + extract_into_tensor(self.posterior_mean_coef2, t, xt.shape) + / extract_into_tensor(self.posterior_mean_coef1, t, xt.shape) + ) * xt + return x_start + + def _get_x_start_from_eps(self, eps: Tensor, xt: Tensor, t: Tensor) -> Tensor: + """ + Computes the initial state (x_0) from the current state (x_t) and the predicted noise (epsilon). + This method uses the diffusion model's forward process parameters to invert the noise + addition and recover the original clean data, given the current noisy state and the + predicted noise component. + Args: + eps (Tensor): The predicted noise component (epsilon). + x (Tensor): The current noisy state at timestep t (x_t). + t (Tensor): The current timestep indices as a tensor. + Returns: + Tensor: The inferred initial clean state (x_0) based on the current state and predicted noise. + """ + x_start = (1.0 / extract_into_tensor(self.sqrt_alphas_bar, t, xt.shape)) * xt - ( + (torch.ones_like(eps) - extract_into_tensor(self.alphas_bar, t, eps.shape)).sqrt() + / extract_into_tensor(self.sqrt_alphas_bar, t, xt.shape) + ) * eps + return x_start + + def get_x_start(self, model_output: Tensor, xt: Tensor, t: Tensor, clamp_x: bool = False) -> Tensor: + """ + Infers the initial state (x_0) from the model's output based on the specified mean prediction type. + This method determines how to interpret the model's output (whether as noise, x_0, or x_{t-1}) + and computes the corresponding x_0. It also optionally clamps the inferred x_0 to a valid range + for numerical stability. + Args: + model_output (Tensor): The raw output from the denoising model. + x (Tensor): The current noisy state at timestep t (x_t). + t (Tensor): The current timestep indices as a tensor. + clamp_x (bool, optional): Whether to clamp the predicted x_start to [-1, 1] range + for numerical stability. Defaults to False. + Returns: + Tensor: The inferred initial clean state (x_0) based on the model's output and current state. + Raises: + ValueError: If an unknown mean type is specified. + """ + dispatch = { + MeanType.XPREV.value: lambda: self._get_x_start_from_x_prev(model_output, xt, t), + MeanType.XSTART.value: lambda: model_output, + MeanType.EPSILON.value: lambda: self._get_x_start_from_eps(model_output, xt, t), + } + + try: + x_start = dispatch[self.mean_type]() + except KeyError: + raise ValueError(f"Unknown mean type: {self.mean_type}") + + if clamp_x: + x_start = torch.clamp(x_start, -1, 1) + return x_start + + def _get_mean_from_x_start(self, xt: Tensor, x_start: Tensor, t: Tensor) -> Tensor: + """ + Computes the mean of the posterior distribution q(x_{t-1} | x_t, x_0) given the current state and inferred initial state. + This function calculates the mean of the posterior distribution using pre-computed coefficients that + depend on the noise schedule. The posterior mean is used during the reverse diffusion process to sample + from q(x_{t-1} | x_t, x_0). + Args: + x (Tensor): The current state at timestep t (x_t). + x_start (Tensor): The inferred or predicted initial clean state (x_0). + t (Tensor): The current timestep indices as a tensor. + Returns: + Tensor: The mean of the posterior distribution q(x_{t-1} | x_t, x_0). + """ + + mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_start.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, xt.shape) * xt + ) + return mean + + def get_variance( + self, + t: Tensor, + x_shape: tuple[int, ...], + log_var: Tensor | None = None, + ) -> tuple[Tensor, Tensor | None]: + """ + Computes the variance and log variance for the posterior distribution q(x_{t-1} | x_t, x_0). + This method determines the variance type specified during initialization and computes + the corresponding variance and log variance values. It supports fixed small, fixed large, + learned, and learned range variance types. + Args: + t (Tensor): The current timestep indices as a tensor. + x_shape (tuple[int, ...]): The shape of the input tensor for which variance is computed. + log_var (Tensor | None, optional): The predicted log variance from the model, required + if the variance type is "learned" or "learned_range". Defaults to None. + Returns: + tuple[Tensor, Tensor | None]: A tuple containing: + - var (Tensor): The computed variance for the posterior distribution. + - log_var (Tensor | None): The computed log variance, or None if not applicable. + Raises: + ValueError: If an unknown variance type is specified or if log_var is required but not provided. + """ + + def _fixed_small(): + v = extract_into_tensor(self.posterior_variance, t, x_shape) + lv = extract_into_tensor(self.posterior_log_variance_clipped, t, x_shape) + return v, lv + + def _fixed_large(): + v_seq = torch.cat([self.posterior_variance[1:2], self.betas[1:]]) + lv_seq = torch.log(v_seq) + v = extract_into_tensor(v_seq, t, x_shape) + lv = extract_into_tensor(lv_seq, t, x_shape) + return v, lv + + def _learned(): + assert log_var is not None, "log_var must be provided for LEARNED" + return log_var.exp(), log_var + + def _learned_range(): + assert log_var is not None, "log_var must be provided for LEARNED_RANGE" + min_log = extract_into_tensor(self.posterior_log_variance_clipped, t, x_shape) + max_log = extract_into_tensor(self.betas, t, x_shape).log() + w = (log_var + 1) / 2 + lv = cast(Tensor, w * max_log + (1 - w) * min_log) + return lv.exp(), lv + + dispatch = { + ModelVarType.FIXED_SMALL.value: _fixed_small, + ModelVarType.FIXED_LARGE.value: _fixed_large, + ModelVarType.LEARNED.value: _learned, + ModelVarType.LEARNED_RANGE.value: _learned_range, + } + + try: + var, lv = dispatch[self.var_type]() + except KeyError: + raise ValueError(f"Unknown model var type: {self.var_type}") + + return var, lv + + def _get_p_mean_var( + self, prediction: Tensor, xt: Tensor, t: Tensor, clamp_x: bool = False + ) -> tuple[Tensor, Tensor, Tensor, Tensor]: + """ + Computes the posterior mean and variance for the reverse diffusion step. + This core method calculates the parameters of the posterior distribution + p(x_{t-1} | x_t, x_0) used in the reverse diffusion process. It processes the model's + prediction and current noisy state to extract the mean, variance, log variance, + and the predicted clean state. + Args: + prediction (Tensor): The raw output from the denoising model. Could be noise prediction, + x_start prediction, or x_prev prediction depending on self.mean_type. + x (Tensor): The current noisy state at timestep t (x_t). + t (Tensor): The current timestep indices as a tensor. + clamp_x (bool, optional): Whether to clamp the predicted x_start to [-1, 1] range + for numerical stability. Defaults to False. + Returns: + tuple[Tensor, Tensor, Tensor, Tensor]: A tuple containing: + - mean (Tensor): The mean of the posterior distribution p(x_{t-1} | x_t). + - var (Tensor): The variance of the posterior distribution. + - log_var (Tensor | None): The log variance of the posterior distribution. + - x_start (Tensor): The predicted clean data (x_0) based on current state and prediction. + """ + assert self.var_type in [ + ModelVarType.FIXED_SMALL.value, + ModelVarType.FIXED_LARGE.value, + ModelVarType.LEARNED.value, + ModelVarType.LEARNED_RANGE.value, + ] + + model_output = prediction + log_var: Tensor | None = None + if self.var_type in [ModelVarType.LEARNED.value, ModelVarType.LEARNED_RANGE.value]: + assert model_output.shape[1] % 2 == 0 + model_output, log_var = torch.chunk(model_output, 2, dim=1) + + # extract x_start + x_start = self.get_x_start(model_output, xt, t, clamp_x) + # extract mean + mean = self._get_mean_from_x_start(xt, x_start, t) + # extract variance + var, log_var = self.get_variance(t, xt.shape, log_var) + assert log_var is not None # for pyright + + return mean, var, log_var, x_start + + def _get_x_prev_from_mean_var(self, mean: Tensor, log_var: Tensor, t: Tensor) -> Tensor: + """ + Computes the previous state (x_{t-1}) from the posterior mean and variance. + This method samples the previous state from the posterior distribution p(x_{t-1}|x_t) + using the provided mean and variance parameters. For all timesteps except the final one (t=0), + it adds random Gaussian noise scaled by the square root of the variance. For the final timestep, + it returns the mean without adding noise. + Args: + mean (Tensor): The posterior mean of p(x_{t-1}|x_t). + var (Tensor): The posterior variance of p(x_{t-1}|x_t). + t (Tensor): The current timestep indices as a tensor. + Returns: + Tensor: The sampled previous state (x_{t-1}) from the posterior distribution. + """ + # Create a mask where t > 0 is True, and t == 0 is False + t_mask = (t > 0).float().view(-1, *([1] * (mean.dim() - 1))) + + # Generate noise with the same shape as mean + noise = torch.randn_like(mean) + + # Apply noise only where t > 0, using the mask to conditionally add noise + return mean + t_mask * noise * torch.exp(0.5 * log_var) + + def _get_eps_from_xstart(self, x_start: Tensor, xt: Tensor, t: Tensor) -> Tensor: + """ + Computes the noise component (epsilon) from the initial state and current state. + This method is the inverse of _get_x_start_from_eps, calculating the noise that would + have been added to x_start to produce the current noisy state x at timestep t. + Args: + x_start (Tensor): The inferred or known initial clean state (x_0). + x (Tensor): The current noisy state at timestep t (x_t). + t (Tensor): The current timestep indices as a tensor. + Returns: + StepResult: A dictionary containing the results of the step, including: + - x_prev (Tensor): The updated state tensor at the previous timestep. + - estimated_x0 (Tensor, optional): Estimated original data. + - x_prev_mean (Tensor, optional): The mean of the updated state before adding noise. + - x_prev_std (Tensor, optional): The standard deviation of the noise added. + - logprob (Tensor, optional): The log probability of the transition. + """ + eps = ((1 / extract_into_tensor(self.sqrt_alphas_bar, t, xt.shape)) * xt - x_start) / ( + 1 / extract_into_tensor(self.alphas_bar, t, xt.shape) - 1 + ).sqrt() + + return eps + + def step(self, model_prediction: Tensor, timesteps: Tensor, xt: Tensor, clamp_x: bool = False) -> StepResult: + """ + Performs a single reverse diffusion step using the DDPM approach. + This method computes the posterior mean and variance based on the model's prediction + and the current noisy state. It then samples the previous state from the posterior distribution. + Additionally, it calculates the log probability of the transition for potential use in + likelihood estimation or other analyses. + Args: + model_prediction (Tensor): The raw output from the denoising model. + timesteps (Tensor): The current timestep indices as a tensor. + x (Tensor): The current noisy state at timestep t (x_t). + clamp_x (bool, optional): Whether to clamp the predicted x_start to [-1, 1] range + for numerical stability. Defaults to False. + Returns: + StepResult: A dictionary containing the results of the step, including: + - x_prev (Tensor): The updated state tensor at the previous timestep. + - estimated_x0 (Tensor, optional): Estimated original data. + - x_prev_mean (Tensor, optional): The mean of the posterior distribution. + - x_prev_std (Tensor, optional): The standard deviation of the posterior distribution. + - logprob (Tensor, optional): The log probability of the transition. + """ + mean, var, log_var, x_start = self._get_p_mean_var(model_prediction, xt, timesteps, clamp_x) + x_prev = self._get_x_prev_from_mean_var(mean, log_var, timesteps) + + var_safe = var.clamp_min(1e-20) + const = torch.log(2 * torch.pi * var_safe) * 0.5 + elem = -((x_prev.detach() - mean) ** 2) / (2.0 * var_safe) - const + # zero out at t==0 to avoid degenerate edge-case + t_mask = (timesteps > 0).float().view(-1, *([1] * (elem.dim() - 1))) + logprob = elem * t_mask + + return StepResult( + x_prev=x_prev, estimated_x0=x_start, x_prev_mean=mean, x_prev_std=var_safe.sqrt(), logprob=logprob + ) diff --git a/src/diffulab/diffuse/utils.py b/src/diffulab/diffuse/utils.py index f8eaaaa..c1a91ae 100644 --- a/src/diffulab/diffuse/utils.py +++ b/src/diffulab/diffuse/utils.py @@ -1,3 +1,5 @@ +from typing import NotRequired, Required, TypedDict + from torch import Tensor @@ -15,3 +17,12 @@ def extract_into_tensor(arr: Tensor, timesteps: Tensor, broadcast_shape: tuple[i while len(res.shape) < len(broadcast_shape): res = res[..., None] return res.expand(broadcast_shape) + + +class SamplingOutput(TypedDict, total=False): + x: Required[Tensor] # final sample + estimated_x0: NotRequired[Tensor] # estimated x0 at each step + xt: NotRequired[Tensor] # samples at each step + xt_mean: NotRequired[Tensor] # mean of samples at each step for SDE based methods + xt_std: NotRequired[Tensor] # std of samples at each step for SDE based methods + logprob: NotRequired[Tensor] # logprob at each step for flow based methods diff --git a/src/diffulab/networks/__init__.py b/src/diffulab/networks/__init__.py index c628f62..0ae7d9a 100644 --- a/src/diffulab/networks/__init__.py +++ b/src/diffulab/networks/__init__.py @@ -1,5 +1,5 @@ -from .denoisers import Denoiser, MMDiT, UNetModel -from .embedders import SD3TextEmbedder +from .denoisers import DDT, Denoiser, MMDiT, UNetModel +from .embedders import QwenTextEmbedder, SD3TextEmbedder from .repa import REPA, DinoV2, PerceiverResampler from .vision_towers import DCAE, VisionTower @@ -7,7 +7,9 @@ "Denoiser", "UNetModel", "MMDiT", + "DDT", "SD3TextEmbedder", + "QwenTextEmbedder", "REPA", "DinoV2", "PerceiverResampler", diff --git a/src/diffulab/networks/denoisers/__init__.py b/src/diffulab/networks/denoisers/__init__.py index 5dc5d67..bfd7b36 100644 --- a/src/diffulab/networks/denoisers/__init__.py +++ b/src/diffulab/networks/denoisers/__init__.py @@ -1,5 +1,6 @@ from .common import Denoiser +from .ddt import DDT from .mmdit import MMDiT from .unet import UNetModel -__all__ = ["Denoiser", "UNetModel", "MMDiT"] +__all__ = ["Denoiser", "UNetModel", "MMDiT", "DDT"] diff --git a/src/diffulab/networks/denoisers/common.py b/src/diffulab/networks/denoisers/common.py index c5dd63b..2353cc3 100644 --- a/src/diffulab/networks/denoisers/common.py +++ b/src/diffulab/networks/denoisers/common.py @@ -10,6 +10,7 @@ class ModelInput(TypedDict, total=False): p: NotRequired[float] # probabilily of label dropping y: NotRequired[Tensor] # class labels context: NotRequired[Tensor] # context information, can be text image etc + x_context: NotRequired[Tensor] # additional image context, will be concatenated to x class ModelOutput(TypedDict, total=False): @@ -18,6 +19,16 @@ class ModelOutput(TypedDict, total=False): repa_features: NotRequired[list[Tensor]] # list of features after projection for REPA alignment +class ModelInputGRPO(TypedDict, total=False): + x: NotRequired[Tensor] # noisy input tensor + p: NotRequired[float] # probabilily of label dropping + context: Required[Tensor] # context information, for GRPO it's text embedding + + +class ExtraInputGRPO(TypedDict, total=False): + captions: Required[list[str]] # list of captions for the batch + + class Denoiser(nn.Module, ABC): classifier_free: bool diff --git a/src/diffulab/networks/denoisers/ddt.py b/src/diffulab/networks/denoisers/ddt.py new file mode 100644 index 0000000..11937e7 --- /dev/null +++ b/src/diffulab/networks/denoisers/ddt.py @@ -0,0 +1,430 @@ +# Recoded from scratch taking inspiration from https://arxiv.org/pdf/2504.05741 +# Some changes were made to the architecture to reuse DiT and MMDiT components +# e.g double stream + +from typing import Any + +import torch +import torch.nn as nn +from einops import rearrange +from jaxtyping import Float, Int +from torch import Tensor + +from diffulab.networks.denoisers.common import Denoiser, ModelOutput +from diffulab.networks.denoisers.mmdit import DiTBlock, MMDiTBlock +from diffulab.networks.embedders.common import ContextEmbedder, ContextEmbedderOutput +from diffulab.networks.utils.nn import ( + LabelEmbed, + modulate, + timestep_embedding, +) + + +class ModulatedLastLayerDDT(nn.Module): + def __init__(self, embedding_dim: int, hidden_size: int, patch_size: int, out_channels: int): + super().__init__() # type: ignore + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(embedding_dim, 2 * hidden_size, bias=True)) + + def forward( + self, x: Float[Tensor, "batch_size seq_len dim"], vec: Float[Tensor, "batch_size seq_len dim"] + ) -> Tensor: + alpha, beta = self.adaLN_modulation(vec).chunk(2, dim=-1) + x = modulate(self.norm_final(x), scale=alpha, shift=beta) + x = self.linear(x) + return x + + +class DDT(Denoiser): + """ + Decoder-Encoder architecture following https://arxiv.org/pdf/2504.05741 + + This module implements a dual-stream DDT: an encoder stream that consumes the noisy input + (and optional context) and a lightweight decoder stream conditioned on the encoder output + and timestep. It reuses DiT/MMDiT blocks and supports both label-only and multimodal conditioning. + + Args: + simple_ddt (bool): If True, uses a DiT-style encoder with class-label conditioning only + (no multimodal context). If False, uses an MMDiT-style encoder. Default: False. + input_channels (int): Number of channels of the main input x. Default: 3. + output_channels (int | None): Number of channels to predict. If None, equals + `input_channels`. Default: None. + input_dim (int): Token/patch embedding width for both encoder and decoder streams. + Also used as the hidden size of the last prediction layer. Default: 768. + hidden_dim (int): Inner attention dimension used by DiT/MMDiT attention blocks. + Default: 768. + num_heads (int): Number of attention heads in each block. Default: 12. + mlp_ratio (int): Expansion ratio for the MLP in each block. Default: 4. + patch_size (int): Side length P of square patches. Images are projected with stride P + in both encoder and decoder streams. Default: 16. + context_dim (int): Model width of contextual tokens after `context_embed` when + `simple_ddt=False`. Ignored when `simple_ddt=True`. Default: 1024. + encoder_depth (int): Number of DiT/MMDiT blocks in the encoder. Default: 8. + decoder_depth (int): Number of DiT blocks in the decoder. Default: 4. + partial_rotary_factor (float): Fraction of each head dimension using RoPE. + 1.0 means full rotary. Default: 1.0. + frequency_embedding (int): Size of the Fourier timestep embedding before the time MLP. + Default: 256. + n_classes (int | None): Number of classes for label conditioning in `simple_ddt` mode. + Required to use classifier-free guidance with labels. Default: None. + classifier_free (bool): Enables classifier-free guidance. In `simple_ddt`, it applies to + dropped labels; in MMDiT mode, it is forwarded to the context embedder which may drop + context. Default: False. + context_embedder (ContextEmbedder | None): When `simple_ddt=False`, a module returning + `ContextEmbedderOutput`. Must be provided for text conditioning. Must be None + when `simple_ddt=True`. If the embedder returns pooled and token embeddings + (`n_output == 2`), pooled features are fused into the timestep embedding via an MLP. + Default: None. + use_checkpoint (bool): Enable torch.utils.checkpoint in blocks to trade compute for memory. + Default: False. + """ + + def __init__( + self, + simple_ddt: bool = False, + input_channels: int = 3, + output_channels: int | None = None, + input_dim: int = 768, + hidden_dim: int = 768, + num_heads: int = 12, + mlp_ratio: int = 4, + patch_size: int = 16, + context_dim: int = 1024, + encoder_depth: int = 8, + decoder_depth: int = 4, + partial_rotary_factor_input: float = 1, + base_input: int = 100, + partial_rotary_factor_context: float = 1, + base_context: int = 10000, + frequency_embedding: int = 256, + n_classes: int | None = None, + classifier_free: bool = False, + context_embedder: ContextEmbedder | None = None, + use_checkpoint: bool = False, + ): + super().__init__() + assert not (n_classes is not None and context_embedder is not None), ( + "n_classes and context_embedder cannot both be specified" + ) + self.simple_ddt = simple_ddt + self.patch_size = patch_size + self.input_channels = input_channels + if not output_channels: + output_channels = input_channels + self.output_channels = output_channels + self.context_embedder = context_embedder + self.frequency_embedding = frequency_embedding + + self.n_classes = n_classes + self.classifier_free = classifier_free + + if not self.simple_ddt: + assert self.context_embedder is not None, "for ddt with text context embedder must be provided" + assert isinstance(self.context_embedder.output_size, tuple) and all( + isinstance(i, int) for i in self.context_embedder.output_size + ), "context_embedder.output_size must be a tuple of integers" + + self.pooled_embedding = False + self.mlp_pooled_context = None + if self.context_embedder.n_output == 2: + self.pooled_embedding = True + self.mlp_pooled_context = nn.Sequential( + nn.Linear(self.context_embedder.output_size[0], input_dim), + nn.SiLU(), + nn.Linear(input_dim, input_dim), + ) + self.context_embed = nn.Linear(self.context_embedder.output_size[1], context_dim) + else: + assert self.context_embedder.n_output == 1 + self.context_embed = nn.Linear(self.context_embedder.output_size[0], context_dim) + else: + self.label_embed = ( + LabelEmbed(self.n_classes, input_dim, self.classifier_free) if self.n_classes is not None else None + ) + + self.last_layer = ModulatedLastLayerDDT( + embedding_dim=input_dim, + hidden_size=input_dim, + patch_size=self.patch_size, + out_channels=self.output_channels, + ) + self.time_embed = nn.Sequential( + nn.Linear(self.frequency_embedding, input_dim), + nn.SiLU(), + nn.Linear(input_dim, input_dim), + ) + + self.conv_proj_encoder = nn.Conv2d( + self.input_channels, input_dim, kernel_size=self.patch_size, stride=self.patch_size + ) + self.conv_proj_decoder = nn.Conv2d( + self.input_channels, input_dim, kernel_size=self.patch_size, stride=self.patch_size + ) + + # -------------- + # encoder layers + # -------------- + self.layers = nn.ModuleList( + [ + MMDiTBlock( + context_dim=context_dim, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=input_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + partial_rotary_factor_input=partial_rotary_factor_input, + base_input=base_input, + partial_rotary_factor_context=partial_rotary_factor_context, + base_context=base_context, + use_checkpoint=use_checkpoint, + ) + if not self.simple_ddt + else DiTBlock( + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=input_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + partial_rotary_factor=partial_rotary_factor_input, + use_checkpoint=use_checkpoint, + ) + for _ in range(encoder_depth) + ] + ) + + # -------------- + # decoder layers + # -------------- + self.decoder_layers = nn.ModuleList( + [ + DiTBlock( + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=input_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + partial_rotary_factor=partial_rotary_factor_input, + use_checkpoint=use_checkpoint, + ) + for _ in range(decoder_depth) + ] + ) + + self.apply(self._init_weights) + + def _init_weights(self, module: nn.Module) -> None: + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + nn.init.xavier_uniform_(module.weight) + if module.bias is not None: # type: ignore + nn.init.constant_(module.bias, 0) + + def patchify( + self, x: Float[Tensor, "batch_size channels height width"], encoder: bool = True + ) -> Float[Tensor, "batch_size num_patches patch_dim"]: + """ + Convert image tensor into patches using convolutional projection. + Args: + x (Tensor): Input tensor of shape (B, C, H, W) + Returns: + Tensor: Patchified tensor of shape (B, num_patches, patch_dim) + """ + _, _, H, W = x.shape + self.original_size = (H, W) + + x = self.conv_proj_encoder(x) if encoder else self.conv_proj_decoder(x) + _, _, Hp, Wp = x.shape + self.grid_size = (Hp, Wp) + x = rearrange(x, "b c h w -> b (h w) c") + + return x + + def unpatchify( + self, x: Float[Tensor, "batch_size seq_len patch_dim"] + ) -> Float[Tensor, "batch_size channels height width"]: + """ + Convert patches back into the original image tensor. + Args: + x (Tensor): Patchified tensor of shape (B, num_patches, patch_dim) + Returns: + Tensor: Reconstructed image tensor of shape (B, C, H, W) + """ + # Reshape the tensor to the original image dimensions + x = rearrange( + x, + "b (h w) (p1 p2 c) -> b c (h p1) (w p2)", + h=self.grid_size[0], + w=self.grid_size[1], + p1=self.patch_size, + p2=self.patch_size, + c=self.output_channels, + ) + return x + + def encode_mmddt( + self, + x: Float[Tensor, "batch_size seq_len patch_dim"], + timesteps: Float[Tensor, "batch_size"], + initial_context: Any | None = None, + p: float = 0.0, + intermediate_features: bool = False, + ) -> ModelOutput: + """ + Forward pass through the encoder of the mmDDT model. + Args: + x (Tensor): Input tensor of shape (B, seq_len, patch_dim) + timesteps (Tensor): Timestep tensor of shape (B,) + initial_context (Any, optional): Initial context for the context embedder. Defaults to None. + p (float, optional): Probability for classifier-free guidance. Defaults to 0.0. + intermediate_features (bool, optional): Whether to return intermediate features. Defaults to False. + Returns: + ModelOutput: Output dictionary containing the final output and optional intermediate features. + """ + assert self.context_embedder is not None, "for MMDiT context embedder must be provided" + emb = self.time_embed(timestep_embedding(timesteps, self.frequency_embedding)) + context_output: ContextEmbedderOutput = self.context_embedder(initial_context, p) + if self.pooled_embedding: + assert self.mlp_pooled_context is not None, ( + "for MMDiT with pooled context, mlp_pooled_context must be defined" + ) + assert "pooled_embeddings" in context_output, "pooled embeddings must be in context_output" + context_pooled = context_output["pooled_embeddings"] + emb = self.mlp_pooled_context(context_pooled) + emb + + context = context_output["embeddings"] + context = self.context_embed(context) + attn_mask = context_output.get("attn_mask", None) + + features: list[Tensor] | None = [] if intermediate_features else None + # Pass through each layer sequentially + for layer in self.layers: + x, context = layer(x, emb, context, attn_mask=attn_mask, shape=self.grid_size) + if features is not None: + features.append(x) + + encoder_output: ModelOutput = {"x": x} + if features: + encoder_output["features"] = features + return encoder_output + + def encode_ddt( + self, + x: Float[Tensor, "batch_size seq_len_input patch_dim"], + timestep: Float[Tensor, "batch_size"], + p: float = 0.0, + y: Int[Tensor, "batch_size"] | None = None, + intermediate_features: bool = False, + ) -> ModelOutput: + """ + Forward pass through the encoder of the DDT model. + Args: + x (Tensor): Input tensor of shape (B, seq_len, patch_dim) + timestep (Tensor): Timestep tensor of shape (B,) + p (float, optional): Probability for classifier-free guidance. Defaults to 0.0 + y (Tensor, optional): Class labels. Defaults to None. + intermediate_features (bool, optional): Whether to return intermediate features. Defaults to False. + Returns: + ModelOutput: Output dictionary containing the final output and optional intermediate features. + """ + if p > 0: + assert self.n_classes, ( + "probability of dropping for classifier free guidance is only available if a number of classes is set" + ) + + emb = self.time_embed(timestep_embedding(timestep, self.frequency_embedding)) + if self.label_embed is not None: + emb = emb + self.label_embed(y, p) + + features: list[Tensor] | None = [] if intermediate_features else None + # Pass through each layer sequentially + for layer in self.layers: + x = layer(x, emb, shape=self.grid_size) + if features is not None: + features.append(x) + + encoder_output: ModelOutput = {"x": x} + if features: + encoder_output["features"] = features + return encoder_output + + def decode( + self, + x: Float[Tensor, "batch_size seq_len_input patch_dim"], + encoder_output: Float[Tensor, "batch_size seq_len_tot patch_dim"], + timesteps: Float[Tensor, "batch_size"], + intermediate_features: bool = False, + ) -> ModelOutput: + """ + Forward pass through the decoder of the DDT model. + Args: + x (Tensor): Input tensor of shape (B, seq_len_input, patch_dim) + encoder_output (Tensor): Encoder output tensor of shape (B, seq_len_tot, patch_dim) + timesteps (Tensor): Timestep tensor of shape (B,) + intermediate_features (bool, optional): Whether to return intermediate features. Defaults to False. + Returns: + ModelOutput: Output dictionary containing the final output and optional intermediate features. + """ + emb = self.time_embed(timestep_embedding(timesteps, self.frequency_embedding))[:, None, :] + encoder_output = nn.functional.silu(encoder_output + emb) + + features: list[Tensor] | None = [] if intermediate_features else None + for layer in self.decoder_layers: + x = layer(x, encoder_output, shape=self.grid_size) + if features is not None: + features.append(x) + + x = self.last_layer(x, encoder_output) + + decoder_output: ModelOutput = {"x": x} + if features: + decoder_output["features"] = features + return decoder_output + + def forward( + self, + x: Float[Tensor, "batch_size channels height width"], + timesteps: Float[Tensor, "batch_size"], + initial_context: Any | None = None, + p: float = 0.0, + y: Int[Tensor, "batch_size"] | None = None, + x_context: Tensor | None = None, + intermediate_features: bool = False, + ) -> ModelOutput: + """ + Forward pass through the DDT model. + Args: + x (Tensor): Input tensor of shape (B, C, H, W) + timesteps (Tensor): Timestep tensor of shape (B,) + initial_context (Any, optional): Initial context for the context embedder. Defaults to None. + p (float, optional): Probability for classifier-free guidance. Defaults to 0.0 + y (Tensor, optional): Class labels. Defaults to None. + x_context (Tensor, optional): Additional context tensor to concatenate with input. Defaults to None. + intermediate_features (bool, optional): Whether to return intermediate features. Defaults to False. + Returns: + ModelOutput: Output dictionary containing the final output and optional intermediate features. + """ + assert not (initial_context is not None and y is not None), "initial_context and y cannot both be specified" + if p > 0: + assert self.classifier_free, ( + "probability of dropping for classifier free guidance is only available if model is set up to be classifier free" + ) + if x_context is not None: + x = torch.cat([x, x_context], dim=1) + + encoder_input = self.patchify(x, encoder=True) + if self.simple_ddt: + encoder_output = self.encode_ddt(encoder_input, timesteps, p, y, intermediate_features) + else: + encoder_output = self.encode_mmddt(encoder_input, timesteps, initial_context, p, intermediate_features) + + decoder_input = self.patchify(x, encoder=False) + decoder_output = self.decode(decoder_input, encoder_output["x"], timesteps, intermediate_features) + + decoder_output["x"] = self.unpatchify(decoder_output["x"]) + + if "features" in encoder_output: + if "features" in decoder_output: + decoder_output["features"] = encoder_output["features"] + decoder_output["features"] + + return decoder_output diff --git a/src/diffulab/networks/denoisers/mmdit.py b/src/diffulab/networks/denoisers/mmdit.py index ef356c3..49e333e 100644 --- a/src/diffulab/networks/denoisers/mmdit.py +++ b/src/diffulab/networks/denoisers/mmdit.py @@ -5,12 +5,12 @@ import torch import torch.nn as nn from einops import rearrange -from jaxtyping import Float, Int +from jaxtyping import Bool, Float, Int from torch import Tensor from torch.utils.checkpoint import checkpoint # type: ignore from diffulab.networks.denoisers.common import Denoiser, ModelOutput -from diffulab.networks.embedders.common import ContextEmbedder +from diffulab.networks.embedders.common import ContextEmbedder, ContextEmbedderOutput from diffulab.networks.utils.nn import ( LabelEmbed, Modulation, @@ -18,6 +18,7 @@ QKNorm, RMSNorm, RotaryPositionalEmbedding, + RotaryPositionalEmbedding2D, modulate, timestep_embedding, ) @@ -79,11 +80,15 @@ def __init__( self.partial_rotary_factor = partial_rotary_factor self.rotary_dim = int(self.head_dim * self.partial_rotary_factor) - self.rope = RotaryPositionalEmbedding(dim=self.rotary_dim, base=base) + self.rope = RotaryPositionalEmbedding2D(dim=self.rotary_dim, base=base) self.proj_out = nn.Linear(dim, input_dim) - def forward(self, input: Float[Tensor, "batch_size seq_len dim"]) -> Float[Tensor, "batch_size seq_len dim"]: + def forward( + self, + input: Float[Tensor, "batch_size seq_len dim"], + shape: tuple[int, int] | None = None, + ) -> Float[Tensor, "batch_size seq_len dim"]: input_q, input_k, input_v = self.qkv(input).chunk(3, dim=-1) input_q, input_k = self.qk_norm(input_q, input_k, input_v) @@ -92,7 +97,7 @@ def forward(self, input: Float[Tensor, "batch_size seq_len dim"]) -> Float[Tenso rearrange(input_k, "b n (h d) -> b n h d", h=self.num_heads), rearrange(input_v, "b n (h d) -> b n h d", h=self.num_heads), ) - q, k, v = self.rope(q=q, k=k, v=v) + q, k, v = self.rope(q=q, k=k, v=v, shape=shape) q, k, v = map(lambda x: rearrange(x, "b n h d -> b h n d"), [q, k, v]) attn_output = nn.functional.scaled_dot_product_attention( @@ -161,8 +166,10 @@ def __init__( input_dim: int, dim: int, num_heads: int, - partial_rotary_factor: float = 1, - base: int = 10000, + partial_rotary_factor_input: float = 1, + base_input: int = 100, + partial_rotary_factor_context: float = 1, + base_context: int = 10000, ): super().__init__() # type: ignore self.num_heads = num_heads @@ -174,17 +181,21 @@ def __init__( self.qk_norm_input = QKNorm(dim) self.qk_norm_context = QKNorm(dim) - self.partial_rotary_factor = partial_rotary_factor - self.rotary_dim = int(self.head_dim * self.partial_rotary_factor) - self.rope = RotaryPositionalEmbedding(dim=self.rotary_dim, base=base) + rotary_dim_input = int(self.head_dim * partial_rotary_factor_input) + self.rope_input = RotaryPositionalEmbedding2D(dim=rotary_dim_input, base=base_input) + + rotary_dim_context = int(self.head_dim * partial_rotary_factor_context) + self.rope_context = RotaryPositionalEmbedding(dim=rotary_dim_context, base=base_context) self.input_proj_out = nn.Linear(dim, input_dim) self.context_proj_out = nn.Linear(dim, context_dim) def forward( self, - input: Float[Tensor, "batch_size seq_len input_dim"], - context: Float[Tensor, "batch_size seq_len context_dim"], + input: Float[Tensor, "batch_size seq_len_input input_dim"], + context: Float[Tensor, "batch_size seq_len_context context_dim"], + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, + shape: tuple[int, int] | None = None, ) -> tuple[Float[Tensor, "batch_size seq_len input_dim"], Float[Tensor, "batch_size seq_len context_dim"]]: input_q, input_k, input_v = self.qkv_input(input).chunk(3, dim=-1) context_q, context_k, context_v = self.qkv_context(context).chunk(3, dim=-1) @@ -192,19 +203,39 @@ def forward( input_q, input_k = self.qk_norm_input(input_q, input_k, input_v) context_q, context_k = self.qk_norm_context(context_q, context_k, context_v) + input_q, input_k, input_v = ( + rearrange(input_q, "b n (h d) -> b n h d", h=self.num_heads), + rearrange(input_k, "b n (h d) -> b n h d", h=self.num_heads), + rearrange(input_v, "b n (h d) -> b n h d", h=self.num_heads), + ) + context_q, context_k, context_v = ( + rearrange(context_q, "b n (h d) -> b n h d", h=self.num_heads), + rearrange(context_k, "b n (h d) -> b n h d", h=self.num_heads), + rearrange(context_v, "b n (h d) -> b n h d", h=self.num_heads), + ) + + input_q, input_k, input_v = self.rope_input(q=input_q, k=input_k, v=input_v, shape=shape) + context_q, context_k, context_v = self.rope_context(q=context_q, k=context_k, v=context_v) + q, k, v = ( - rearrange(torch.cat([context_q, input_q], dim=1), "b n (h d) -> b n h d", h=self.num_heads), - rearrange(torch.cat([context_k, input_k], dim=1), "b n (h d) -> b n h d", h=self.num_heads), - rearrange(torch.cat([context_v, input_v], dim=1), "b n (h d) -> b n h d", h=self.num_heads), + rearrange(torch.cat([context_q, input_q], dim=1), "b n h d -> b h n d"), + rearrange(torch.cat([context_k, input_k], dim=1), "b n h d -> b h n d"), + rearrange(torch.cat([context_v, input_v], dim=1), "b n h d -> b h n d"), ) - q, k, v = self.rope(q=q, k=k, v=v) - q, k, v = map(lambda x: rearrange(x, "b n h d -> b h n d"), [q, k, v]) + + if attn_mask is not None: + attn_mask = torch.cat( + [ + attn_mask.bool(), + torch.ones(attn_mask.size(0), input.size(1), device=attn_mask.device).bool(), + ], + dim=1, + ) + b, n = attn_mask.shape + attn_mask = attn_mask[:, None, None, :].expand(b, 1, n, n) attn_output = nn.functional.scaled_dot_product_attention( - query=q, - key=k, - value=v, - scale=self.scale, + query=q, key=k, value=v, scale=self.scale, attn_mask=attn_mask ) attn_output = rearrange(attn_output, "b h n d -> b n (h d)") @@ -231,8 +262,7 @@ class DiTBlock(nn.Module): Performs the forward pass of the MMDiTBlock. Args: input (Tensor): The input tensor. - y (Tensor): An additional tensor, not used in the current implementation. - context (Tensor): The context tensor. + y (Tensor): The conditioning tensor used for modulation Returns: Tuple[Tensor, Tensor]: The processed input tensor. @@ -252,12 +282,15 @@ def __init__( num_heads: int, mlp_ratio: int, partial_rotary_factor: float = 1, + base: int = 100, use_checkpoint: bool = False, ): super().__init__() # type: ignore self.modulation = Modulation(embedding_dim, input_dim) self.norm_1 = nn.RMSNorm(input_dim) - self.attention = DiTAttention(input_dim, hidden_dim, num_heads, partial_rotary_factor=partial_rotary_factor) + self.attention = DiTAttention( + input_dim, hidden_dim, num_heads, partial_rotary_factor=partial_rotary_factor, base=base + ) self.norm_2 = nn.RMSNorm(input_dim) self.mlp_input = nn.Sequential( nn.Linear(input_dim, mlp_ratio * input_dim), @@ -270,7 +303,8 @@ def forward( self, input: Float[Tensor, "batch_size seq_len embedding_dim"], y: Float[Tensor, "batch_size embedding_dim"], - ) -> Float[Tensor, "batch_size seq_len embedding_dim"]: + shape: tuple[int, int] | None = None, + ) -> Float[Tensor, "batch_size seq_len input_dim"]: """ Forward pass of the DiTBlock applying modulation and attention mechanisms. Args: @@ -280,19 +314,22 @@ def forward( Tensor: The processed input tensor with residual connection """ return ( - checkpoint(self._forward, *(input, y), use_reentrant=False) + checkpoint(self._forward, *(input, y, shape), use_reentrant=False) if self.use_checkpoint - else self._forward(input, y) + else self._forward(input, y, shape) ) # type: ignore def _forward( - self, input: Float[Tensor, "batch_size seq_len embedding_dim"], y: Float[Tensor, "batch_size embedding_dim"] + self, + input: Float[Tensor, "batch_size seq_len embedding_dim"], + y: Float[Tensor, "batch_size embedding_dim"] | Float[Tensor, "batch_size seq_len embedding_dim"], + shape: tuple[int, int] | None = None, ) -> Tensor: modulation: ModulationOut = self.modulation(y) modulated_input = ( input - + self.attention(modulate(self.norm_1(input), scale=modulation.alpha, shift=modulation.beta)) + + self.attention(modulate(self.norm_1(input), scale=modulation.alpha, shift=modulation.beta), shape=shape) * modulation.gamma ) @@ -345,7 +382,10 @@ def __init__( embedding_dim: int, num_heads: int, mlp_ratio: int, - partial_rotary_factor: float = 1, + partial_rotary_factor_input: float = 1, + base_input: int = 100, + partial_rotary_factor_context: float = 1, + base_context: int = 10000, use_checkpoint: bool = False, ): super().__init__() # type: ignore @@ -356,7 +396,14 @@ def __init__( self.input_norm_1 = RMSNorm(input_dim) self.attention = MMDiTAttention( - context_dim, input_dim, hidden_dim, num_heads, partial_rotary_factor=partial_rotary_factor + context_dim, + input_dim, + hidden_dim, + num_heads, + partial_rotary_factor_input, + base_input, + partial_rotary_factor_context, + base_context, ) self.context_norm_2 = RMSNorm(context_dim) @@ -376,16 +423,22 @@ def __init__( def forward( self, - input: Float[Tensor, "batch_size seq_len embedding_dim"], + input: Float[Tensor, "batch_size seq_len_input embedding_dim"], y: Float[Tensor, "batch_size embedding_dim"], - context: Float[Tensor, "batch_size seq_len context_dim"], - ) -> tuple[Float[Tensor, "batch_size seq_len embedding_dim"], Float[Tensor, "batch_size seq_len context_dim"]]: + context: Float[Tensor, "batch_size seq_len_context context_dim"], + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, + shape: tuple[int, int] | None = None, + ) -> tuple[ + Float[Tensor, "batch_size seq_len_input embedding_dim"], Float[Tensor, "batch_size seq_len_context context_dim"] + ]: """ Forward pass of the MMDiT module applying modulation and attention mechanisms. Args: - input (Tensor): The input tensor to be processed - y (Tensor): The conditioning tensor used for modulation - context (Tensor): The context tensor to be processed alongside input + - attn_mask (Tensor | None): Optional attention mask for context + - shape (tuple[int, int] | None): Optional shape for rotary embeddings Returns: tuple: A tuple containing: - Tensor: The modulated and processed input tensor with residual connection @@ -398,9 +451,9 @@ def forward( 5. Residual connections for both input and context paths """ return ( - checkpoint(self._forward, *(input, y, context), use_reentrant=False) + checkpoint(self._forward, *(input, y, context, attn_mask, shape), use_reentrant=False) if self.use_checkpoint - else self._forward(input, y, context) + else self._forward(input, y, context, attn_mask, shape) ) # type: ignore def _forward( @@ -408,6 +461,8 @@ def _forward( input: Float[Tensor, "batch_size seq_len embedding_dim"], y: Float[Tensor, "batch_size embedding_dim"], context: Float[Tensor, "batch_size seq_len context_dim"], + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, + shape: tuple[int, int] | None = None, ): modulation_input: ModulationOut = self.modulation_input(y) modulation_context: ModulationOut = self.modulation_context(y) @@ -417,7 +472,9 @@ def _forward( self.context_norm_1(context), scale=modulation_context.alpha, shift=modulation_context.beta ) - modulated_input, modulated_context = self.attention(modulated_input, modulated_context) + modulated_input, modulated_context = self.attention( + modulated_input, modulated_context, attn_mask=attn_mask, shape=shape + ) modulated_input = input + modulated_input * modulation_input.gamma modulated_context = context + modulated_context * modulation_context.gamma @@ -461,7 +518,51 @@ def forward(self, x: Float[Tensor, "batch_size seq_len dim"], vec: Float[Tensor, class MMDiT(Denoiser): """ - architecture following https://arxiv.org/pdf/2403.03206 + Multimodal DiT architecture following https://arxiv.org/pdf/2403.03206 + + This module implements a DiT-style transformer that can run in two modes: + - simple_dit=True: a single-stream DiT with label conditioning only (no multimodal context). + - simple_dit=False: an MMDiT with self attention with contextual tokens from a `context_embedder`. + + In both modes the input image is patchified with a convolutional projection, processed by a stack + of DiT/MMDiT blocks, then projected back to per-patch predictions and finally unpatchified to + the image space via a modulation-aware last layer. + + Args: + simple_dit (bool): If True, use DiT blocks with class-label conditioning only (no context + or cross-attention). If False, use MMDiT blocks with cross-attention to contextual tokens + produced by `context_embedder`. Default: False. + input_channels (int): Number of channels of the main input x. Default: 3. + output_channels (int | None): Number of channels to predict. If None, equals `input_channels`. + Default: None. + input_dim (int): Token/patch embedding width for the image stream. Also the hidden size used + before the final per-patch projection. Default: 4096. + hidden_dim (int): Inner attention dimension for attention projections. Default: 4096. + embedding_dim (int): Conditioning embedding width (for timestep/labels/pooled context) used + by modulation layers and the last prediction layer. Default: 4096. + num_heads (int): Number of attention heads in each block. Default: 16. + mlp_ratio (int): Expansion ratio for the MLP in each block. Default: 4. + patch_size (int): Side length P of square patches. Images are projected with stride P. Default: 16. + depth (int): Number of DiT/MMDiT blocks. Default: 38. + context_dim (int): Model width for contextual tokens after `context_embed` when + `simple_dit=False`. Ignored when `simple_dit=True`. Default: 4096. + partial_rotary_factor (float): Fraction of each head dimension using RoPE. + 1.0 means full rotary. Default: 1.0. + frequency_embedding (int): Size of the Fourier timestep embedding before the time MLP. + Default: 256. + n_classes (int | None): Number of classes for label conditioning in `simple_dit` mode. + Required to use classifier-free guidance with labels. Must be None when using + a `context_embedder`. Default: None. + classifier_free (bool): Enables classifier-free guidance. In `simple_dit`, it applies to + dropped labels; in MMDiT mode, it is forwarded to the context embedder which may drop + context. Default: False. + context_embedder (ContextEmbedder | None): When `simple_dit=False`, a module returning + `ContextEmbedderOutput`. Must be provided for text conditioning and must be None + when `simple_dit=True`. If the embedder returns pooled and token embeddings + (`n_output == 2`), pooled features are fused into the timestep embedding via an MLP. + Default: None. + use_checkpoint (bool): Enable torch.utils.checkpoint in blocks to trade compute for memory. + Default: False. """ def __init__( @@ -477,7 +578,10 @@ def __init__( patch_size: int = 16, depth: int = 38, context_dim: int = 4096, - partial_rotary_factor: float = 1, + partial_rotary_factor_input: float = 1, + base_input: int = 100, + partial_rotary_factor_context: float = 1, + base_context: int = 10000, frequency_embedding: int = 256, n_classes: int | None = None, classifier_free: bool = False, @@ -502,16 +606,23 @@ def __init__( if not self.simple_dit: assert self.context_embedder is not None, "for MMDiT context embedder must be provided" - assert self.context_embedder.n_output == 2, "for MMDiT context embedder should provide 2 embeddings" assert isinstance(self.context_embedder.output_size, tuple) and all( isinstance(i, int) for i in self.context_embedder.output_size ), "context_embedder.output_size must be a tuple of integers" - self.mlp_pooled_context = nn.Sequential( - nn.Linear(self.context_embedder.output_size[0], embedding_dim), - nn.SiLU(), - nn.Linear(embedding_dim, embedding_dim), - ) - self.context_embed = nn.Linear(self.context_embedder.output_size[1], context_dim) + + self.pooled_embedding = False + self.mlp_pooled_context = None + if self.context_embedder.n_output == 2: + self.pooled_embedding = True + self.mlp_pooled_context = nn.Sequential( + nn.Linear(self.context_embedder.output_size[0], embedding_dim), + nn.SiLU(), + nn.Linear(embedding_dim, embedding_dim), + ) + self.context_embed = nn.Linear(self.context_embedder.output_size[1], context_dim) + else: + assert self.context_embedder.n_output == 1 + self.context_embed = nn.Linear(self.context_embedder.output_size[0], context_dim) else: self.label_embed = ( LabelEmbed(self.n_classes, embedding_dim, self.classifier_free) if self.n_classes is not None else None @@ -540,7 +651,10 @@ def __init__( embedding_dim=embedding_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, - partial_rotary_factor=partial_rotary_factor, + partial_rotary_factor_input=partial_rotary_factor_input, + base_input=base_input, + partial_rotary_factor_context=partial_rotary_factor_context, + base_context=base_context, use_checkpoint=use_checkpoint, ) if not self.simple_dit @@ -550,7 +664,8 @@ def __init__( embedding_dim=embedding_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, - partial_rotary_factor=partial_rotary_factor, + partial_rotary_factor=partial_rotary_factor_input, + base=base_input, use_checkpoint=use_checkpoint, ) for _ in range(depth) @@ -579,6 +694,8 @@ def patchify( self.original_size = (H, W) x = self.conv_proj(x) + _, _, Hp, Wp = x.shape + self.grid_size = (Hp, Wp) x = rearrange(x, "b c h w -> b (h w) c") return x @@ -593,16 +710,16 @@ def unpatchify( Returns: Tensor: Reconstructed image tensor of shape (B, C, H, W) """ - H, W = self.original_size - patch_size = self.patch_size - p = self.output_channels - - # Calculate number of patches in height and width dimensions - h = H // patch_size - w = W // patch_size - # Reshape the tensor to the original image dimensions - x = rearrange(x, "b (h w) (p1 p2 c) -> b c (h p1) (w p2)", h=h, w=w, p1=patch_size, p2=patch_size, c=p) + x = rearrange( + x, + "b (h w) (p1 p2 c) -> b c (h p1) (w p2)", + h=self.grid_size[0], + w=self.grid_size[1], + p1=self.patch_size, + p2=self.patch_size, + c=self.output_channels, + ) return x def mmdit_forward( @@ -615,18 +732,27 @@ def mmdit_forward( ) -> ModelOutput: assert self.context_embedder is not None, "for MMDiT context embedder must be provided" emb = self.time_embed(timestep_embedding(timesteps, self.frequency_embedding)) - context_pooled, context = self.context_embedder(initial_context, p) - context_pooled = self.mlp_pooled_context(context_pooled) + emb + context_output: ContextEmbedderOutput = self.context_embedder(initial_context, p) + if self.pooled_embedding: + assert self.mlp_pooled_context is not None, ( + "for MMDiT with pooled context, mlp_pooled_context must be defined" + ) + assert "pooled_embeddings" in context_output, "pooled embeddings must be in context_output" + context_pooled = context_output["pooled_embeddings"] + emb = self.mlp_pooled_context(context_pooled) + emb + + context = context_output["embeddings"] context = self.context_embed(context) + attn_mask = context_output.get("attn_mask", None) features: list[Tensor] | None = [] if intermediate_features else None # Pass through each layer sequentially for layer in self.layers: - x, context = layer(x, context_pooled, context) + x, context = layer(x, emb, context, attn_mask=attn_mask, shape=self.grid_size) if features: features.append(x) - x = self.last_layer(x, context_pooled) + x = self.last_layer(x, emb) if features: features.append(x) model_output: ModelOutput = {"x": x} @@ -654,7 +780,7 @@ def simple_dit_forward( features: list[Tensor] | None = [] if intermediate_features else None # Pass through each layer sequentially for layer in self.layers: - x = layer(x, emb) + x = layer(x, emb, shape=self.grid_size) if features: features.append(x) @@ -674,6 +800,7 @@ def forward( p: float = 0.0, y: Int[Tensor, "batch_size"] | None = None, x_context: Tensor | None = None, + intermediate_features: bool = False, ) -> ModelOutput: assert not (initial_context is not None and y is not None), "initial_context and y cannot both be specified" if p > 0: @@ -685,9 +812,9 @@ def forward( x = self.patchify(x) if self.simple_dit: - model_output = self.simple_dit_forward(x, timesteps, p, y) + model_output = self.simple_dit_forward(x, timesteps, p, y, intermediate_features) else: - model_output = self.mmdit_forward(x, timesteps, initial_context, p) + model_output = self.mmdit_forward(x, timesteps, initial_context, p, intermediate_features) model_output["x"] = self.unpatchify(model_output["x"]) return model_output diff --git a/src/diffulab/networks/denoisers/unet.py b/src/diffulab/networks/denoisers/unet.py index 7f48be3..af274db 100644 --- a/src/diffulab/networks/denoisers/unet.py +++ b/src/diffulab/networks/denoisers/unet.py @@ -3,12 +3,13 @@ import torch import torch.nn as nn -from jaxtyping import Float, Int +from einops import rearrange +from jaxtyping import Bool, Float, Int from torch import Tensor from torch.utils.checkpoint import checkpoint # type: ignore from diffulab.networks.denoisers.common import Denoiser, ModelOutput -from diffulab.networks.embedders.common import ContextEmbedder +from diffulab.networks.embedders.common import ContextEmbedder, ContextEmbedderOutput from diffulab.networks.utils.nn import ( Downsample, LabelEmbed, @@ -35,7 +36,8 @@ def forward( class ContextBlock(nn.Module): """ - Any module where forward() takes context embeddings as a second argument. + Any module where forward() takes context embeddings as a second argument and + attnention mask as an optional third argument. """ @abstractmethod @@ -43,6 +45,7 @@ def forward( self, x: Float[Tensor, "batch_size channels height width"], context: Float[Tensor, "batch_size context_channels context_length"], + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, ) -> Float[Tensor, "batch_size channels height width"]: """ Apply the module to `x` given `context` embeddings. @@ -60,14 +63,15 @@ def forward( # type:ignore x: Float[Tensor, "batch_size channels height width"], emb: Float[Tensor, "batch_size emb_channels"], context: Float[Tensor, "batch_size context_channels context_length"] | None = None, + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, ) -> Float[Tensor, "batch_size channels height width"]: for layer in self: if isinstance(layer, TimestepBlock) and isinstance(layer, ContextBlock): - x = layer(x, emb, context) + x = layer(x, emb, context, attn_mask=attn_mask) elif isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, ContextBlock): - x = layer(x, context) + x = layer(x, context, attn_mask=attn_mask) else: x = layer(x) return x @@ -271,21 +275,23 @@ def forward( self, x: Float[Tensor, "batch_size channels height width"], context: Float[Tensor, "batch_size context_channels context_length"] | None = None, + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, ) -> Float[Tensor, "batch_size channels height width"]: return ( checkpoint( self._forward, - *(x, context), + *(x, context, attn_mask), use_reentrant=False, ) if self.use_checkpoint - else self._forward(x, context) + else self._forward(x, context, attn_mask) ) # type: ignore def _forward( self, x: Float[Tensor, "batch_size channels height width"], context: Float[Tensor, "batch_size context_channels context_length"] | None = None, + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, ) -> Float[Tensor, "batch_size channels height width"]: b, c, *spatial = x.shape x = x.reshape(b, c, -1) @@ -293,149 +299,233 @@ def _forward( q = self.to_q(self.norm_x(x)) # (b, inner_channels, x_len) k, v = self.to_kv(self.norm_context(context)).chunk(2, dim=1) # (b, inner_channels, context_len) each - x_len = x.shape[-1] - context_len = k.shape[-1] - # reshape for multi-head attention - q = q.view(b * self.num_heads, self.dim_head, x_len) - k = k.view(b * self.num_heads, self.dim_head, context_len) - v = v.view(b * self.num_heads, self.dim_head, context_len) + q, k, v = ( + rearrange(q, "b (h d) n -> b h n d", h=self.num_heads), + rearrange(k, "b (h d) n -> b h n d", h=self.num_heads), + rearrange(v, "b (h d) n -> b h n d", h=self.num_heads), + ) - dots = torch.einsum("bct,bcs->bts", q * self.scale, k * self.scale) # (b*h, x_len, context_len) - attn = self.attend(dots.float()).type(dots.dtype) - attn = self.dropout(attn) + if attn_mask is not None: + b, n = attn_mask.shape + attn_mask = attn_mask[:, None, None, :].expand(b, 1, x.shape[2], n) - out = torch.einsum("bts,bcs->bct", attn, v) - out = out.reshape(b, -1, x_len) # (b, inner_channels, x_len) + out = nn.functional.scaled_dot_product_attention( + query=q, + key=k, + value=v, + scale=self.scale, + attn_mask=attn_mask, + ) + out = rearrange(out, "b h n d -> b (h d) n") out = self.to_out(out) return (x + out).reshape(b, c, *spatial) +class GEGLU(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() # type: ignore[reportUnknownMemberType] + self.proj = nn.Conv1d(in_channels, out_channels * 2, kernel_size=1) + + def forward( + self, x: Float[Tensor, "batch_size in_channels seq_len"] + ) -> Float[Tensor, "batch_size out_channels seq_len"]: + x_proj = self.proj(x) + x, gate = x_proj.chunk(2, dim=1) + return x * torch.nn.functional.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, in_channels: int, inner_channels: int, dropout: float = 0.0): + super().__init__() # type: ignore[reportUnknownMemberType] + self.net = nn.Sequential( + GEGLU(in_channels, inner_channels), + nn.Dropout(dropout), + nn.Conv1d(inner_channels, in_channels, kernel_size=1), + ) + self.norm = normalization(in_channels) + + def forward( + self, x: Float[Tensor, "batch_size channels height width"] + ) -> Float[Tensor, "batch_size channels height width"]: + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + h = self.norm(x) + h = self.net(h) + return (x + h).reshape(b, c, *spatial) + + +class TransformerAttentionBlock(ContextBlock): + def __init__( + self, + channels: int, + context_channels: int | None = None, + num_heads: int = 8, + inner_channels: int = -1, + dropout: float = 0.0, + norm: Callable[..., nn.Module] = normalization, + use_checkpoint: bool = False, + q_bias: bool = True, + kv_bias: bool = True, + mlp_ratio: int = 4, + ): + super().__init__() # type: ignore[reportUnknownMemberType] + self.self_attn = AttentionBlock( + channels, + context_channels=None, + num_heads=num_heads, + inner_channels=inner_channels, + dropout=dropout, + norm=norm, + use_checkpoint=use_checkpoint, + q_bias=q_bias, + kv_bias=kv_bias, + ) + self.cross_attn = AttentionBlock( + channels, + context_channels=context_channels, + num_heads=num_heads, + inner_channels=inner_channels, + dropout=dropout, + norm=norm, + use_checkpoint=use_checkpoint, + q_bias=q_bias, + kv_bias=kv_bias, + ) + self.ff = FeedForward(channels, channels * mlp_ratio, dropout) + + def forward( + self, + x: Float[Tensor, "batch_size channels height width"], + context: Float[Tensor, "batch_size context_channels context_length"] | None = None, + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, + ) -> Float[Tensor, "batch_size channels height width"]: + # residuals are handled in the submodules + h = self.self_attn(x) + h = self.cross_attn(h, context=context, attn_mask=attn_mask) + return self.ff(h) + + +class TransformerBlock(ContextBlock): + def __init__( + self, + channels: int, + context_channels: int | None = None, + num_heads: int = 8, + inner_channels: int = -1, + dropout: float = 0.0, + norm: Callable[..., nn.Module] = normalization, + use_checkpoint: bool = False, + q_bias: bool = True, + kv_bias: bool = True, + mlp_ratio: int = 4, + depth: int = 1, + ): + super().__init__() # type: ignore[reportUnknownMemberType] + self.channels = channels + self.context_channels = context_channels or channels + self.inner_channels = channels if inner_channels == -1 else inner_channels + self.num_heads = num_heads + assert self.inner_channels % self.num_heads == 0, "inner_channels must be divisible by num_heads" + self.dim_head = self.inner_channels // num_heads + + self.norm_x = norm(self.channels) + self.proj_in = nn.Conv2d(self.channels, self.inner_channels, kernel_size=1, stride=1, padding=0) + self.attn_blocks = nn.ModuleList( + [ + TransformerAttentionBlock( + channels=self.inner_channels, + context_channels=self.context_channels, + num_heads=num_heads, + dropout=dropout, + norm=norm, + use_checkpoint=use_checkpoint, + q_bias=q_bias, + kv_bias=kv_bias, + mlp_ratio=mlp_ratio, + ) + for _ in range(depth) + ] + ) + self.proj_out = nn.Conv2d(self.inner_channels, self.channels, kernel_size=1, stride=1, padding=0) + + def forward( + self, + x: Float[Tensor, "batch_size channels height width"], + context: Float[Tensor, "batch_size context_channels context_length"], + attn_mask: Bool[Tensor, "batch_size seq_len_context"] | Int[Tensor, "batch_size seq_len_context"] | None = None, + ) -> Float[Tensor, "batch_size channels height width"]: + assert context is not None, "TransformerBlock requires context input" + h = self.norm_x(x) + h = self.proj_in(h) + for attn_block in self.attn_blocks: + h = attn_block(h, context=context, attn_mask=attn_mask) + h = self.proj_out(h) + return x + h + + class UNetModel(Denoiser): """ - A UNet-based denoising network with residual, attention, and optional class/context conditioning, - inspired by OpenAI's guided-diffusion implementation (MIT license, referenced 2024-08-18). - - The architecture consists of: - - Input convolution followed by a sequence of residual blocks (with optional attention) and - downsampling operations. - - A middle block combining residual + attention + residual structure. - - A symmetric decoding path with skip connections, residual blocks (with optional attention), - and upsampling. - - Final normalization + SiLU + zero-initialized convolution for stable training. - - Supports: - - Class conditioning via learned label embeddings (optional classifier-free guidance). - - Arbitrary external context (e.g. text/image) via a user-provided ContextEmbedder. - - Optional concatenation of auxiliary image-like conditioning input (x_context). - - Attention at user-specified resolutions. - - Optional scale–shift normalization (FiLM-like). - - Optional residual up/downsampling blocks instead of plain (up/down)sample layers. - - Mixed precision (bfloat16) compute path. + U-Net denoiser with optional class-label and/or multimodal context conditioning. - Args: - image_size (list[int]): Spatial size [H, W] expected for all inputs. Enforced at runtime. - in_channels (int): Number of channels in the noisy input tensor x. (If x_context is used, - the effective input channels become in_channels + x_context.channels.) - model_channels (int): Base channel width; channel multipliers expand from this. - out_channels (int): Number of channels in the model output (e.g., predicted noise or image). - num_res_blocks (int): Number of residual blocks at each resolution level (encoder side). - attention_resolutions (list[int]): Downsample factors (relative to input) at which to apply - self-attention (e.g., [4, 8]). - dropout (float, optional): Dropout probability inside residual blocks. Defaults to 0. - channel_mult (str, optional): Comma-separated multipliers per level (parsed via eval into - a list of ints). Defaults to "1, 2, 4, 8". - conv_resample (bool, optional): If True, learnable convolutional (up/down)sampling; - otherwise use nearest + conv or similar lightweight ops. Defaults to True. - use_checkpoint (bool, optional): Enables gradient checkpointing for memory efficiency - (slower compute). Defaults to False. - use_fp16 (bool, optional): If True, internal activations use torch.bfloat16; otherwise float32. - (Name reflects legacy; bfloat16 is used if True.) Defaults to False. - num_heads (int, optional): Number of attention heads in each AttentionBlock. Defaults to 1. - use_scale_shift_norm (bool, optional): Enables scale–shift (FiLM-like) conditioning inside - residual blocks. Defaults to False. - resblock_updown (bool, optional): If True, use residual blocks with internal (up/down)sampling - instead of dedicated (Up/Down)sample modules. Defaults to False. - n_classes (int | None, optional): If provided, enables class-conditioning via LabelEmbed. - Mutually exclusive with context_embedder. Defaults to None. - classifier_free (bool, optional): If True and n_classes is set, label embeddings can be - probabilistically dropped during training for classifier-free guidance. Defaults to False. - context_embedder (ContextEmbedder | None, optional): Module that embeds arbitrary context - (e.g., text tokens, images). Mutually exclusive with n_classes. Defaults to None. - - Attributes: - image_size (list[int]): Expected spatial shape of inputs. - in_channels (int): Base number of channels for the primary input tensor. - out_channels (int): Output channel count (predicted target). - model_channels (int): Base feature width before multipliers. - channel_mult (list[int]): Parsed channel multipliers per resolution stage. - time_embed_dim (int): Dimensionality of the sinusoidal + MLP time embedding. - time_embed (nn.Sequential): Two-layer MLP mapping time features to conditioning embedding. - label_embed (LabelEmbed | None): Label embedding module (if class-conditional). - context_embedder (ContextEmbedder | None): External context conditioning module. - classifier_free (bool): Whether classifier-free guidance is enabled for labels. - dtype (torch.dtype): Chosen compute dtype (bfloat16 or float32). - input_blocks (nn.ModuleList): Encoder path (with optional attention + downsampling). - middle_block (EmbedSequential): Bottleneck block (res-attn-res). - output_blocks (nn.ModuleList): Decoder path (skip connections + optional attention + upsampling). - out (nn.Sequential): Final projection layer producing the model output tensor. - - Forward Args: - x (Tensor): Noisy input image tensor of shape (N, C, H, W); H, W must match image_size. - timesteps (Tensor): 1-D tensor of length N with integer or float diffusion timesteps. - y (Tensor | None, optional): Class label indices (N,) if class conditioning is enabled. - Required iff n_classes is not None. - context (Any | None, optional): Raw context data passed to context_embedder if present. - Required iff context_embedder is not None. - p (float, optional): Probability of dropping labels (classifier-free guidance). Only valid - if classifier_free is True and n_classes is set. Defaults to 0.0. - x_context (Tensor | None, optional): Additional image-like conditioning tensor concatenated - channel-wise with x (shape must be (N, C_ctx, H, W)). Defaults to None. - - Forward Returns: - ModelOutput: A dictionary-like object with: - x (Tensor): The predicted output tensor of shape (N, out_channels, H, W). - - Behavior: - - Timestep embeddings are generated via a sinusoidal embedding followed by an MLP. - - If class-conditional, label embeddings are added to the time embedding (with optional - dropout for classifier-free guidance). - - If context-conditional, the provided context is embedded and supplied to attention layers. - - The encoder stores intermediate feature maps for skip connections used in the decoder. - - Attention is selectively applied at resolutions whose downsample factors are in - attention_resolutions. - - The final conv is zero-initialized, encouraging the network to initially predict zeros - and stabilize training. + This model is a configurable U-Net backbone with: + - timestep embeddings added via ResBlocks (FiLM-style optional scale/shift), + - optional class-label conditioning for classifier-free guidance, + - optional cross-attention conditioning via a ContextEmbedder (Transformer blocks), + - optional gradient checkpointing for reduced memory usage. - Raises: - AssertionError: If input spatial size mismatches image_size. - AssertionError: If label presence mismatches class-conditional configuration. - AssertionError: If context presence mismatches context-conditional configuration. - AssertionError: If p > 0 but classifier-free guidance is not properly enabled. + The spatial encoder-decoder is organized into stages specified by `channel_mult` and + `num_res_blocks`, with attention (self-attn or cross-attn) inserted at resolutions + defined in `attention_resolutions`. If a `context_embedder` is provided (n_output=1), + cross-attention is used; otherwise, self-attention is used. - Notes: - - channel_mult is evaluated via Python eval; ensure trusted input. - - When both n_classes and context_embedder are provided (invalid), initialization fails. + Args: + image_size (list[int]): Spatial size [H, W] the model expects. Inputs must match this + size at training/inference. + in_channels (int): Number of input channels consumed by the first convolution. If you + plan to provide `x_context` to forward (concatenated to x), set this to the + combined channel count (C + C_ctx) to avoid shape mismatch. + model_channels (int): Base channel width. Each level scales this by the factors in + `channel_mult`. + out_channels (int): Number of output channels predicted by the final conv head. + num_res_blocks (int): Number of residual blocks per resolution level (encoder and decoder). + attention_resolutions (list[int]): Downsampling factors at which to insert attention + blocks. The running downsample factor starts at 1 at the highest resolution, + doubles at each downsample, and halves on upsample. If the current factor `ds` + is in this list, an attention (or transformer) block is added at that stage. + dropout (float, default=0.0): Dropout probability used inside ResBlocks and attention FFNs. + channel_mult (str, default="1, 2, 4, 8"): Comma-separated multipliers determining the + channel width at each level: channels[level] = model_channels * channel_mult[level]. + conv_resample (bool, default=True): If True, use convolutional up/downsampling ops + in the simple Upsample/Downsample paths. + use_checkpoint (bool, default=False): Enable torch.utils.checkpoint to trade compute + for reduced activation memory in supported blocks. + num_heads (int, default=1): Number of heads used by attention blocks. + use_scale_shift_norm (bool, default=False): If True, ResBlocks interpret the embedding MLP + output as (scale, shift) for FiLM-like modulation; otherwise the embedding is added. + resblock_updown (bool, default=False): If True, perform up/down sampling within ResBlocks + (learned, anti-aliased). If False, use separate Upsample/Downsample modules. + n_classes (int | None, default=None): Number of classes for class-conditional training. If + set, a label embedding is added to the timestep embedding in forward. + classifier_free (bool, default=False): Enable classifier-free guidance. When True, labels + can be randomly dropped with probability `p` during forward. Requires `n_classes` set. + context_embedder (ContextEmbedder | None, default=None): Optional embedder for external + conditioning (e.g., text). Must have `n_output == 1` and returns: + - embeddings: [B, C_ctx, L_ctx] + - attn_mask (optional): [B, L_ctx] + When provided, cross-attention Transformer blocks are used at attention stages. + transformer_depth (int, default=1): Number of stacked attention+MLP sub-blocks inside each + TransformerBlock inserted at attention resolutions. - Example: - ```python - model = UNetModel( - image_size=[64, 64], - in_channels=3, - model_channels=128, - out_channels=3, - num_res_blocks=2, - attention_resolutions=[4, 8], - dropout=0.1, - channel_mult="1, 2, 4", - n_classes=10, - classifier_free=True, - x = torch.randn(8, 3, 64, 64) - t = torch.randint(0, 1000, (8,)) - labels = torch.randint(0, 10, (8,)) - out = model(x, t, y=labels, p=0.1) - pred = out["x"] - ``` + Notes: + - Timestep embeddings are produced by `timestep_embedding(t, model_channels)` and + projected to `time_embed_dim = 4 * model_channels`. + - If `n_classes` is set, a LabelEmbed module adds label conditioning to the time embedding. + - With classifier-free guidance enabled, labels may be dropped in forward with probability `p`. + - If `context_embedder` is provided, it must have `n_output == 1`; its embeddings and + optional mask are fed to cross-attention Transformer blocks at configured resolutions. + - If you pass `x_context` to forward, ensure `in_channels` already accounts for those + extra channels (i.e., set `in_channels = C + C_ctx`). """ def __init__( @@ -450,13 +540,13 @@ def __init__( channel_mult: str = "1, 2, 4, 8", conv_resample: bool = True, use_checkpoint: bool = False, - use_fp16: bool = False, num_heads: int = 1, use_scale_shift_norm: bool = False, resblock_updown: bool = False, n_classes: int | None = None, classifier_free: bool = False, context_embedder: ContextEmbedder | None = None, + transformer_depth: int = 1, ): super().__init__() # type: ignore assert not (n_classes is not None and context_embedder is not None), ( @@ -464,8 +554,13 @@ def __init__( ) if context_embedder: - assert context_embedder.n_output == 1 + assert context_embedder.n_output == 1, ( + "For UNet please provide a context embedder with n_output=1 (context and attention mask)" + ) # context and attention mask + self.context_channels = None if context_embedder is None else context_embedder.output_size[0] + self.transformer_depth = transformer_depth + self.use_context = self.context_channels is not None self.image_size = image_size self.in_channels = in_channels @@ -477,7 +572,6 @@ def __init__( self.channel_mult: list[int] = eval(f"[{channel_mult}]") self.conv_resample = conv_resample self.use_checkpoint = use_checkpoint - self.dtype = torch.bfloat16 if use_fp16 else torch.float32 self.num_heads = num_heads self.context_embedder = context_embedder self.classifier_free = classifier_free @@ -517,11 +611,19 @@ def __init__( if ds in attention_resolutions: layers.append( AttentionBlock( + ch, + num_heads=num_heads, + dropout=dropout, + use_checkpoint=use_checkpoint, + ) + if not self.use_context + else TransformerBlock( ch, context_channels=self.context_channels, num_heads=num_heads, dropout=dropout, use_checkpoint=use_checkpoint, + depth=self.transformer_depth, ) ) self.input_blocks.append(EmbedSequential(*layers)) @@ -563,6 +665,15 @@ def __init__( num_heads=num_heads, dropout=dropout, use_checkpoint=use_checkpoint, + ) + if not self.use_context + else TransformerBlock( + ch, + context_channels=self.context_channels, + num_heads=num_heads, + dropout=dropout, + use_checkpoint=use_checkpoint, + depth=self.transformer_depth, ), ResBlock( ch, @@ -597,6 +708,15 @@ def __init__( num_heads=num_heads, dropout=dropout, use_checkpoint=use_checkpoint, + ) + if not self.use_context + else TransformerBlock( + ch, + context_channels=self.context_channels, + num_heads=num_heads, + dropout=dropout, + use_checkpoint=use_checkpoint, + depth=self.transformer_depth, ), ) if level and i == num_res_blocks: @@ -713,16 +833,21 @@ class labels and/or external context (e.g. text or images) with support for emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) if self.label_embed is not None: emb = emb + self.label_embed(y, p) + + attn_mask = None if self.context_embedder is not None: - context = self.context_embedder(context, p) + context_output: ContextEmbedderOutput = self.context_embedder(context, p) + context = context_output["embeddings"] + attn_mask = context_output.get("attn_mask") + if x_context is not None: x = torch.cat([x, x_context], dim=1) - h = x.type(self.dtype) + h = x for module in self.input_blocks: - h: Tensor = module(h, emb=emb, context=context) + h: Tensor = module(h, emb=emb, context=context, attn_mask=attn_mask) hs.append(h) - h = self.middle_block(h, emb=emb, context=context) + h = self.middle_block(h, emb=emb, context=context, attn_mask=attn_mask) for module in self.output_blocks: h = torch.cat([h, hs.pop()], dim=1) - h = module(h, emb=emb, context=context) + h = module(h, emb=emb, context=context, attn_mask=attn_mask) return {"x": self.out(h)} diff --git a/src/diffulab/networks/disc/__init__.py b/src/diffulab/networks/disc/__init__.py new file mode 100644 index 0000000..69552bc --- /dev/null +++ b/src/diffulab/networks/disc/__init__.py @@ -0,0 +1,3 @@ +from .rae import RAEDiscriminator + +__all__ = ["RAEDiscriminator"] diff --git a/src/diffulab/networks/disc/rae.py b/src/diffulab/networks/disc/rae.py new file mode 100644 index 0000000..ff7fb13 --- /dev/null +++ b/src/diffulab/networks/disc/rae.py @@ -0,0 +1,122 @@ +from typing import Any, cast + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils.spectral_norm import SpectralNorm +from transformers import ViTImageProcessor, ViTModel + + +class SpectralConv1d(nn.Conv1d): + def __init__(self, *args: Any, **kwargs: Any): + super().__init__(*args, **kwargs) # type: ignore + SpectralNorm.apply(self, name="weight", n_power_iterations=1, dim=0, eps=1e-12) + + +class BatchNormLocal(nn.Module): + def __init__(self, num_features: int, affine: bool = True, virtual_bs: int = 1, eps: float = 1e-6): + super().__init__() # type: ignore + self.virtual_bs = virtual_bs + self.eps = eps + self.affine = affine + + if self.affine: + self.weight = nn.Parameter(torch.ones(num_features)) + self.bias = nn.Parameter(torch.zeros(num_features)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shape = x.size() + x = x.float() + + # Reshape batch into groups. + G = np.ceil(x.size(0) / self.virtual_bs).astype(int) + x = x.view(G, -1, x.size(-2), x.size(-1)) + # Calculate stats. + mean = x.mean([1, 3], keepdim=True) + var = x.var([1, 3], keepdim=True, unbiased=False) + x = (x - mean) / (torch.sqrt(var + self.eps)) + + if self.affine: + x = x * self.weight[None, :, None] + self.bias[None, :, None] + + return x.view(shape) + + +class ResidualBlock(nn.Module): + def __init__(self, fn: nn.Module): + super().__init__() # type: ignore + self.fn = fn + self.ratio = 1 / np.sqrt(2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # x = x.float() + return (self.fn(x).add(x)).mul_(self.ratio) + + +class RAEDiscriminator(nn.Module): + def __init__(self, model_name: str = "facebook/dino-vits8", features_depth: list[int] = [2, 5, 8, 11]): + super().__init__() # type: ignore + image_processor: ViTImageProcessor = ViTImageProcessor.from_pretrained(model_name, do_rescale=False) # type: ignore + self.register_buffer("image_mean", torch.tensor(image_processor.image_mean).view(1, -1, 1, 1)) + self.register_buffer("image_std", torch.tensor(image_processor.image_std).view(1, -1, 1, 1)) + self.image_size: tuple[int, int] = (image_processor.size["height"], image_processor.size["width"]) + + self.dino_model: ViTModel = ViTModel.from_pretrained(model_name, add_pooling_layer=False) # type: ignore + self.features_depth = features_depth + + self.dino_model.eval() + for param in self.dino_model.parameters(): + param.requires_grad = False + + channels = self.dino_model.config.hidden_size + self.heads = nn.ModuleList( + [ + nn.Sequential( + SpectralConv1d(channels, channels, kernel_size=9, padding=9 // 2, padding_mode="circular"), + BatchNormLocal(channels, eps=1e-6), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + ResidualBlock( + nn.Sequential( + SpectralConv1d(channels, channels, kernel_size=9, padding=9 // 2, padding_mode="circular"), + BatchNormLocal(channels, eps=1e-6), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + ) + ), + SpectralConv1d(channels, 1, kernel_size=1, padding=0), + ) + for _ in range(len(features_depth)) + ] + ) + # train heads + for p in self.heads: + p.requires_grad_(True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + forward pass through the discriminator + Args: + x (torch.Tensor): input images (B, C, H, W) in range [0, 1] + Returns: + torch.Tensor: discriminator outputs (B, sum of N_i) + """ + inputs = x.float() + inputs = cast( + torch.Tensor, + F.interpolate(inputs, size=self.image_size, mode="bilinear", align_corners=False, antialias=True), # type: ignore + ) + inputs = (inputs - self.image_mean.to(dtype=inputs.dtype, device=x.device)) / self.image_std.to( # type: ignore + dtype=inputs.dtype, device=inputs.device + ) + inputs = inputs.to(dtype=x.dtype) # type: ignore + features: list[torch.Tensor] = list( + self.dino_model(pixel_values=inputs, output_hidden_states=True).hidden_states + ) # type: ignore + activations = [ + features[i + 1][:, 1:, :].transpose(1, 2) for i in self.features_depth + ] # skip cls token and transpose to (B, C, N) + outputs: list[torch.Tensor] = [] + for head, act in zip(self.heads, activations): + out = head(act).view(x.shape[0], -1) + outputs.append(out) + return torch.cat(outputs, dim=1) diff --git a/src/diffulab/networks/embedders/__init__.py b/src/diffulab/networks/embedders/__init__.py index bb64eb6..5a397c8 100644 --- a/src/diffulab/networks/embedders/__init__.py +++ b/src/diffulab/networks/embedders/__init__.py @@ -1,3 +1,4 @@ -from .text import SD3TextEmbedder +from .qwen import QwenTextEmbedder +from .sd3 import SD3TextEmbedder -__all__ = ["SD3TextEmbedder"] +__all__ = ["SD3TextEmbedder", "QwenTextEmbedder"] diff --git a/src/diffulab/networks/embedders/common.py b/src/diffulab/networks/embedders/common.py index 90888b9..23ec38f 100644 --- a/src/diffulab/networks/embedders/common.py +++ b/src/diffulab/networks/embedders/common.py @@ -1,29 +1,36 @@ from abc import ABC, abstractmethod -from typing import Any +from typing import Any, NotRequired, Required, TypedDict import torch.nn as nn from torch import Tensor +class ContextEmbedderOutput(TypedDict): + embeddings: Required[Tensor] + pooled_embeddings: NotRequired[Tensor] + attn_mask: NotRequired[Tensor] + + class ContextEmbedder(nn.Module, ABC): + _n_output: int + _output_size: tuple[int, ...] + def __init__(self): super().__init__() # type: ignore @property - @abstractmethod def n_output(self) -> int: """ Represents the number of output embedding the embedder is returning. """ - pass + return self._n_output @property - @abstractmethod def output_size(self) -> tuple[int, ...]: """ Represents the dimension of each output embedding. """ - pass + return self._output_size @abstractmethod def drop_conditions( @@ -43,7 +50,7 @@ def drop_conditions( pass @abstractmethod - def forward(self, context: Any, p: float) -> tuple[Tensor, ...]: + def forward(self, context: Any, p: float = 0) -> ContextEmbedderOutput: """ Apply the model to an input batch. @@ -51,6 +58,7 @@ def forward(self, context: Any, p: float) -> tuple[Tensor, ...]: context (Any): the input batch, can be a tensor or a list of str for example. p (float): the probability of dropping the context. Returns: - tuple[Tensor, ...]: a tuple of tensors representing the embeddings. + ContextEmbedderOutput: a dictionary containing the output embeddings and + optionally pooled embeddings and mask. """ pass diff --git a/src/diffulab/networks/embedders/qwen.py b/src/diffulab/networks/embedders/qwen.py new file mode 100644 index 0000000..0ce080b --- /dev/null +++ b/src/diffulab/networks/embedders/qwen.py @@ -0,0 +1,86 @@ +import random +from typing import cast + +import torch +from torch import Tensor +from transformers import Qwen2Tokenizer, Qwen3VLForConditionalGeneration + +from diffulab.networks.embedders.common import ContextEmbedder, ContextEmbedderOutput + + +class QwenTextEmbedder(ContextEmbedder): + def __init__( + self, model_id: str = "Qwen/Qwen3-VL-2B-Instruct", device: str | torch.device = "cuda", max_length: int = 1024 + ) -> None: + super().__init__() + self.model: Qwen3VLForConditionalGeneration = Qwen3VLForConditionalGeneration.from_pretrained( # type: ignore + model_id, device_map=device, dtype="auto" + ) + self.model.eval() + self.model.requires_grad_(False) + self.tokenizer: Qwen2Tokenizer = Qwen2Tokenizer.from_pretrained(model_id, device_map=device) # type: ignore + self.prompt_template: str = self._get_prompt_template() + self.prompt_template_encoder_start_idx = 34 + self.max_length = max_length + + self._output_size: tuple[int] = (self.model.config.text_config.hidden_size,) + self._n_output = 1 + + def _get_prompt_template(self) -> str: + return ( + "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, " + "spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n" + "<|im_start|>assistant\n" + ) + + def drop_conditions(self, context: list[str], p: float) -> list[str]: + """Randomly drop prompts for classifier-free guidance style training. + + Args: + context (list[str]): Original list of prompt strings. + p (float): Drop probability. + + Returns: + list[str]: context with some entries replaced by empty strings. + """ + return ["" if random.random() < p else c for c in context] + + def forward(self, context: list[str], p: float = 0) -> ContextEmbedderOutput: + """Compute Qwen text embeddings. + + Args: + context (list[str]): List of input prompt strings. + p (float): Probability of dropping the condition (replacing with empty string). + + Returns: + ContextEmbedderOutput: + Sequence embeddings of shape ``[B, N_ctx, D]`` where ``N_ctx`` is the number of tokens after prompt template + and ``D`` is the model hidden size. + Attention mask of shape ``[B, N_ctx]`` indicating non-padding tokens. + + """ + context_dropped = self.drop_conditions(context, p) + prompt_texts = [self.prompt_template.format(c) for c in context_dropped] + tokens = self.tokenizer( + prompt_texts, + max_length=self.max_length + self.prompt_template_encoder_start_idx, + padding=True, + truncation=True, + return_tensors="pt", + ).to(self.model.device) + + embeddings = cast( + Tensor, + self.model( + input_ids=tokens.input_ids, # type: ignore + attention_mask=tokens.attention_mask, # type: ignore + output_hidden_states=True, + ).hidden_states[-1], + ) + + embeddings = embeddings[:, self.prompt_template_encoder_start_idx :, :] + + return ContextEmbedderOutput( + sequence_embeddings=embeddings, + attention_mask=tokens.attention_mask.bool(), # type: ignore + ) diff --git a/src/diffulab/networks/embedders/sd3.py b/src/diffulab/networks/embedders/sd3.py new file mode 100644 index 0000000..af3d98f --- /dev/null +++ b/src/diffulab/networks/embedders/sd3.py @@ -0,0 +1,216 @@ +import random +from typing import TYPE_CHECKING, cast + +import torch +from jaxtyping import Bool, Float +from torch import Tensor +from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer + +from diffulab.networks.embedders.common import ContextEmbedder, ContextEmbedderOutput + +if TYPE_CHECKING: + from transformers.models.clip.tokenization_clip_fast import CLIPTokenizerFast + + +class SD3TextEmbedder(ContextEmbedder): + """ + Composite text embedder combining CLIP-L/14, CLIP-bigG/14 and T5 XXL. + + This class mirrors the text conditioning strategy used by Stable Diffusion 3 + where multiple powerful language / text encoders are fused. Each encoder can + be toggled on/off at construction. + + The forward path returns two tensors: + + * pooled: Concatenated pooled CLIP embeddings of shape ``[B, 2048]`` + (``768 + 1280``) when both CLIP encoders are enabled. + * full_encoding: A composite sequence embedding formed by (a) concatenating + the token-level hidden states of CLIP-L/14 and CLIP-bigG/14 along the last + dimension with zero padding to match the T5 hidden size (4096), then (b) + concatenating that padded CLIP block with the T5 hidden states along the + sequence dimension. Resulting shape is ``[B, N_clip + N_t5, 4096]`` where + ``N_clip`` is the (padded) CLIP token length and ``N_t5`` the T5 token length. + + Args: + device (str | torch.device): Torch device or device string for model instantiation & tensors. + + """ + + def __init__(self, device: str | torch.device = "cuda") -> None: + super().__init__() + self.device = device + self._output_size = (2048, 4096) # pooled, full_encoding + self._n_output = 2 + + # load l_14 + self.clip_l14 = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", device_map=device) # type: ignore + self.tokenizer_l14: CLIPTokenizerFast = AutoTokenizer.from_pretrained( # type: ignore + "openai/clip-vit-large-patch14", device_map=device + ) + self.clip_l14.eval() + self.clip_l14.requires_grad_(False) + + # load g_14 + self.clip_g14 = CLIPTextModel.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160", device_map=device) # type: ignore + + self.tokenizer_g14: CLIPTokenizer = CLIPTokenizer.from_pretrained( # type: ignore + "laion/CLIP-ViT-bigG-14-laion2B-39B-b160", device_map=device + ) + self.clip_g14.eval() + self.clip_g14.requires_grad_(False) + + # load t5 + self.t5 = T5EncoderModel.from_pretrained("google/t5-v1_1-xl", from_tf=True) # type: ignore + self.tokenizer_t5 = cast( + T5Tokenizer, + T5Tokenizer.from_pretrained( # type: ignore + "google/t5-v1_1-xl", clean_up_tokenization_spaces=True, legacy=False + ), + ) + self.t5.eval() # pyright: ignore[reportUnknownMemberType] + self.t5.requires_grad_(False) # pyright: ignore[reportUnknownMemberType] + + def get_l14_embeddings( + self, context: list[str] + ) -> tuple[ + Float[Tensor, "batch_size seq_len 768"], Float[Tensor, "batch_size 768"], Bool[Tensor, "batch_size seq_len"] + ]: + """Compute CLIP ViT-L/14 token and pooled embeddings. Return attention mask too. + + Args: + context (list[str]): List of input prompt strings. + + Returns: + tuple[Tensor, Tensor]: + * last_hidden_state: Shape ``[B, N_ctx, 768]`` + * pooled_output: Shape ``[B, 768]`` (CLIP text projection output) + * attention_mask: Shape ``[B, N_ctx]`` + """ + inputs_l14 = self.tokenizer_l14(context, return_tensors="pt", padding=True).to(self.device) + outputs_l14 = self.clip_l14(**inputs_l14) + last_hidden_state = outputs_l14["last_hidden_state"] # [batch_size, n_ctx, 768] + pooled_output = outputs_l14["pooler_output"] # [batch_size, 768] + attention_mask = cast( + Tensor, + inputs_l14.attention_mask.bool(), # type: ignore + ) # [batch_size, n_ctx] + return last_hidden_state, pooled_output, attention_mask + + def get_g14_embeddings( + self, context: list[str] + ) -> tuple[ + Float[Tensor, "batch_size seq_len 1280"], Float[Tensor, "batch_size 1280"], Bool[Tensor, "batch_size seq_len"] + ]: + """Compute CLIP ViT-bigG/14 token and pooled embeddings. + + Args: + context (list[str]): List of input prompt strings. + + Returns: + tuple[Tensor, Tensor]: + * last_hidden_state: Shape ``[B, N_ctx, 1280]`` + * pooled_output: Shape ``[B, 1280]`` + * attention_mask: Shape ``[B, N_ctx]`` + """ + inputs_g14 = self.tokenizer_g14(context, return_tensors="pt", padding=True).to(self.device) + outputs_g14 = self.clip_g14(**inputs_g14) + last_hidden_state = outputs_g14["last_hidden_state"] # [batch_size, n_ctx, 768] + pooled_output = outputs_g14["pooler_output"] # [batch_size, 768] + attention_mask = cast( + Tensor, + inputs_g14.attention_mask.bool(), # type: ignore + ) # [batch_size, n_ctx] + return last_hidden_state, pooled_output, attention_mask + + def get_t5_embeddings( + self, context: list[str] + ) -> tuple[Float[Tensor, "batch_size seq_len 4096"], Bool[Tensor, "batch_size seq_len"]]: + """Compute T5 XXL token-level embeddings. + + Args: + context (list[str]): List of input prompt strings. + + Returns: + tuple[Tensor, Tensor]: + * last_hidden_state: Shape ``[B, N_ctx, 4096]`` + * attention_mask: Shape ``[B, N_ctx]`` + + """ + inputs_t5_list: dict[str, list[int]] = self.tokenizer_t5(context, padding=True) # type: ignore + inputs_t5: dict[str, Tensor] = { + key: torch.tensor(value, dtype=torch.long, device=self.device) + for key, value in inputs_t5_list.items() # type: ignore + } + last_hidden_state: Tensor = self.t5(**inputs_t5)["last_hidden_state"] # [batch_size, n_ctx, 4096] + attention_mask = inputs_t5["attention_mask"].bool() # [batch_size, n_ctx] + return last_hidden_state, attention_mask + + def get_embeddings(self, context: list[str]) -> dict[str, Tensor]: + """Obtain embeddings + + Args: + context (list[str]): List of input prompt strings. + + Returns: + dict[str, Tensor]: Mapping with keys among ``{"l14", "l14_pooled", "g14", "g14_pooled", "t5"}``. + """ + assert self.loaded_weights, "Cannot get embeddings when model weights are not loaded." + outputs: dict[str, Tensor] = {} + + outputs_l14 = self.get_l14_embeddings(context) + outputs["l14"] = outputs_l14[0] + outputs["l14_pooled"] = outputs_l14[1] + outputs["l14_attn_mask"] = outputs_l14[2] + + outputs_g14 = self.get_g14_embeddings(context) + outputs["g14"] = outputs_g14[0] + outputs["g14_pooled"] = outputs_g14[1] + outputs["g14_attn_mask"] = outputs_g14[2] + + outputs_t5 = self.get_t5_embeddings(context) + outputs["t5"] = outputs_t5[0] + outputs["t5_attn_mask"] = outputs_t5[1] + return outputs + + def drop_conditions(self, context: list[str], p: float) -> list[str]: + """Randomly drop prompts for classifier-free guidance style training. + + Args: + context (list[str]): Original list of prompt strings. + p (float): Drop probability. + + Returns: + list[str]: context with some entries replaced by empty strings. + """ + return ["" if random.random() < p else c for c in context] + + def forward(self, context: list[str], p: float = 0) -> ContextEmbedderOutput: + """Forward pass producing pooled and composite sequence embeddings. + + Args: + context: List of input prompt strings. + p: Probability to drop each prompt (per encoder) for classifier-free guidance. + + Returns: + ContextEmbedderOutput: Dictionary with keys: + * embeddings: Composite sequence embeddings, shape ``[B, N_clip + N_t5, 4096]`` + * pooled_embeddings: Concatenated CLIP pooled embeddings, shape ``[B, 2048]`` + * attn_mask: Attention mask for the composite embeddings, shape ``[B, N_clip + N_t5]`` + """ + context = self.drop_conditions(context, p) + embeddings = self.get_embeddings(context) + pooled = torch.cat([embeddings["l14_pooled"], embeddings["g14_pooled"]], dim=-1) + full_encoding_clip = torch.cat( + [ + embeddings["l14"], + embeddings["g14"], + torch.zeros(embeddings["t5"].shape[-1] - (embeddings["l14"].shape[-1] + embeddings["g14"].shape[-1])), + ], + dim=-1, + ) + assert embeddings["l14_attn_mask"] == embeddings["g14_attn_mask"], ( + "Mismatched CLIP attention masks." + ) # should be same tokenizer + full_encoding = torch.cat([full_encoding_clip, embeddings["t5"]], dim=-2) + attention_mask = torch.cat([embeddings["l14_attn_mask"], embeddings["t5_attn_mask"]], dim=-1).bool() + return ContextEmbedderOutput(embeddings=full_encoding, pooled_embeddings=pooled, attn_mask=attention_mask) diff --git a/src/diffulab/networks/embedders/text.py b/src/diffulab/networks/embedders/text.py deleted file mode 100644 index c22db4c..0000000 --- a/src/diffulab/networks/embedders/text.py +++ /dev/null @@ -1,271 +0,0 @@ -import gc -import random -from pathlib import Path - -import torch -from jaxtyping import Float -from open_clip import create_model_from_pretrained, get_tokenizer # type: ignore -from torch import Tensor -from transformers import AutoTokenizer, CLIPTextModel, T5EncoderModel # type: ignore - -from diffulab.networks.embedders.common import ContextEmbedder - - -class SD3TextEmbedder(ContextEmbedder): - """ - Composite text embedder combining CLIP-L/14, CLIP-bigG/14 and T5 XXL. - - This class mirrors the text conditioning strategy used by Stable Diffusion 3 - where multiple powerful language / text encoders are fused. Each encoder can - be toggled on/off at construction. - - The forward path returns two tensors: - - * pooled: Concatenated pooled CLIP embeddings of shape ``[B, 2048]`` - (``768 + 1280``) when both CLIP encoders are enabled. - * full_encoding: A composite sequence embedding formed by (a) concatenating - the token-level hidden states of CLIP-L/14 and CLIP-bigG/14 along the last - dimension with zero padding to match the T5 hidden size (4096), then (b) - concatenating that padded CLIP block with the T5 hidden states along the - sequence dimension. Resulting shape is ``[B, N_clip + N_t5, 4096]`` where - ``N_clip`` is the (padded) CLIP token length and ``N_t5`` the T5 token length. - - Args: - device (str | torch.device): Torch device or device string for model instantiation & tensors. - load_l14 (bool): Whether to load CLIP ViT-L/14 encoder. - load_g14 (bool): Whether to load CLIP ViT-bigG/14 encoder. - load_t5 (bool): Whether to load T5 v1.1 XXL encoder. - """ - - def __init__( - self, device: str | torch.device = "cuda", load_l14: bool = True, load_g14: bool = True, load_t5: bool = True - ) -> None: - super().__init__() - self.device = device - - self.load_l14 = load_l14 - if load_l14: - self.clip_l14 = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", device_map=device) # type: ignore - self.tokenizer_l14 = AutoTokenizer.from_pretrained("openai/clip-vit-large-patch14", device_map=device) # type: ignore - - self.load_g14 = load_g14 - if load_g14: - self.clip_g14 = create_model_from_pretrained( # type: ignore - "hf-hub:laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", device=device - )[0] # type: ignore - self.tokenizer_g14 = get_tokenizer("hf-hub:laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") - if hasattr(self.clip_g14, "visual"): # type: ignore - del self.clip_g14.visual # type: ignore - gc.collect() - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - self.load_t5 = load_t5 - if load_t5: - self.t5 = T5EncoderModel.from_pretrained("google/t5-v1_1-xxl") # type: ignore - self.tokenizer_t5 = AutoTokenizer.from_pretrained( # type: ignore - "google/t5-v1_1-xxl", clean_up_tokenization_spaces=True, legacy=False - ) - - self.outputs_size = (2048, 4096) - - @property - def n_output(self) -> int: - """Number of logical outputs produced by the embedder. - - Returns: - int: Always ``2`` (``pooled`` and ``full_encoding``). - """ - return 2 - - @property - def output_size(self) -> tuple[int, int]: - """Dimensionalities of the two outputs. - - Returns: - tuple[int, int]: ``(2048, 4096)`` representing the feature dimension - of the pooled CLIP embedding and the hidden size of the composite - sequence embedding respectively. - """ - return (2048, 4096) - - def dict_to_device(self, d: dict[str, Tensor]) -> dict[str, Tensor]: - """Move all tensors in a dictionary to the configured device. - - Args: - d (dict[str, Tensor]): Mapping of string keys to tensors. - - Returns: - dict[str, Tensor]: Same mapping with tensors moved to ``self.device``. - """ - return {k: v.to(self.device) for k, v in d.items()} - - def get_l14_embeddings( - self, context: list[str] - ) -> tuple[Float[Tensor, "batch_size seq_len 768"], Float[Tensor, "batch_size 768"]]: - """Compute CLIP ViT-L/14 token and pooled embeddings. - - Args: - context (list[str]): List of input prompt strings. - - Returns: - tuple[Tensor, Tensor]: - * last_hidden_state: Shape ``[B, N_ctx, 768]`` - * pooled_output: Shape ``[B, 768]`` (CLIP text projection output) - """ - inputs_l14 = self.dict_to_device(self.tokenizer_l14(context, return_tensors="pt", padding=True)) # type: ignore - outputs_l14 = self.clip_l14(**inputs_l14) # type: ignore - last_hidden_state = outputs_l14["last_hidden_state"] # [batch_size, n_ctx, 768] - pooled_output = outputs_l14["pooler_output"] # [batch_size, 768] - return last_hidden_state, pooled_output - - def get_g14_embeddings( - self, context: list[str] - ) -> tuple[Float[Tensor, "batch_size seq_len 1280"], Float[Tensor, "batch_size 1280"]]: - """Compute CLIP ViT-bigG/14 token and pooled embeddings. - - Args: - context (list[str]): List of input prompt strings. - - Returns: - tuple[Tensor, Tensor]: - * last_hidden_state: Shape ``[B, N_ctx, 1280]`` - * pooled_output: Shape ``[B, 1280]`` (selection at EOS token) - """ - inputs_g14 = self.tokenizer_g14(context).to(self.device) - cast_dtype = self.clip_g14.get_cast_dtype() # type: ignore - - x = self.clip_g14.token_embedding(inputs_g14).to(cast_dtype) # [batch_size, n_ctx, d_model] # type: ignore - x = x + self.clip_g14.positional_embedding.to(cast_dtype) # type: ignore - x = self.clip_g14.transformer(x, attn_mask=self.attn_mask) # type: ignore - last_hidden_state: Tensor = self.clip_g14.ln_final(x) # [batch_size, n_ctx, 1280] # type: ignore - pooled_output: Tensor = last_hidden_state[ # type: ignore - torch.arange(last_hidden_state.shape[0]), # type: ignore - inputs_g14.to(dtype=torch.int, device=self.device).argmax(dim=-1), - ] - - return last_hidden_state, pooled_output # type: ignore - - def get_t5_embeddings(self, context: list[str]) -> Float[Tensor, "batch_size seq_len 4096"]: - """Compute T5 XXL token-level embeddings. - - Args: - context (list[str]): List of input prompt strings. - - Returns: - Tensor: Last hidden state of shape ``[B, N_ctx, 4096]``. - """ - inputs_t5_list: dict[str, list[int]] = self.tokenizer_t5(context) # type: ignore - inputs_t5: dict[str, Tensor] = { - key: torch.tensor(value, dtype=torch.long, device=self.device) - for key, value in inputs_t5_list.items() # type: ignore - } - last_hidden_state: Tensor = self.t5(**inputs_t5)["last_hidden_state"] # [batch_size, n_ctx, 4096] - return last_hidden_state - - def get_embeddings(self, context: list[str]) -> dict[str, Tensor]: - """Obtain embeddings for the enabled encoders. - - Args: - context (list[str]): List of input prompt strings. - - Returns: - dict[str, Tensor]: Mapping with keys among ``{"l14", "l14_pooled", "g14", "g14_pooled", "t5"}``. - """ - outputs: dict[str, Tensor] = {} - if self.load_l14: - outputs_l14 = self.get_l14_embeddings(context) # [batch_size, n_ctx, 768] - outputs["l14"] = outputs_l14[0] - outputs["l14_pooled"] = outputs_l14[1] - if self.load_g14: - outputs_g14 = self.get_g14_embeddings(context) # [batch_size, n_ctx, 1280] - outputs["g14"] = outputs_g14[0] - outputs["g14_pooled"] = outputs_g14[1] - if self.load_t5: - outputs_t5 = self.get_t5_embeddings(context) # [batch_size, n_ctx, 4096] - outputs["t5"] = outputs_t5 - return outputs - - def create_cache_from_list( - self, context: list[str], batch_size: int = 8, path_to_save: str | Path = Path.home() / "saved_embeddings" - ) -> None: - """Compute and persist embeddings for a list of prompts. - - Each prompt's pooled CLIP embedding and composite full encoding are - stored separately as ``.pt`` along with a ``.txt`` file - containing the raw prompt string to allow later reconstruction. - - Existing cached indices are detected so new prompts append seamlessly. - - Args: - context: List of prompt strings to encode. - batch_size: Number of prompts per forward pass. ``-1`` processes all at once. - path_to_save: Directory where embeddings will be written / appended. - """ - path_to_save = Path(path_to_save) - path_to_save.mkdir(parents=True, exist_ok=True) - - # Check for existing embeddings and set the starting index - existing_files = list(path_to_save.glob("*.pt")) - if existing_files: - existing_indices = [int(f.stem) for f in existing_files if f.stem.isdigit()] - begins = max(existing_indices) + 1 - else: - begins = 0 - - if batch_size == -1: - batch_size = len(context) - - for i in range(0, len(context), batch_size): - embeddings = self.forward(context[i : i + batch_size], 0.0) - for b in range(batch_size): - torch.save( # type: ignore - {"pooled": embeddings[0][b], "full_encoding": embeddings[1][b]}, - path_to_save / f"{begins + i + b}.pt", - ) - with (path_to_save / f"{begins + i + b}.txt").open("r") as f: - f.write(context[i + b]) - - def drop_conditions(self, context: list[str], p: float) -> list[str]: - """Randomly drop prompts for classifier-free guidance style training. - - Args: - context (list[str]): Original list of prompt strings. - p (float): Drop probability. - - Returns: - list[str]: context with some entries replaced by empty strings. - """ - return ["" if random.random() < p else c for c in context] - - def forward( - self, context: list[str], p: float - ) -> tuple[Float[Tensor, "batch_size 2048"], Float[Tensor, "batch_size seq_len 4096"]]: - """Forward pass producing pooled and composite sequence embeddings. - - Args: - context: List of input prompt strings. - p: Probability to drop each prompt (per encoder) for classifier-free guidance. - - Returns: - tuple[Tensor, Tensor]: - * pooled: Shape ``[B, 2048]`` concatenated pooled CLIP features. - * full_encoding: Shape ``[B, seq_len, 4096]`` composite sequence embedding. - - Raises: - AssertionError: If any of the required encoders (l14, g14, t5) were disabled at init. - """ - assert self.load_l14 and self.load_g14 and self.load_t5 - context = self.drop_conditions(context, p) - embeddings = self.get_embeddings(context) - pooled = torch.cat([embeddings["l14_pooled"], embeddings["g14_pooled"]], dim=-1) - full_encoding_clip = torch.cat( - [ - embeddings["l14"], - embeddings["g14"], - torch.zeros(embeddings["t5"].shape[-1] - (embeddings["l14"].shape[-1] + embeddings["g14"].shape[-1])), - ], - dim=-1, - ) - full_encoding = torch.cat([full_encoding_clip, embeddings["t5"]], dim=-2) - return pooled, full_encoding diff --git a/src/diffulab/networks/repa/common.py b/src/diffulab/networks/repa/common.py index a877a06..2f255d1 100644 --- a/src/diffulab/networks/repa/common.py +++ b/src/diffulab/networks/repa/common.py @@ -35,9 +35,7 @@ def embedding_dim(self) -> int: """ @abstractmethod - def preprocess( - self, x: Float[Tensor, "batch_size channels height width"] - ) -> Float[Tensor, "batch_size channels height width"]: + def preprocess(self, x: Float[Tensor, "batch_size channels height width"]) -> Any: """ Preprocess the input tensor before encoding. @@ -46,7 +44,7 @@ def preprocess( Assumed to be an image tensor with shape [N, C, H, W]. Returns: - Tensor: Preprocessed input tensor. + Any: Preprocessed input tensor or format expected by the encoder. """ @abstractmethod @@ -61,6 +59,7 @@ def forward(self, x: Float[Tensor, "batch_size channels height width"]) -> Float Tensor: Encoded representation of the input tensor. """ + @torch.inference_mode() def compute_on_dataset( self, dataset_path: str, @@ -119,7 +118,7 @@ def _collate_fn(batch: list[dict[str, Any]]) -> dict[str, Any]: # Move to correct device and dtype images = images.to(dtype=dtype, device=device) # Compute embeddings - embeddings = self.forward(images) + embeddings = self.forward(images).float() embeddings_numpy = embeddings.cpu().numpy() # type: ignore[reportUnknownMemberType] if target_type == "float32": diff --git a/src/diffulab/networks/repa/dinov2.py b/src/diffulab/networks/repa/dinov2.py index 8d6dd50..2f3b145 100644 --- a/src/diffulab/networks/repa/dinov2.py +++ b/src/diffulab/networks/repa/dinov2.py @@ -17,7 +17,7 @@ class DinoV2(REPA): def __init__( self, - dino_model: str = f"dinov2_vitl14_reg", + dino_model: str = "dinov2_vitl14_reg", resolution: int = 256, target_seq_len: int | None = None, ) -> None: @@ -37,8 +37,8 @@ def __init__( grid_size = int(sqrt_val) self.inference_resolution = grid_size * self.base_patch_pixel_size - self._encoder.pos_embed.data = timm.layers.pos_embed.resample_abs_pos_embed( - self._encoder.pos_embed.data, # type: ignore + self._encoder.pos_embed.data = timm.layers.pos_embed.resample_abs_pos_embed( # type: ignore + self._encoder.pos_embed.data, [grid_size, grid_size], ) @@ -68,20 +68,18 @@ def embedding_dim(self) -> int: def preprocess( self, x: Float[Tensor, "batch_size channels height width"] ) -> Float[Tensor, "batch_size channels height width"]: + # ensure float x = x.float() x_min = x.min().item() x_max = x.max().item() - # Normalize to [0,1] - if x_min >= -1.0 and x_max <= 1.0 and x_min < 0.0: - x = (x + 1.0) / 2.0 - elif x_min >= 0.0 and x_max <= 1.0: + if x_min >= 0.0 and x_max <= 1.0: pass - elif x_min >= 0.0 and x_max <= 255.0: + elif x_min >= 0.0 and x_max <= 255.0 and x_max > 1.0: x = x / 255.0 else: - raise ValueError("Input tensor range is not supported. Expected 0–255, 0–1, or -1–1.") + raise ValueError("Input tensor range is not supported. Expected 0–255 or 0–1") x = x.clamp(0.0, 1.0) @@ -91,6 +89,7 @@ def preprocess( return x + @torch.inference_mode() def forward(self, x: Float[Tensor, "batch_size channels height width"]) -> Float[Tensor, "batch_size seq_len dim"]: """ Forward pass of the encoder. diff --git a/src/diffulab/networks/repa/dinov3.py b/src/diffulab/networks/repa/dinov3.py new file mode 100644 index 0000000..c88f0a7 --- /dev/null +++ b/src/diffulab/networks/repa/dinov3.py @@ -0,0 +1,118 @@ +from typing import TYPE_CHECKING, cast + +import numpy as np +import torch +from jaxtyping import Float +from numpy.typing import NDArray +from PIL import Image +from torch import Tensor +from transformers import AutoImageProcessor, AutoModel + +from diffulab.networks.repa.common import REPA + +if TYPE_CHECKING: + from transformers.feature_extraction_utils import BatchFeature + from transformers.modeling_outputs import BaseModelOutputWithPooling + from transformers.models.dinov3_vit import ( + DINOv3ViTImageProcessorFast, + DINOv3ViTModel, + ) + + +class DinoV3(REPA): + def __init__( + self, + dino_model: str = "facebook/dinov3-vitb16-pretrain-lvd1689m", + cancel_affine: bool = False, + size: tuple[int, int] = (256, 256), + ) -> None: + super().__init__() + self.processor = cast("DINOv3ViTImageProcessorFast", AutoImageProcessor.from_pretrained(dino_model)) # type: ignore[reportUnknownMemberType] + self._encoder = cast( + "DINOv3ViTModel", + AutoModel.from_pretrained( # type: ignore[reportUnknownMemberType] + dino_model, + device_map="auto", + ), + ) + self.size = size + if cancel_affine: + norm = self.encoder.norm + norm.register_parameter("weight", None) + norm.register_parameter("bias", None) + norm.elementwise_affine = False + self.encoder.eval() + for param in self.encoder.parameters(): + param.requires_grad = False + + @property + def encoder(self) -> "DINOv3ViTModel": + """ + The encoder module that processes the input tensor. + Returns: + DINOv3ViTModel: The DINO V3 model. + """ + return self._encoder + + @property + def embedding_dim(self) -> int: + """ + The dimension of the encoded representation. + Returns: + int: The embedding dimension of the DINO V2 encoder. + """ + return self.encoder.config.hidden_size + + def preprocess(self, x: Float[Tensor, "batch_size channels height width"]) -> "BatchFeature": + """ + Preprocess the input tensor before encoding. + + Args: + x (Tensor): Input tensor to be preprocessed. + Assumed to be an image tensor with shape [N, C, H, W]. + + Returns: + BatchFeature: Preprocessed input tensor. + """ + # ensure float + x = x.float() + + # detect range once (cheaper than calling .min()/.max() repeatedly) + x_min = x.min().item() + x_max = x.max().item() + + # convert to [0, 255] + if x_min >= 0.0 and x_max <= 1.0: + x = x * 255.0 + elif x_min >= 0.0 and x_max <= 255.0 and x_max > 1.0: + pass + else: + raise ValueError("Input tensor range is not supported. Expected 0–255 or 0–1") + + x = x.clamp(0.0, 255.0) + + # convert to PIL Images + x_cpu = x.detach().to("cpu").round().byte() + imgs: list[Image.Image] = [] + for xi in x_cpu: + arr = cast(NDArray[np.uint8], xi.permute(1, 2, 0).contiguous().numpy()) # type: ignore[reportUnknownMemberType] + imgs.append(Image.fromarray(arr)) + + processed_imgs = self.processor(images=imgs, return_tensors="pt", size=self.size).to(self.encoder.device) # type: ignore[reportUnknownMemberType] + return processed_imgs + + @torch.inference_mode() + def forward(self, x: Float[Tensor, "batch_size channels height width"]) -> Float[Tensor, "batch_size seq_len dim"]: + """ + Forward pass of the encoder. + Args: + x (Tensor): Input tensor to be encoded. + Returns: + Tensor: Encoded representation of the input tensor. + """ + x_processed = self.preprocess(x) + with torch.no_grad(): + outputs: "BaseModelOutputWithPooling" = self.encoder(**x_processed) + last_hidden_states = cast(Tensor, outputs.last_hidden_state) + z = last_hidden_states[:, 1 + self.encoder.config.num_register_tokens :, :] + return z diff --git a/src/diffulab/networks/repa/perceiver_resampler.py b/src/diffulab/networks/repa/perceiver_resampler.py index bf4a8a0..67b69b8 100644 --- a/src/diffulab/networks/repa/perceiver_resampler.py +++ b/src/diffulab/networks/repa/perceiver_resampler.py @@ -8,10 +8,10 @@ from jaxtyping import Float from torch import Tensor, einsum, nn -from diffulab.networks.utils.nn import RotaryPositionalEmbedding +from diffulab.networks.utils.nn import RotaryPositionalEmbedding2D -class PerceiverRotaryPositionalEmbedding(RotaryPositionalEmbedding): +class PerceiverRotaryPositionalEmbedding(RotaryPositionalEmbedding2D): """Rotary positional embedding applied only to keys. This subclass restricts rotary application to the key tensor (``k``) while @@ -31,11 +31,8 @@ def forward( q: Float[Tensor, "batch_size seq_len n_heads head_dim"], k: Float[Tensor, "batch_size seq_len n_heads head_dim"], v: Float[Tensor, "batch_size seq_len n_heads head_dim"], - ) -> tuple[ - Float[Tensor, "batch_size seq_len n_heads head_dim"], - Float[Tensor, "batch_size seq_len n_heads head_dim"], - Float[Tensor, "batch_size seq_len n_heads head_dim"], - ]: + shape: tuple[int, int] | None = None, + ) -> tuple[Tensor, Tensor, Tensor]: """Apply rotary embedding to the key tensor only. Args: @@ -48,17 +45,21 @@ def forward( the first ``dim`` key channels rotated. """ seq_len = k.shape[1] - self._cache(seq_len) - cos = self.cos.to(device=k.device, dtype=k.dtype) - sin = self.sin.to(device=k.device, dtype=k.dtype) + if shape is None: + shape = (int(seq_len**0.5), int(seq_len**0.5)) + height, width = shape + assert height * width == seq_len, "Sequence length does not match provided shape." + self._cache(height, width) + + cos = self.cos.to(device=q.device, dtype=q.dtype) # [S, dim] + sin = self.sin.to(device=q.device, dtype=q.dtype) # [S, dim] # [batch_size, seq_length, num_heads, head_dim] -> [batch_size, num_heads, seq_length, head_dim] k = k.transpose(1, 2) # K rotation k_rope, k_pass = k[..., : self.dim], k[..., self.dim :] - k_neg_half = self._neg_half(k_rope) - k_rope = (k_rope * cos[:seq_len]) + (k_neg_half * sin[:seq_len]) + k_rope = self._apply_rotary(k_rope, cos, sin) k_rot = torch.cat((k_rope, k_pass), dim=-1) # [batch_size, num_heads, seq_length, head_dim] -> [batch_size, seq_length, num_heads, head_dim] diff --git a/src/diffulab/networks/rewards/__init__.py b/src/diffulab/networks/rewards/__init__.py new file mode 100644 index 0000000..9ee7904 --- /dev/null +++ b/src/diffulab/networks/rewards/__init__.py @@ -0,0 +1,4 @@ +from .common import RewardModel +from .grpo import PrefGRPORewardModel + +__all__ = ["RewardModel", "PrefGRPORewardModel"] diff --git a/src/diffulab/networks/rewards/common.py b/src/diffulab/networks/rewards/common.py new file mode 100644 index 0000000..0ac3627 --- /dev/null +++ b/src/diffulab/networks/rewards/common.py @@ -0,0 +1,40 @@ +from abc import ABC, abstractmethod +from typing import Any + +import torch +import torch.nn as nn +from jaxtyping import Float +from torch import Tensor + + +class RewardModel(nn.Module, ABC): + def __init__(self, n_image_per_prompt: int): + super().__init__() # type: ignore + self._n_image_per_prompt = n_image_per_prompt + + @abstractmethod + @torch.inference_mode() + def forward(self, images: Float[Tensor, "batch n_channels height width"], context: Any) -> Float[Tensor, "batch"]: + """ + Apply the model to an input batch. + + Args: + images (torch.Tensor): the input batch. + context (Any): additional context information. + Returns: + torch.Tensor: a tensor representing the reward for the given input batch. + """ + pass + + def set_n_image_per_prompt(self, n: int) -> None: + self._n_image_per_prompt = n + + @property + def n_image_per_prompt(self) -> int: + """ + Returns the number of images per prompt that the model has generated. + + Returns: + int or None: the number of images per prompt, or None if not set. + """ + return self._n_image_per_prompt diff --git a/src/diffulab/networks/rewards/grpo.py b/src/diffulab/networks/rewards/grpo.py new file mode 100644 index 0000000..9c901b2 --- /dev/null +++ b/src/diffulab/networks/rewards/grpo.py @@ -0,0 +1,529 @@ +import re +from typing import TYPE_CHECKING, Literal, TypedDict, Union, cast # added TypedDict related imports + +import numpy as np +import torch +from einops import rearrange +from jaxtyping import Float +from PIL import Image +from qwen_vl_utils import process_vision_info # type: ignore[reportUnknownVariableType] +from torch import Tensor +from transformers import ( # type: ignore[reportMissingTypeStubs] + AutoProcessor, + CLIPModel, + CLIPProcessor, + Qwen2_5_VLForConditionalGeneration, +) + +from diffulab.networks.rewards.common import RewardModel + +if TYPE_CHECKING: + from numpy.typing import NDArray + from transformers import BatchEncoding, CLIPOutput, Qwen2_5_VLProcessor # type: ignore + + +class ImageContent(TypedDict): + type: Literal["image"] + image: Image.Image + + +class TextContent(TypedDict): + type: Literal["text"] + text: str + + +ContentItem = Union[ImageContent, TextContent] + + +class ChatMessage(TypedDict): + role: Literal["user", "assistant", "system"] + content: list[ContentItem] + + +class PrefGRPORewardModel(RewardModel): + model_registry = { + "cot_7b": "CodeGoat24/UnifiedReward-Think-qwen-7b", + "3b": "CodeGoat24/UnifiedReward-2.0-qwen-3b", + "7b": "CodeGoat24/UnifiedReward-2.0-qwen-7b", + "32b": "CodeGoat24/UnifiedReward-2.0-qwen-32b", + "72b": "CodeGoat24/UnifiedReward-2.0-qwen-72b", + } + + def __init__( + self, + version: str = "7b", + n_image_per_prompt: int = 16, + advantage_clip_max: float = 5.0, + use_clip: bool = False, + lambda_base: float = 0.7, + lambda_clip: float = 1.4, + clip_model_id: str = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", + ): + super().__init__(n_image_per_prompt) + self.use_cot = version.startswith("cot") + self.version = version + assert self.version in self.model_registry, ( + f"Unsupported model version: {self.version}, available versions: {list(self.model_registry.keys())}" + ) + + self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # type: ignore[reportUnknownMemberType] + self.model_registry[self.version], dtype="auto", device_map="auto" + ) + self.processor: "Qwen2_5_VLProcessor" = AutoProcessor.from_pretrained(self.model_registry[self.version]) # type: ignore[reportUnknownMemberType] + for param in self.model.parameters(): # type: ignore + param.requires_grad = False + + self.clip_model: None | CLIPModel = None + self.clip_processor: None | CLIPProcessor = None + self.use_clip = use_clip + if self.use_clip: + self.clip_model = CLIPModel.from_pretrained(clip_model_id) # type: ignore + self.clip_processor = CLIPProcessor.from_pretrained(clip_model_id) # type: ignore + self.clip_model.eval() + for param in self.clip_model.parameters(): # type: ignore + param.requires_grad = False + + self.advantage_clip_max = advantage_clip_max + self.lambda_base = lambda_base + self.lambda_clip = lambda_clip + + @staticmethod + def _extract_cot_answer(text: str) -> str | None: + """Extract the content inside ... tags (case-insensitive).""" + match = re.search(r"(.*?)", text, re.DOTALL | re.IGNORECASE) + if match: + return match.group(1).strip() + + return None + + @staticmethod + def _parse_scores(raw: str) -> dict[str, dict[str, float]]: + """ + Parse blocks like: + Alignment Score: + Image 1: 0.45 + Image 2: 0.54 + Coherence Score: + ... + into a nested dict: {section: {image_label: float, ...}, ...} + + Args: + raw (str): The raw text to parse. + Returns: + dict[str, dict[str, float]]: A dictionary mapping section names to dictionaries of image scores. + + Example: + ```python + text = ''' + Alignment Score: + Image 1: 0.4537 + Image 2: 0.5463 + + Coherence Score: + Image 1: 0.6000 + Image 2: 0.4000 + ''' + scores = parse_scores(text) + # scores == { + # "Alignment Score": {"Image 1": 0.4537, "Image 2": 0.5463}, + # "Coherence Score": {"Image 1": 0.6000, "Image 2": 0.4000} + # } + ``` + """ + + def _unescape_newlines(s: str) -> str: + """ + Un-escape newline characters in a string, if any. + E.g. "Hello\\nWorld" -> "Hello + World" + """ + return bytes(s, "utf-8").decode("unicode_escape") if "\\n" in s else s + + text = _unescape_newlines(raw).strip() + sections: dict[str, dict[str, float]] = {} + current = None + + for line in text.splitlines(): + line = line.strip() + if not line: + continue + + m = re.match(r"^(.*?\S)\s*:\s*$", line) + if m and ("Score" in m.group(1)): + current = m.group(1) + sections[current] = {} + continue + + m = re.match(r"^Image\s+(\d+)\s*:\s*([+-]?\d+(?:\.\d+)?)$", line) + if m and current: + label = f"Image {m.group(1)}" + sections[current][label] = float(m.group(2)) + + return sections + + @staticmethod + def convert_to_image(image: Float[Tensor, "C H W"]) -> Image.Image: + """ + Convert a tensor image to a PIL Image. + Assume + + Args: + image (Float[Tensor, "C H W"]): The input tensor image. + Returns: + Image.Image: The converted PIL Image. + """ + image = ((image * 0.5 + 0.5).clamp(0, 1) * 255).byte() + np_image: "NDArray[np.uint8]" = rearrange(image.cpu(), "C H W -> H W C").numpy() # type: ignore[reportUnknownMemberType] + return Image.fromarray(np_image) + + def get_template(self, prompt: str) -> str: + if self.use_cot: + return f"""Given a caption and two images generated based on this caption, please analyze in detail the + two provided images. Evaluate them on various dimensions such as semantic consistency (how closely the image + content aligns with the caption), aesthetics (composition, color usage, artistic expression), authenticity + (realism and attention to detail), and any other factors you deem relevant. For each evaluation dimension, + provide a score between 1-10 for both images (e.g., Image 1: 8/10, Image 2: 6/10) and provide a concise + rationale for the score. Calculate the total score for each image by summing all dimension scores. Use a + chain-of-thought process to detail your reasoning steps, and enclose all your detailed reasoning within + and tags. Then, in the tag, output exactly one of the following strings: 'Image 1 + is better' or 'Image 2 is better' based on the total scores. No additional text is allowed in the + section.\n\nExample output format:\n\n1. Semantic consistency: Image 1 (9/10) - ...; Image 2 (7/10) + - ...\n2. Aesthetics: Image 2 (8/10) - ...; Image 1 (8/10) - ...\n3. Authenticity: Image 1 (8/10) - ...; + Image 2 (5/10) - ...\n[Additional dimensions if any]: Image 2 (8/10) - ...; Image 1 (6/10) - ...\nTotal + score:\nImage 1: 9+8+8+6=31\nImage 2: 7+8+5+8=28\n\nImage 1 is better\n**Note: + In the example above, scores and the final answer are placeholders meant only to demonstrate the format. + Your actual evaluation should be based on the quality of two given images.**\n\nYour task is provided as + follows:\nText Caption: [{prompt}]""" + + return f"""You are presented with two generated images (Image 1 and Image 2) along with a shared text caption. + Your task is to comparatively evaluate the two images across three specific dimensions:\n\n + - Alignment Score: How well each image matches the caption in terms of content.\n + - Coherence Score: How logically consistent and visually coherent each image is (absence of visual glitches, distorted objects, etc.).\n + - Style Score: How aesthetically appealing each image is, regardless of caption accuracy.\n\n + For each dimension, you must assign a relative score to Image 1 and Image 2, such that:\n + - Each score is a float between 0 and 1 (inclusive).\n + - The scores for Image 1 and Image 2 must sum to exactly 1.0 for each dimension.\n + - The higher the score, the better that image is in the corresponding dimension *relative to the other*.\n\n + This format emphasizes comparative quality rather than absolute evaluation.\n\n + Please provide your evaluation in the format below:\n\n + Alignment Score:\n + Image 1: X\n + Image 2: Y\n\n + Coherence Score:\n + Image 1: X\n + Image 2: Y\n\n + Style Score:\n + Image 1: X\n + Image 2: Y\n\n + Your task is provided as follows:\n + Text Caption: [{prompt}]""" + + def get_message(self, image_1: Image.Image, image_2: Image.Image, prompt: str) -> list[ChatMessage]: + template = self.get_template(prompt) + message: list[ChatMessage] = [ + { + "role": "user", + "content": [ + {"type": "image", "image": image_1}, + {"type": "image", "image": image_2}, + {"type": "text", "text": template}, + ], + } + ] + return message + + def _assess_winner(self, output: str) -> Literal[0, 1] | None: + if self.use_cot: + # Prefer explicit tag extraction; fall back to whole output if missing + answer = self._extract_cot_answer(output) or output + answer_lower = answer.lower() + if "image 1 is better" in answer_lower: + return 0 + if "image 2 is better" in answer_lower: + return 1 + else: + scores = self._parse_scores(output) + if not scores: + return + total_img1 = sum(section["Image 1"] for section in scores.values() if "Image 1" in section) + total_img2 = sum(section["Image 2"] for section in scores.values() if "Image 2" in section) + if total_img1 > total_img2: + return 0 + if total_img2 > total_img1: + return 1 + return None + + def parse_and_aggregate( + self, + outputs: list[str], + pairs: torch.Tensor, + P: int, + ) -> tuple[Float[Tensor, "P n_image_per_prompt"], Float[Tensor, "P n_image_per_prompt"]]: + """ + Aggregate pairwise preferences into per-image win and compare counts. + + Args: + outputs: Model text outputs, ordered by prompt first, then pair index. + pairs: Tensor of shape (n_pairs, 2) with image indices per prompt (0..n_images_per_prompt-1). + P: Number of prompts in the batch. + + Returns: + win_count: (P, n_image_per_prompt) float tensor where each entry is the accumulated wins + (ties contribute 0.5). + compare_count: (P, n_image_per_prompt) int tensor with number of comparisons per image. + """ + assert self.n_image_per_prompt is not None, ( + "n_image_per_prompt must be set before calling parse_and_aggregate()" + ) + assert pairs.ndim == 2 and pairs.shape[1] == 2, "pairs must have shape (n_pairs, 2)" + n_pairs = pairs.shape[0] + + # Initialize counts + win_count = torch.zeros(P, self.n_image_per_prompt, dtype=torch.float32) + compare_count = torch.zeros(P, self.n_image_per_prompt, dtype=torch.int32) + + # Ensure pairs on CPU for indexing ops; we only need indices here + pairs_cpu = pairs.to("cpu") + + for i, output in enumerate(outputs): + p = i // n_pairs # prompt index + j = i % n_pairs # pair index within the prompt + # Extract explicit Python ints for stable typing + idx1 = int(pairs_cpu[j, 0].item()) + idx2 = int(pairs_cpu[j, 1].item()) + + # Each image in the pair participates in one comparison + compare_count[p, idx1] += 1 + compare_count[p, idx2] += 1 + + winner = self._assess_winner(output) + if winner is None: + # tie: split the point + win_count[p, idx1] += 0.5 + win_count[p, idx2] += 0.5 + elif winner == 0: + win_count[p, idx1] += 1.0 + else: # winner == 1 + win_count[p, idx2] += 1.0 + + return win_count, compare_count + + def compute_advantages( + self, + advantages: Float[Tensor, "P n_image_per_prompt"], + advantage_per_prompt: bool = True, + ) -> Float[Tensor, "P n_image_per_prompt"]: + """ + Compute per-image standardized advantages from win and compare counts. + Args: + advantages (Float[Tensor, "P n_image_per_prompt"]): Float tensor with per-image advantage. + advantage_per_prompt (bool, optional): If True, compute a per-prompt z-score (mean/std computed + across the images within each prompt). If False, compute a global z-score across all images + in the batch. Defaults to True. + Returns: + Float[Tensor, "P n_image_per_prompt"]: Float tensor of shape (P, n_image_per_prompt,) with standardized advantages. + Raises: + ValueError: If shapes of win_count and compare_count do not match. + Notes: + - Standard deviation uses unbiased=False and is clamped with a minimum of 1e-6 to avoid + division by zero. + """ + if advantage_per_prompt: + mean = advantages.mean(dim=1, keepdim=True) + std = advantages.std(dim=1, keepdim=True).clamp_min(1e-6) + advantages = (advantages - mean) / std + else: + mean = advantages.mean() + std = advantages.std().clamp_min(1e-6) + advantages = (advantages - mean) / std + return advantages + + @torch.inference_mode() + def compute_pairwise_advantages( + self, + images: Float[Tensor, "n_group images_per_prompt n_channels height width"], + context: list[str], + advantage_per_prompt: bool = True, + ) -> Float[Tensor, "n_group images_per_prompt"]: + """ + Compute per-image standardized rewards from pairwise preference judgments within each prompt group. + This method groups the input batch into P prompts, each containing `self.n_image_per_prompt` images. + For every prompt, it forms all unordered image pairs, builds chat inputs with the corresponding + text context, queries the underlying vision-language model to obtain pairwise preferences, aggregates + the results into per-image win rates, and returns z-scored rewards either per prompt or globally. + Args: + images (torch.Tensor): Float tensor of shape (P, N, C, H, W) containing a batch of images. + context (list[str]): A list of length P*N with the textual context (e.g., prompt) for each + image. + advantage_per_prompt (bool, optional): If True, compute a per-prompt z-score (mean/std computed + across the images within each prompt). If False, compute a global z-score across all images + in the batch. Defaults to True. + Returns: + torch.Tensor: Float tensor of shape (P, N) on the same device as `images`, containing the + standardized reward for each image. + Raises: + AssertionError: If B % `self.n_image_per_prompt` != 0. + RuntimeError: Propagated from underlying model/processor during tokenization or generation. + ValueError: Propagated from parsing/aggregation if model outputs cannot be interpreted. + Notes: + - Pairs are formed as all unordered combinations of the `self.n_image_per_prompt` images + within each prompt (r=2). + - Win rates are computed as wins / comparisons per image; images with zero comparisons + receive a zero win rate before standardization. + - Standard deviation uses unbiased=False and is clamped with a minimum of 1e-6 to avoid + division by zero. + - Inference is batched with an upper bound of B pairwise conversations per generation call + and uses max_new_tokens=4096. + """ + P, n_images, C, H, W = images.shape + assert n_images == self.n_image_per_prompt, ( + f"Expected {self.n_image_per_prompt} images per prompt, but got {n_images}" + ) + assert len(context) == P * n_images, f"Expected {P * n_images} context entries, but got {len(context)}" + + pairs = torch.combinations(torch.arange(self.n_image_per_prompt, device=images.device), r=2) + trg_tensor = images[:, pairs] + trg_tensor = trg_tensor.reshape(-1, 2, C, H, W) # (P * n_pairs, 2, C, H, W) + + data_to_process: list[list[ChatMessage]] = [ + self.get_message(self.convert_to_image(pair[0]), self.convert_to_image(pair[1]), pair_context) + for pair, pair_context in zip(trg_tensor, context) + ] + + outputs: list[str] = [] + # Batch processing respecting the original batch size + for batch in range(0, len(data_to_process), P): + batch_data = data_to_process[batch : batch + P] + chat_input = self.processor.apply_chat_template( # type: ignore[reportArgumentType] + batch_data, # type: ignore[reportArgumentType] + tokenize=False, + add_generation_prompt=True, + ) + image_inputs, _ = process_vision_info(batch_data) # type: ignore[reportArgumentType] + + inputs = self.processor(text=chat_input, images=image_inputs, return_tensors="pt", padding=True).to( # type: ignore[reportUnknownMemberType] + self.model.device + ) + + generated_ids = self.model.generate(**inputs) # type: ignore[reportUnknownMemberType] + generated_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs["input_ids"], generated_ids)] + outputs.extend(cast(list[str], self.processor.batch_decode(generated_trimmed, skip_special_tokens=True))) # type: ignore[reportUnknownMemberType] + + # Aggregate pairwise preferences into per-image counts per prompt + win_count, compare_count = self.parse_and_aggregate(outputs, pairs, P=P) + compare_count = compare_count.to(torch.float32) + advantages = torch.where(compare_count > 0, win_count / compare_count, torch.zeros_like(win_count)) + advantages = self.compute_advantages(advantages, advantage_per_prompt) + return advantages.to(images.device) + + @torch.inference_mode() + def compute_clip_advantages( + self, + images: Float[Tensor, "n_group images_per_prompt n_channels height width"], + context: list[str], + advantage_per_prompt: bool = True, + ) -> Float[Tensor, "n_group images_per_prompt"]: + """ + Compute per-image standardized rewards using CLIP cosine similarity between images and text context. + Args: + images (torch.Tensor): Float tensor of shape (P, N, C, H, W) containing a batch of images. + context (list[str]): A list of length P*N with the textual context (e.g., prompt) for each + image. + advantage_per_prompt (bool, optional): If True, compute a per-prompt z-score (mean/std computed + across the images within each prompt). If False, compute a global z-score across all images + in the batch. Defaults to True. + Returns: + torch.Tensor: Float tensor of shape (P, N) on the same device as `images`, containing the + standardized reward for each image. + Raises: + AssertionError: If B % `self.n_image_per_prompt` != 0 or if CLIP model/processor are not initialized. + RuntimeError: Propagated from underlying model/processor during tokenization or forward pass. + Notes: + - Inference is batched with an upper bound of B images per forward call. + - Standard deviation uses unbiased=False and is clamped with a minimum of 1e-6 to avoid + division by zero. + """ + assert self.clip_model is not None, "Clip model is not initialized" + assert self.clip_processor is not None, "Clip processor is not initialized" + device = next(self.clip_model.parameters()).device + + P, n_images, C, H, W = images.shape + images = images.reshape(-1, C, H, W) + + advantages = torch.zeros((images.shape[0],), dtype=torch.float32, device=device) + for batch in range(0, len(images), P): + batch_images = [self.convert_to_image(img) for img in images[batch : batch + P]] + batch_context = context[batch : batch + P] + + inputs = cast( + "BatchEncoding", + self.clip_processor(text=batch_context, images=batch_images, return_tensors="pt", padding=True).to( # type: ignore + self.clip_model.device + ), + ) + outputs: "CLIPOutput" = self.clip_model(**inputs) + logits_per_image = cast(Tensor, outputs.logits_per_image) # type: ignore + cosine_sims = logits_per_image / self.clip_model.logit_scale.exp() + advantages[batch : batch + P] = cosine_sims + + advantages = advantages.reshape(P, n_images) + advantages = self.compute_advantages(advantages, advantage_per_prompt) + return advantages + + def forward( + self, + images: Float[Tensor, "batch n_channels height width"], + context: list[str], + advantage_per_prompt: bool = True, + ) -> Float[Tensor, "batch"]: + """ + Compute per-image standardized rewards from pairwise preference judgments within each prompt group. + Args: + images (torch.Tensor): Float tensor of shape (B, C, H, W) containing a batch of images. + context (list[str]): A list of length B with the textual context (e.g., prompt) for each + image. + advantage_per_prompt (bool, optional): If True, compute a per-prompt z-score (mean/std computed + across the images within each prompt). If False, compute a global z-score across all images + in the batch. Defaults to True. + Returns: + torch.Tensor: Float tensor of shape (B,) on the same device as `images`, containing the + standardized reward for each image. + Raises: + AssertionError: If B % `self.n_image_per_prompt` != 0. + RuntimeError: Propagated from underlying model/processor during tokenization or generation. + ValueError: Propagated from parsing/aggregation if model outputs cannot be interpreted. + Notes: + - This method reshapes the input batch into P prompts of N images each, where + N=`self.n_image_per_prompt` and P=B/N. + - Rewards are computed by combining pairwise preference judgments and optional CLIP + cosine similarities, followed by z-scoring either per prompt or globally. + """ + B, C, H, W = images.shape + assert self.n_image_per_prompt is not None, "n_image_per_prompt must be set before calling forward()" + assert B % self.n_image_per_prompt == 0, ( + f"Batch size {images.shape[0]} is not divisible by n_image_per_prompt {self.n_image_per_prompt}" + ) + assert len(context) == B // self.n_image_per_prompt, ( + f"Length of context {len(context)} does not match number of prompts {B // self.n_image_per_prompt}" + ) + + # P also corresponds to the original batch size + P = B // self.n_image_per_prompt + images = images.reshape(P, self.n_image_per_prompt, C, H, W) + + advantages = self.compute_pairwise_advantages( + images=images, + context=context, + advantage_per_prompt=advantage_per_prompt, + ) + + if self.use_clip: + clip_advantages = self.compute_clip_advantages( + images=images, + context=context, + advantage_per_prompt=advantage_per_prompt, + ) + advantages = self.lambda_base * advantages + self.lambda_clip * clip_advantages + + advantages = advantages.clamp(-self.advantage_clip_max, self.advantage_clip_max) + return advantages.reshape(B) diff --git a/src/diffulab/networks/utils/nn.py b/src/diffulab/networks/utils/nn.py index 02d1aa0..7bf4085 100644 --- a/src/diffulab/networks/utils/nn.py +++ b/src/diffulab/networks/utils/nn.py @@ -1,6 +1,5 @@ import math from dataclasses import dataclass -from typing import Tuple import torch import torch.nn as nn @@ -217,7 +216,7 @@ def forward( q: Float[Tensor, "batch_size seq_len n_heads head_dim"], k: Float[Tensor, "batch_size seq_len n_heads head_dim"], v: Float[Tensor, "batch_size seq_len n_heads head_dim"], - ) -> Tuple[ + ) -> tuple[ Float[Tensor, "batch_size seq_len n_heads head_dim"], Float[Tensor, "batch_size seq_len n_heads head_dim"], Float[Tensor, "batch_size seq_len n_heads head_dim"], @@ -230,7 +229,7 @@ def forward( k (Tensor): the key tensor of shape [batch_size, seq_len, n_heads, head_dim]. v (Tensor): the value tensor of shape [batch_size, seq_len, n_heads, head_dim]. Returns: - Tuple[Tensor, Tensor, Tensor]: the rotated query and key tensors, and the unchanged value tensor. + tuple[Tensor, Tensor, Tensor]: the rotated query and key tensors, and the unchanged value tensor. """ seq_len = q.shape[1] self._cache(seq_len) @@ -260,6 +259,145 @@ def forward( return q_rot, k_rot, v +class RotaryPositionalEmbedding2D(nn.Module): + theta: Tensor + h: int + w: int + cos: Tensor + sin: Tensor + """ + 2D Rotary Positional Embedding (RoPE) module. + This module applies 2D rotary positional encoding to the query and key tensors in a multi-head attention mechanism. + """ + + def __init__(self, dim: int = 32, base: int = 100) -> None: + super().__init__() # type: ignore + assert dim % 4 == 0, "Dimension must be divisible by 4 for 2D rotary embeddings." + self.dim = dim + self.base = base + self.register_buffer( + name="theta", + tensor=torch.pow(base, torch.arange(0, self.dim, 4, dtype=torch.float32) / self.dim).reciprocal(), + ) + self.h = 0 + self.w = 0 + self.cos = torch.empty(0, requires_grad=False) + self.sin = torch.empty(0, requires_grad=False) + + def get_2d_grid(self, height: int, width: int) -> tuple[Tensor, Tensor]: + """ + Get a 2D grid of coordinates. + Args: + height (int): the height of the grid. + width (int): the width of the grid. + Returns: + tuple[Tensor, Tensor]: the y and x coordinates of the grid. + """ + y, x = torch.meshgrid( + torch.arange(height, device=self.theta.device), torch.arange(width, device=self.theta.device), indexing="ij" + ) + return y.flatten().float(), x.flatten().float() + + def _cache(self, height: int, width: int) -> None: + """ + Cache the cosine and sine values for the given height and width. + Args: + height (int): the height of the sequence for which to cache the values. + width (int): the width of the sequence for which to cache the values. + """ + if self.h == height and self.w == width: + return + self.h = height + self.w = width + y, x = self.get_2d_grid(height, width) + + angles_x = torch.outer(x, self.theta) + angles_y = torch.outer(y, self.theta) + + freqs_pairs = torch.cat([angles_x, angles_y], dim=-1) # [S, dim/2] + + with torch.no_grad(): + self.cos = freqs_pairs.cos().float() # [N, dim] + self.sin = freqs_pairs.sin().float() # [N, dim] + + def _apply_rotary( + self, + x: Float[Tensor, "batch_size num_head seq_len dim_rot"], # [B, H, S, D_rot] + cos: Float[Tensor, "seq_len dim_rot/2"], # [S, D_rot/2] + sin: Float[Tensor, "seq_len dim_rot/2"], # [S, D_rot/2] + ) -> Float[Tensor, "batch_size num_head seq_len dim_rot"]: + """ + Apply rotary positional embedding to the input tensor. + Args: + x (Tensor): the input tensor + cos (Tensor): the cached cosine values + sin (Tensor): the cached sine values + Returns: + Tensor: the rotated tensor + """ + # broadcast: [1,1,S,D/2] + cos = cos[None, None, :, :] + sin = sin[None, None, :, :] + + # contiguous RoPE: pair (0,1), (2,3), ... + x_even = x[..., 0::2] # [B, H, S, D/2] + x_odd = x[..., 1::2] # [B, H, S, D/2] + + x_rot_even = x_even * cos - x_odd * sin + x_rot_odd = x_even * sin + x_odd * cos + + # interleave back to [B,H,S,D] + x_rot = torch.stack([x_rot_even, x_rot_odd], dim=-1) # [B,H,S,D/2,2] + x_rot = x_rot.flatten(-2) # [B,H,S,D] + return x_rot + + def forward( + self, + q: Float[Tensor, "batch_size seq_len n_heads head_dim"], + k: Float[Tensor, "batch_size seq_len n_heads head_dim"], + v: Float[Tensor, "batch_size seq_len n_heads head_dim"], + shape: tuple[int, int] | None = None, + ) -> tuple[Tensor, Tensor, Tensor]: + """ + Apply 2D Rotary Positional Encoding to the query and key tensors. + Args: + q (Tensor): the query tensor of shape [batch_size, seq_len, n_heads, head_dim]. + k (Tensor): the key tensor of shape [batch_size, seq_len, n_heads, head_dim]. + v (Tensor): the value tensor of shape [batch_size, seq_len, n_heads, head_dim]. + shape (tuple[int, int] | None): the (height, width) shape of the sequence. If None, assumes square shape. + Returns: + tuple[Tensor, Tensor, Tensor]: the rotated query and key tensors, and the unchanged value tensor. + """ + seq_len = q.shape[1] + if shape is None: + shape = (int(seq_len**0.5), int(seq_len**0.5)) + height, width = shape + assert height * width == seq_len, "Sequence length does not match provided shape." + self._cache(height, width) + + cos = self.cos.to(device=q.device, dtype=q.dtype) # [S, dim] + sin = self.sin.to(device=q.device, dtype=q.dtype) # [S, dim] + + # [batch_size, seq_length, num_heads, head_dim] -> [batch_size, num_heads, seq_length, head_dim] + q = q.transpose(1, 2) + k = k.transpose(1, 2) + + # Q rotation + q_rope, q_pass = q[..., : self.dim], q[..., self.dim :] + q_rope = self._apply_rotary(q_rope, cos, sin) + q_rot = torch.cat((q_rope, q_pass), dim=-1) + + # K rotation + k_rope, k_pass = k[..., : self.dim], k[..., self.dim :] + k_rope = self._apply_rotary(k_rope, cos, sin) + k_rot = torch.cat((k_rope, k_pass), dim=-1) + + q_rot = q_rot.transpose(1, 2) + k_rot = k_rot.transpose(1, 2) + + return q_rot, k_rot, v + + class RMSNorm(torch.nn.Module): """ Root Mean Square Layer Normalization (RMSNorm) module. @@ -376,8 +514,11 @@ def __init__(self, embedding_dim: int, input_dim: int): super().__init__() # type: ignore self.lin = nn.Linear(embedding_dim, 6 * input_dim, bias=True) - def forward(self, vec: Float[Tensor, "... dim"]) -> ModulationOut: - out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(6, dim=-1) + def forward(self, vec: Float[Tensor, "B dim"] | Float[Tensor, "B seq_len dim"]) -> ModulationOut: + out = self.lin(nn.functional.silu(vec)) + if out.dim() == 2: + out = out[:, None, :] + out = out.chunk(6, dim=-1) return ModulationOut(*out) diff --git a/src/diffulab/networks/vision_towers/__init__.py b/src/diffulab/networks/vision_towers/__init__.py index a490be6..16bf121 100644 --- a/src/diffulab/networks/vision_towers/__init__.py +++ b/src/diffulab/networks/vision_towers/__init__.py @@ -1,7 +1,10 @@ from .common import VisionTower from .dc_ae import DCAE +from .rae import RAE, RAEDecoder __all__ = [ "DCAE", "VisionTower", + "RAE", + "RAEDecoder", ] diff --git a/src/diffulab/networks/vision_towers/common.py b/src/diffulab/networks/vision_towers/common.py index 03e3b6c..7c0ea7b 100644 --- a/src/diffulab/networks/vision_towers/common.py +++ b/src/diffulab/networks/vision_towers/common.py @@ -1,7 +1,17 @@ from abc import ABC, abstractmethod +from typing import Any, cast +import numpy as np +import torch import torch.nn as nn +from loguru import logger +from PIL import Image +from streaming import StreamingDataset +from streaming.base import MDSWriter from torch import Tensor +from torch.utils.data import DataLoader +from torchvision import transforms # type: ignore[reportMissingTypeStub] +from tqdm import tqdm class VisionTower(nn.Module, ABC): @@ -17,21 +27,29 @@ def __init__(self, latent_scale: float = 1.0) -> None: @property @abstractmethod - def compression_factor(self) -> int: + def latent_channels(self) -> int: """ - Compression factor of the AE. - This should be implemented in subclasses to return the specific compression factor. + Number of channels in the latent space. + This should be implemented in subclasses to return the specific number of latent channels. """ ... @property - @abstractmethod - def latent_channels(self) -> int: + def compression_factor(self) -> int | None: """ - Number of channels in the latent space. - This should be implemented in subclasses to return the specific number of latent channels. + Compression factor of the AE. + This should be implemented in relevant subclasses to return the specific compression factor. """ - ... + return None + + @property + def patch_size(self) -> int | None: + """ + Patch size of the encoder. + This should be implemented in relevant subclasses to return the specific patch size. + Valid for transformer-based encoders like DinoV3. + """ + return None @abstractmethod def encode(self, x: Tensor) -> Tensor: @@ -70,3 +88,97 @@ def forward(self, x: Tensor, *args, **kwargs) -> Tensor: # type: ignore Returns: forward (Tensor): Output tensor after encoding and decoding. """ + + @torch.inference_mode() + def compute_on_dataset( + self, + dataset_path: str, + dst_path: str | tuple[str, str], + local: bool = True, + batch_size: int = 64, + split: str | None = None, + to_process_data_key: str | None = None, + target_type: str = "float32", # "float32" | "float16" + column_target: str = "vision_latents", + ) -> None: + """ + Run the embedder over a MosaicML StreamingDataset and write an MDS with + the original columns + one ndarray column per output of the embedder. + + Each output i is written to a column named f"{column_prefix}_{i}" with + encoding "ndarray:". + + Args: + dataset_path: Path to existing StreamingDataset (local dir or remote URI). + dst_path: Destination for MDSWriter (dir or (remote, local) tuple). + local: If True, treat dataset_path as local; otherwise as remote. + batch_size: Loader batch size (controls tokenization/forward work size). + split: Optional split name. + to_process_data_key: Explicit column name containing data to process. If None, + target_type: "float32" or "float16" for output arrays. + column_prefix: Prefix for destination embedding columns. + progress_desc: TQDM description. + """ + dataset = StreamingDataset( + remote=dataset_path if not local else None, + local=dataset_path if local else None, + batch_size=batch_size, + split=split, + ) + assert dataset.shards, "Dataset has no shards." # type: ignore + + def _collate_fn(batch: list[dict[str, Any]]) -> dict[str, Any]: + return {key: [item[key] for item in batch] for key in batch[0].keys()} + + dataloader = cast( + DataLoader[dict[str, Any]], + DataLoader(dataset, batch_size=batch_size, collate_fn=_collate_fn), + ) + + source_columns: dict[str, str] = { + name: encoding + for name, encoding in zip(dataset.shards[0].column_names, dataset.shards[0].column_encodings) # type: ignore + } + + dst_columns = dict(source_columns) + dst_columns[column_target] = f"ndarray:{target_type}" + + # device / dtype (best effort; works even if submodules are off-device) + try: + param = next(self.parameters()) + device = param.device + dtype = param.dtype + except StopIteration: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32 + logger.warning(f"device and dtype not found | defaulting to {device} and {dtype}.") + + transform = transforms.ToTensor() + with MDSWriter(out=dst_path, columns=dst_columns, compression="zstd") as writer: + for batch in tqdm(dataloader, desc="computing embeddings", unit="batch"): + to_process_data = batch[to_process_data_key] + assert all(isinstance(img, Image.Image) for img in to_process_data), ( + "All images must be PIL Image objects" + ) + + # Convert PIL images to tensors + images = torch.stack([transform(img) for img in to_process_data]) + # Move to correct device and dtype + images = images.to(dtype=dtype, device=device) + + # Forward -> tuple of Tensors + output = self.encode(images) + np_output = output.detach().to("cpu").float().numpy() # type: ignore + if target_type == "float32": + np_output = np_output.astype(np.float32) + elif target_type == "float16": + np_output = np_output.astype(np.float16) + else: + raise ValueError("target_type must be 'float32' or 'float16'") + + B = len(to_process_data) + # Write per-sample rows, preserving original columns + for i in range(B): + sample = {k: v[i] for k, v in batch.items()} + sample[column_target] = np_output[i] + writer.write(sample) diff --git a/src/diffulab/networks/vision_towers/dc_ae.py b/src/diffulab/networks/vision_towers/dc_ae.py index 433a97b..9bd0b31 100644 --- a/src/diffulab/networks/vision_towers/dc_ae.py +++ b/src/diffulab/networks/vision_towers/dc_ae.py @@ -43,11 +43,18 @@ def encode(self, x: Float[Tensor, "batchsize 3 H W"]) -> Float[Tensor, "batchsiz Returns: Tensor: Encoded representation of the input tensor. """ - if x.min() >= 0 and x.max() <= 1: # Case: 0-1 - x = (x - 0.5) * 2.0 - elif x.min() >= 0 and x.max() <= 255: # Case: 0-255 - x = x.float() / 255.0 - x = (x - 0.5) * 2.0 + x = x.float() + x_min = x.min().item() + x_max = x.max().item() + + if x_min >= 0.0 and x_max <= 1.0: + pass + elif x_min >= 0.0 and x_max <= 255.0 and x_max > 1.0: + x = x / 255.0 + else: + raise ValueError("Input tensor range is not supported. Expected 0–255 or 0–1") + x = (x - 0.5) * 2.0 + return self.model.encode(x).latent # type: ignore def decode(self, z: Float[Tensor, "batchsize latent_channels H' W'"]) -> Float[Tensor, "batchsize 3 H W"]: diff --git a/src/diffulab/networks/vision_towers/rae.py b/src/diffulab/networks/vision_towers/rae.py new file mode 100644 index 0000000..abc5ade --- /dev/null +++ b/src/diffulab/networks/vision_towers/rae.py @@ -0,0 +1,505 @@ +from typing import TYPE_CHECKING, cast + +import numpy as np +import torch +import torch.nn as nn +from einops import rearrange +from jaxtyping import Float +from numpy.typing import NDArray +from PIL import Image +from torch import Tensor +from torch.utils.checkpoint import checkpoint # type: ignore +from transformers import AutoImageProcessor, AutoModel + +from diffulab.networks.utils.nn import QKNorm, RotaryPositionalEmbedding2D +from diffulab.networks.vision_towers.common import VisionTower + +if TYPE_CHECKING: + from transformers.feature_extraction_utils import BatchFeature + from transformers.modeling_outputs import BaseModelOutputWithPooling + from transformers.models.dinov3_vit import ( + DINOv3ViTImageProcessorFast, + DINOv3ViTModel, + ) + + +class Attention(nn.Module): + """ + Multi-head self-attention mechanism with rotary positional embeddings. + + Args: + input_dim (int): Dimension of the input. + dim (int): Inner Attention dimension. + num_heads (int): Number of attention heads. + partial_rotary_factor (float, optional): Factor for partial rotary embeddings. Default is 0.5. + base (int, optional): Base for rotary positional embeddings. Default is 10000. + + Attributes: + num_heads (int): Number of attention heads. + head_dim (int): Dimension of each attention head. + scale (float): Scaling factor for attention scores. + qkv (nn.Linear): Linear layer for query, key, and value projection of input. + qk_norm (QKNorm): Normalization layer for input query and key. + partial_rotary_factor (float): Factor for partial rotary embeddings. + rotary_dim (int): Dimension for rotary embeddings. + rope (RotaryPositionalEmbedding): Rotary positional embedding layer. + input_proj (nn.Linear): Linear layer for projecting the output of the input. + + Methods: + forward(input: Tensor, context: Tensor) -> tuple[Tensor, Tensor]: + Forward pass of the attention mechanism. + + Args: + input (Tensor): Input tensor of shape (batch_size, seq_len, input_dim). + + Returns: + Tensor: output tensor. + """ + + def __init__( + self, + input_dim: int, + dim: int, + num_heads: int, + partial_rotary_factor: float = 1, + base: int = 100, + dropout: float = 0.0, + ) -> None: + super().__init__() # type: ignore + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim**-0.5 + + self.qkv = nn.Linear(input_dim, 3 * dim) + self.qk_norm = QKNorm(dim) + + self.partial_rotary_factor = partial_rotary_factor + self.rotary_dim = int(self.head_dim * self.partial_rotary_factor) + self.rope = RotaryPositionalEmbedding2D(dim=self.rotary_dim, base=base) + self.dropout = dropout + + self.proj_out = nn.Linear(dim, input_dim) + + def forward( + self, input: Float[Tensor, "batch_size seq_len dim"], shape: tuple[int, int] | None = None + ) -> Float[Tensor, "batch_size seq_len dim"]: + input_q, input_k, input_v = self.qkv(input).chunk(3, dim=-1) + input_q, input_k = self.qk_norm(input_q, input_k, input_v) + + q, k, v = ( + rearrange(input_q, "b n (h d) -> b n h d", h=self.num_heads), + rearrange(input_k, "b n (h d) -> b n h d", h=self.num_heads), + rearrange(input_v, "b n (h d) -> b n h d", h=self.num_heads), + ) + # Remove CLS token for rotary embeddings + q[:, 1:], k[:, 1:], v = self.rope(q=q[:, 1:], k=k[:, 1:], v=v, shape=shape) + q, k, v = map(lambda x: rearrange(x, "b n h d -> b h n d"), [q, k, v]) + + attn_output = nn.functional.scaled_dot_product_attention( + query=q, + key=k, + value=v, + scale=self.scale, + dropout_p=self.dropout, + ) + + attn_output = rearrange(attn_output, "b h n d -> b n (h d)") + + output: Tensor = self.proj_out(attn_output) + + return output + + +class TransformerBlock(nn.Module): + """ + Transformer block with attention mechanisms. + + Args: + input_dim (int): Dimension of the input tensor. + hidden_dim (int): Dimension of the hidden layer in the attention mechanism. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio used to determine the size of the MLP layers. + + Methods: + forward(input: Tensor, y: Tensor, context: Tensor) -> Tuple[Tensor, Tensor]: + Performs the forward pass of the MMDiTBlock. + Args: + input (Tensor): The input tensor. + context (Tensor): The context tensor. + Returns: + Tuple[Tensor, Tensor]: The processed input tensor. + """ + + def __init__( + self, + input_dim: int, + hidden_dim: int, + num_heads: int, + mlp_ratio: int, + partial_rotary_factor: float = 1, + base: int = 100, + use_checkpoint: bool = False, + dropout_attn: float = 0, + dropout_mlp: float = 0, + ): + super().__init__() # type: ignore + self.norm_1 = nn.RMSNorm(input_dim) + self.attention = Attention( + input_dim, + hidden_dim, + num_heads, + partial_rotary_factor=partial_rotary_factor, + base=base, + dropout=dropout_attn, + ) + self.norm_2 = nn.RMSNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, mlp_ratio * input_dim), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_ratio * input_dim, input_dim), + ) + self.use_checkpoint = use_checkpoint + self.dropout = nn.Dropout(dropout_mlp) + + def forward( + self, + input: Float[Tensor, "batch_size seq_len embedding_dim"], + shape: tuple[int, int] | None = None, + ) -> Float[Tensor, "batch_size seq_len input_dim"]: + """ + Forward pass of the DiTBlock applying modulation and attention mechanisms. + Args: + - input (Tensor): The input tensor to be processed + - y (Tensor): The conditioning tensor used for modulation + Returns: + Tensor: The processed input tensor with residual connection + """ + return ( + checkpoint(self._forward, *(input, shape), use_reentrant=False) + if self.use_checkpoint + else self._forward(input, shape) + ) # type: ignore + + def _forward( + self, + input: Float[Tensor, "batch_size seq_len embedding_dim"], + shape: tuple[int, int] | None = None, + ) -> Float[Tensor, "batch_size seq_len embedding_dim"]: + input = input + self.attention(self.norm_1(input), shape=shape) + input = input + self.dropout(self.mlp(self.norm_2(input))) + return input + + +class RAEDecoder(nn.Module): + """ + Transformer-based image decoder for ViT-style self-supervised encoders. + + This module reconstructs an image from a sequence of patch tokens produced by a + frozen vision encoder (e.g., DINOv3 ViT). It first linearly projects encoder + embeddings to the decoder width, prepends a learned [CLS] token, applies a stack + of Transformer blocks with rotary positional embeddings, and finally maps each + token to a patch vector that is unpatchified into the output image. + + Notes: + The spatial size of the reconstructed image must be divisible by `patch_size`. + + Args: + out_size (tuple[int, int]): Spatial size (H, W) of the reconstructed image. + out_channels (int): Number of output channels (e.g., 3 for RGB). + encoder_dim (int): Channel dimension of encoder tokens (input to the decoder). + input_dim (int): Internal decoder width (embedding size after projection). + hidden_dim (int): Attention inner dimension for multi-head attention. + num_heads (int): Number of attention heads. + mlp_ratio (int): Expansion ratio for the MLP inside each Transformer block. + patch_size (int): Patch size used by the encoder; also drives unpatchify. + depth (int): Number of Transformer blocks in the decoder. + partial_rotary_factor (float): Fraction of head dimension using rotary embeddings. + use_checkpoint (bool): Enable gradient checkpointing inside blocks. + dropout_attn (float): Dropout applied in attention. + dropout_mlp (float): Dropout applied in MLP. + + Attributes: + input_proj (nn.Linear): Projects encoder_dim -> input_dim. + layers (nn.ModuleList[TransformerBlock]): Transformer stack. + last_layer (nn.Sequential): LayerNorm + Linear to patch vectors (p*p*C). + cls_token (nn.Parameter): Learned [CLS] token prepended to the sequence. + patch_size (int): Patch size used for unpatchify. + out_size (tuple[int, int]): Target reconstruction size (H, W). + out_channels (int): Number of output channels. + + Typical usage: + z = encoder_tokens # (B, N, encoder_dim) + x_recon = decoder(z) # (B, C, H, W) + """ + + def __init__( + self, + out_size: tuple[int, int] = (256, 256), + out_channels: int = 3, + encoder_dim: int = 768, + input_dim: int = 1152, + hidden_dim: int = 1152, + num_heads: int = 16, + mlp_ratio: int = 4, + patch_size: int = 16, + depth: int = 28, + partial_rotary_factor: float = 1, + base: int = 100, + use_checkpoint: bool = False, + dropout_attn: float = 0, + dropout_mlp: float = 0, + ): + super().__init__() # type: ignore + self.input_proj = nn.Linear(encoder_dim, input_dim) + self.layers = nn.ModuleList( + [ + TransformerBlock( + input_dim=input_dim, + hidden_dim=hidden_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + partial_rotary_factor=partial_rotary_factor, + base=base, + use_checkpoint=use_checkpoint, + dropout_attn=dropout_attn, + dropout_mlp=dropout_mlp, + ) + for _ in range(depth) + ] + ) + + self.last_layer = nn.Sequential( + nn.LayerNorm(input_dim), + nn.Linear(input_dim, patch_size * patch_size * out_channels), + ) + self.apply(self._init_weights) + self.cls_token = nn.Parameter(torch.zeros(1, 1, input_dim)) + self.patch_size = patch_size + self.out_size = out_size + self.out_channels = out_channels + self.encoder_dim = encoder_dim + self.grid_size = (out_size[0] // patch_size, out_size[1] // patch_size) + + def _init_weights(self, module: nn.Module) -> None: + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + nn.init.xavier_uniform_(module.weight) + if module.bias is not None: # type: ignore + nn.init.constant_(module.bias, 0) + + def unpatchify( + self, x: Float[Tensor, "batch_size seq_len patch_dim"] + ) -> Float[Tensor, "batch_size channels height width"]: + """ + Convert patches back into the original image tensor. + Args: + x (Tensor): Patchified tensor of shape (B, num_patches, patch_dim) + Returns: + Tensor: Reconstructed image tensor of shape (B, C, H, W) + """ + # Reshape the tensor to the original image dimensions + x = rearrange( + x, + "b (h w) (p1 p2 c) -> b c (h p1) (w p2)", + h=self.grid_size[0], + w=self.grid_size[1], + p1=self.patch_size, + p2=self.patch_size, + c=self.out_channels, + ) + return x + + def forward( + self, + x: Float[Tensor, "batch_size seq_len encoder_dim"], + ) -> Float[Tensor, "batch_size channels height width"]: + x = self.input_proj(x) + x = torch.cat([self.cls_token.repeat(x.size(0), 1, 1), x], dim=1) + + for layer in self.layers: + x = layer(x, shape=self.grid_size) + + x = self.last_layer(x) + x = self.unpatchify(x[:, 1:]) # remove cls token + return x + + +class RAE(VisionTower): + """ + Reconstruction Auto-Encoder built from a frozen DINOv3 ViT encoder and a + Transformer decoder. + + The encoder is loaded from Hugging Face Transformers and kept frozen in eval + mode. Inputs are preprocessed with the corresponding `AutoImageProcessor` and + encoded into a sequence of patch embeddings. The decoder then reconstructs the + image from those tokens. + + Pipeline: + 1) preprocess(x): Accepts a tensor in [0,1] or [0,255], converts to PIL, + runs the DINOv3 image processor (no resize), and moves data to the + encoder device. + 2) encode(x): Runs the ViT encoder and returns patch tokens, excluding the + CLS token and any register tokens. + 3) decode(z): Uses `RAEDecoder` to reconstruct the image from tokens. + 4) forward(x): Convenience wrapper calling encode -> decode. + + Args: + decoder (RAEDecoder | None): Optional decoder instance. If None, + `decoder_config` must be provided to construct one. + decoder_config (dict[str, Any] | None): Keyword args for `RAEDecoder` + when `decoder` is not provided. + dinov3_id (str): Model identifier for the DINOv3 ViT on Hugging Face + (e.g., "facebook/dinov3-vitb16-pretrain-lvd1689m"). + + Properties: + latent_channels (int): Encoder hidden size (channel dimension of tokens). + patch_size (int): Patch size used by the encoder. + + Methods: + preprocess(x): Prepares inputs for the encoder. Raises ValueError if the + value range is not [0,1] or [0,255]. + encode(x): Returns token embeddings with CLS and register tokens removed. + decode(z): Reconstructs an image from token embeddings. + forward(x): End-to-end reconstruction. + + Notes: + - The encoder parameters are frozen and set to eval mode. + - Device placement uses `device_map="auto"` when loading the encoder. + - The decoder output spatial size is controlled by `decoder.out_size`. + """ + + def __init__( + self, + decoder: RAEDecoder, + dinov3_id: str = "facebook/dinov3-vitb16-pretrain-lvd1689m", + load_encoder: bool = True, + encoder_patch_size: int | None = None, + ) -> None: + super().__init__() # type: ignore + assert load_encoder or encoder_patch_size, "If not loading the encoder, must provide encoder_patch_size" + self.load_encoder = load_encoder + self._patch_size = encoder_patch_size + self._latent_channels = decoder.encoder_dim + if load_encoder: + self.processor = cast("DINOv3ViTImageProcessorFast", AutoImageProcessor.from_pretrained(dinov3_id)) # type: ignore[reportUnknownMemberType] + self.encoder = cast( + "DINOv3ViTModel", + AutoModel.from_pretrained( # type: ignore[reportUnknownMemberType] + dinov3_id, + device_map="auto", + ), + ) + norm = self.encoder.norm + norm.register_parameter("weight", None) + norm.register_parameter("bias", None) + norm.elementwise_affine = False + self.encoder.eval() + for param in self.encoder.parameters(): + param.requires_grad = False + + assert self._latent_channels == self.encoder.config.hidden_size + self._patch_size = self.encoder.config.patch_size + + self.decoder = decoder + + @property + def latent_channels(self) -> int: + """ + Number of channels in the latent space. + This should be implemented in subclasses to return the specific number of latent channels. + """ + assert self._latent_channels is not None, "latent_channels is not set" + return self._latent_channels + + @property + def patch_size(self) -> int: + """ + Patch size of the encoder. + """ + assert self._patch_size is not None, "patch_size is not set" + return self._patch_size + + def preprocess(self, x: Float[Tensor, "batch_size channels height width"]) -> "BatchFeature": + """ + Preprocess the input tensor before encoding. + + Args: + x (Tensor): Input tensor to be preprocessed. + Assumed to be an image tensor with shape [N, C, H, W]. + + Returns: + BatchFeature: Preprocessed input tensor. + """ + # ensure float + x = x.float() + + # detect range once (cheaper than calling .min()/.max() repeatedly) + x_min = x.min().item() + x_max = x.max().item() + + # convert to [0, 255] + if x_min >= 0.0 and x_max <= 1.0: + x = x * 255.0 + elif x_min >= 0.0 and x_max <= 255.0 and x_max > 1.0: + pass + else: + raise ValueError("Input tensor range is not supported. Expected 0–255 or 0–1") + + x = x.clamp(0.0, 255.0) + + # convert to PIL Images + x_cpu = x.detach().to("cpu").round().byte() + imgs: list[Image.Image] = [] + for xi in x_cpu: + arr = cast(NDArray[np.uint8], xi.permute(1, 2, 0).contiguous().numpy()) # type: ignore[reportUnknownMemberType] + imgs.append(Image.fromarray(arr)) + + processed_imgs = self.processor(images=imgs, return_tensors="pt", do_resize=False).to(self.encoder.device) # type: ignore[reportUnknownMemberType] + return processed_imgs + + @torch.inference_mode() + def encode(self, x: Float[Tensor, "batch_size channels height width"]) -> Float[Tensor, "batch_size seq_len dim"]: + """ + Forward pass of the encoder. + Args: + x (Tensor): Input tensor to be encoded. + Returns: + Tensor: Encoded representation of the input tensor. + """ + assert self.load_encoder, "Encoder must be loaded to use encode()" + x_processed = self.preprocess(x) + with torch.no_grad(): + outputs: "BaseModelOutputWithPooling" = self.encoder(**x_processed) + last_hidden_states = cast(Tensor, outputs.last_hidden_state) + z = last_hidden_states[:, 1 + self.encoder.config.num_register_tokens :, :] + return z + + def decode( + self, z: Float[Tensor, "batch_size seq_len dim"] + ) -> Float[Tensor, "batch_size channels height_d width_d"]: + """ + Forward pass of the decoder. + Args: + z (Tensor): Latent tensor to be decoded. + Returns: + Tensor: Decoded representation of the latent tensor. + """ + x_recon = self.decoder(z) + return x_recon + + def forward( + self, + x: Float[Tensor, "batch_size channels height width"], + ) -> Float[Tensor, "batch_size channels height_d width_d"]: + """ + Forward pass of the RAE. + + Args: + x (Tensor): Input tensor to be encoded and decoded. + + Returns: + Tensor: Output tensor after encoding and decoding. + """ + assert self.load_encoder, "Encoder must be loaded to use forward(), use decode() directly instead." + z = self.encode(x) + x_recon = self.decode(z) + return x_recon diff --git a/src/diffulab/training/__init__.py b/src/diffulab/training/__init__.py index 4db4c5f..1f06cff 100644 --- a/src/diffulab/training/__init__.py +++ b/src/diffulab/training/__init__.py @@ -1,4 +1,4 @@ from .losses import LossFunction, RepaLoss -from .trainer import Trainer +from .trainers import BaseTrainer, GRPOTrainer, Trainer -__all__ = ["Trainer", "LossFunction", "RepaLoss"] +__all__ = ["Trainer", "LossFunction", "RepaLoss", "BaseTrainer", "GRPOTrainer"] diff --git a/src/diffulab/training/losses/rae.py b/src/diffulab/training/losses/rae.py new file mode 100644 index 0000000..fdc1974 --- /dev/null +++ b/src/diffulab/training/losses/rae.py @@ -0,0 +1,198 @@ +# loss borrowed directly from the official RAE implementation: +# https://github.com/bytetriper/RAE/blob/main/src/disc/lpips.py +# MiT license as of the 2 of November 2025 + +from __future__ import annotations + +import hashlib +from pathlib import Path +from typing import Literal, NamedTuple + +import requests +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import models # type: ignore +from tqdm import tqdm + +from diffulab.training.losses.common import LossFunction + +# ------------------------------------------------- +# LPIPS loss +# ------------------------------------------------- + + +class ScalingLayer(nn.Module): + def __init__(self) -> None: + super().__init__() # type: ignore + self.register_buffer(name="shift", tensor=torch.tensor([-0.030, -0.088, -0.188])[None, :, None, None]) + self.register_buffer(name="scale", tensor=torch.tensor([0.458, 0.448, 0.450])[None, :, None, None]) + + def forward(self, tensor: torch.Tensor) -> torch.Tensor: + return (tensor - self.shift) / self.scale # type: ignore + + +class NetLinLayer(nn.Module): + def __init__(self, chn_in: int, chn_out: int = 1, use_dropout: bool = False) -> None: + super().__init__() # type: ignore + layers: list[nn.Module] = [nn.Dropout()] if use_dropout else [] + layers.append(nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False)) + self.model = nn.Sequential(*layers) + + +class VGG16FeatureExtractor(nn.Module): + def __init__(self, requires_grad: bool = False, pretrained: bool = True) -> None: + super().__init__() # type: ignore + features = models.vgg16(pretrained=pretrained).features + self.slice1 = nn.Sequential(*[features[x] for x in range(4)]) # type: ignore + self.slice2 = nn.Sequential(*[features[x] for x in range(4, 9)]) # type: ignore + self.slice3 = nn.Sequential(*[features[x] for x in range(9, 16)]) # type: ignore + self.slice4 = nn.Sequential(*[features[x] for x in range(16, 23)]) # type: ignore + self.slice5 = nn.Sequential(*[features[x] for x in range(23, 30)]) # type: ignore + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, tensor: torch.Tensor): + h = self.slice1(tensor) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + VggOutputs = NamedTuple( + "VggOutputs", + [ + ("relu1_2", torch.Tensor), + ("relu2_2", torch.Tensor), + ("relu3_3", torch.Tensor), + ("relu4_3", torch.Tensor), + ("relu5_3", torch.Tensor), + ], + ) + return VggOutputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + + +def _normalize(tensor: torch.Tensor, eps: float = 1e-10) -> torch.Tensor: + norm_factor = torch.sqrt(torch.sum(tensor**2, dim=1, keepdim=True)) + return tensor / (norm_factor + eps) + + +def _spatial_average(tensor: torch.Tensor, keepdim: bool = True) -> torch.Tensor: + return tensor.mean([2, 3], keepdim=keepdim) + + +class LPIPS(LossFunction): + """Learned perceptual metric used by VQGAN.""" + + URL_MAP: dict[str, str] = {"vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"} + + CKPT_MAP: dict[str, str] = {"vgg_lpips": "vgg.pth"} + + def __init__(self, use_dropout: bool = True, root: str = Path.home().as_posix()) -> None: + super().__init__() + self.scaling_layer = ScalingLayer() + self.chns = [64, 128, 256, 512, 512] + self.net = VGG16FeatureExtractor(pretrained=True, requires_grad=False) + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self._load_pretrained_weights(root=root) + for param in self.parameters(): + param.requires_grad = False + + def _download(self, url: str, local_path: str | Path, chunk_size: int = 1024): + Path(local_path).parent.mkdir(parents=True, exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + def _get_ckpt_path(self, root: str, name: str, check: bool = False): + assert name in self.URL_MAP + path = Path(root) / ".cache/diffulab" / self.CKPT_MAP[name] + if not path.exists(): + print("Downloading {} model from {} to {}".format(name, self.URL_MAP[name], path)) + self._download(self.URL_MAP[name], path) + with open(path, "rb") as f: + content = f.read() + assert hashlib.md5(content).hexdigest() == "d507d7349b931f0638a25a48a722f98a" + return path + + def _load_pretrained_weights(self, root: str, name: str = "vgg_lpips") -> None: + ckpt = self._get_ckpt_path(root, name) + state = torch.load(ckpt, map_location=torch.device("cpu")) + self.load_state_dict(state, strict=False) + print(f"[LPIPS] Loaded pretrained weights from {ckpt}") + + def forward(self, input: torch.Tensor, target: torch.Tensor, reduction: str = "mean") -> torch.Tensor: + input_scaled, target_scaled = self.scaling_layer(input), self.scaling_layer(target) + feats_input = self.net(input_scaled) + feats_target = self.net(target_scaled) + diffs: list[torch.Tensor] = [] + lin_layers = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + for idx, (feat_in, feat_tgt) in enumerate(zip(feats_input, feats_target)): + feat_in = _normalize(feat_in) + feat_tgt = _normalize(feat_tgt) + diff = (feat_in - feat_tgt) ** 2 + diffs.append(_spatial_average(lin_layers[idx].model(diff), keepdim=True)) + + value: torch.Tensor = sum(diffs) # type: ignore + + if reduction == "none": + return value + if reduction == "sum": + return value.sum() + if reduction == "mean": + return value.mean() + raise ValueError(f"Unsupported reduction '{reduction}'") + + +# ------------------------------------------------- +# GAN loss +# ------------------------------------------------- + + +class GANLoss(LossFunction): + def __init__(self, gan_type: Literal["vanilla", "hinge"] = "vanilla") -> None: + super().__init__() + assert gan_type in ["vanilla", "hinge"], f"Unsupported GAN loss type '{gan_type}'" + self.gan_type = gan_type + + def compute_d_loss( + self, logits_real: torch.Tensor, logits_fake: torch.Tensor, reduction: str = "mean" + ) -> torch.Tensor: + reduce = torch.mean if reduction == "mean" else torch.sum + if self.gan_type == "hinge": + loss_real = reduce(F.relu(torch.ones_like(logits_real) - logits_real)) + loss_fake = reduce(F.relu(torch.ones_like(logits_fake) + logits_fake)) + return 0.5 * (loss_real + loss_fake) + return 0.5 * (reduce(F.softplus(-logits_real)) + reduce(F.softplus(logits_fake))) + + def compute_g_loss(self, logits_fake: torch.Tensor, reduction: str = "mean") -> torch.Tensor: + reduce = torch.mean if reduction == "mean" else torch.sum + if self.gan_type == "hinge": + return -reduce(logits_fake) + return reduce(F.softplus(-logits_fake)) + + def forward( + self, + logits_fake: torch.Tensor, + logits_real: torch.Tensor | None = None, + is_disc: bool = False, + reduction: str = "mean", + ) -> torch.Tensor: + if is_disc: + assert logits_real is not None, "logits_real must be provided when computing discriminator loss" + return self.compute_d_loss(logits_real, logits_fake, reduction=reduction) + return self.compute_g_loss(logits_fake, reduction=reduction) diff --git a/src/diffulab/training/losses/repa.py b/src/diffulab/training/losses/repa.py index 5e7fcae..c8e9d88 100644 --- a/src/diffulab/training/losses/repa.py +++ b/src/diffulab/training/losses/repa.py @@ -121,7 +121,7 @@ def __init__( self._handles: "WeakKeyDictionary[nn.Module, RemovableHandle]" = WeakKeyDictionary() self._features: "WeakKeyDictionary[nn.Module, torch.Tensor]" = WeakKeyDictionary() self._active_model: nn.Module | None = None - self._hook_layer_idx = self.alignment_layer - 1 # as before + self._hook_layer_idx = self.alignment_layer - 1 self.coeff = coeff @_dynamo_disable @@ -205,5 +205,5 @@ def forward( projected_src_features = self.resampler(projected_src_features) cos_sim = torch.nn.functional.cosine_similarity(projected_src_features, dst_features, dim=-1) # type: ignore - loss = 1 - cos_sim.mean() - return self.coeff * loss + loss = 1 - cos_sim.mean() # type: ignore + return self.coeff * loss # type: ignore diff --git a/src/diffulab/training/trainers/__init__.py b/src/diffulab/training/trainers/__init__.py new file mode 100644 index 0000000..a4f521a --- /dev/null +++ b/src/diffulab/training/trainers/__init__.py @@ -0,0 +1,5 @@ +from .base_trainer import BaseTrainer +from .common import Trainer +from .grpo_trainer import GRPOTrainer + +__all__ = ["Trainer", "BaseTrainer", "GRPOTrainer"] diff --git a/src/diffulab/training/trainer.py b/src/diffulab/training/trainers/base_trainer.py similarity index 69% rename from src/diffulab/training/trainer.py rename to src/diffulab/training/trainers/base_trainer.py index fe08376..75a5da7 100644 --- a/src/diffulab/training/trainer.py +++ b/src/diffulab/training/trainers/base_trainer.py @@ -1,14 +1,10 @@ import logging -import os from datetime import datetime from pathlib import Path -from typing import TYPE_CHECKING, Any, Iterable, cast +from typing import TYPE_CHECKING, Any, Iterable import torch -import wandb -from accelerate import Accelerator # type: ignore [stub file not found] -from accelerate.utils import TorchDynamoPlugin # type: ignore [stub file not found] -from ema_pytorch import EMA # type: ignore [stub file not found] +from ema_pytorch import EMA # type: ignore[reportMissingTypeStubs] from torch import Tensor from torch.optim.lr_scheduler import LRScheduler from torch.optim.optimizer import Optimizer @@ -16,17 +12,17 @@ from diffulab.datasets.base import BatchData from diffulab.networks.denoisers.common import Denoiser, ModelInput +from diffulab.training.trainers.common import Trainer from diffulab.training.utils import AverageMeter if TYPE_CHECKING: from diffulab.diffuse import Diffuser -HOME_PATH = Path.home() -class Trainer: +class BaseTrainer(Trainer): """ - A training class that handles the training loop for diffusion models with support for distributed training, - mixed precision, gradient accumulation, and EMA model averaging. + A training class that handles the supervised training loop for diffusion models with support + for distributed training, mixed precision, gradient accumulation, and EMA model averaging. This class provides a complete training pipeline including validation, logging, and model checkpointing. It uses the Hugging Face Accelerate library for distributed training and handles both conditional and unconditional diffusion model training. @@ -89,42 +85,21 @@ def __init__( "dynamic": True, }, ): - assert (HOME_PATH / ".cache" / "huggingface" / "accelerate" / "default_config.yaml").exists(), ( - "please run `accelerate config` first in the CLI and save the config at the default location" + super().__init__( + n_epoch=n_epoch, + gradient_accumulation_step=gradient_accumulation_step, + precision_type=precision_type, + save_path=save_path, + project_name=project_name, + run_config=run_config, + init_kwargs=init_kwargs, + use_ema=use_ema, + ema_rate=ema_rate, + ema_update_after_step=ema_update_after_step, + ema_update_every=ema_update_every, + compile=compile, + dynamo_plugin_kwargs=dynamo_plugin_kwargs, ) - self.n_epoch = n_epoch - self.use_ema = use_ema - self.ema_rate = ema_rate - self.ema_update_after_step = ema_update_after_step * gradient_accumulation_step - self.ema_update_every = ema_update_every * gradient_accumulation_step - dynamo_plugin = TorchDynamoPlugin(**dynamo_plugin_kwargs) if compile else None - self.compile = compile - self.accelerator = Accelerator( - split_batches=True, - mixed_precision=precision_type, - gradient_accumulation_steps=gradient_accumulation_step, - log_with="wandb", - dynamo_plugin=dynamo_plugin, - ) - self.save_path = Path(save_path) / project_name - Path(self.save_path).mkdir(parents=True, exist_ok=True) - os.environ["WANDB_DIR"] = str(self.save_path / "wandb") - Path(os.environ["WANDB_DIR"]).mkdir(parents=True, exist_ok=True) - self.accelerator.init_trackers(project_name=project_name, config=run_config, init_kwargs=init_kwargs) # type: ignore - - def move_dict_to_device(self, batch: dict[str, Any]) -> dict[str, Any]: - """ - Moves all tensor values in a dictionary to the appropriate device. - This method recursively traverses a dictionary and moves any tensor values to the - device specified by the accelerator, while leaving non-tensor values unchanged. - Args: - batch (dict[str, Any]): A dictionary where values may be tensors - that need to be moved to the appropriate device. - Returns: - dict[str, Any]: A new dictionary with the same structure as the input, but with all - tensor values moved to the accelerator's device. - """ - return {k: v.to(self.accelerator.device) if isinstance(v, Tensor) else v for k, v in batch.items()} def training_step( self, @@ -200,100 +175,13 @@ def validation_step( """ model_inputs: ModelInput = val_batch["model_inputs"] timesteps = diffuser.draw_timesteps(model_inputs["x"].shape[0]).to(self.accelerator.device) - model_inputs: ModelInput = self.move_dict_to_device(model_inputs) # type: ignore + model_inputs = self.move_dict_to_device(model_inputs) # type: ignore extra_args = val_batch.get("extra", {}) extra_args = self.move_dict_to_device(extra_args) val_losses = diffuser.compute_loss(model_inputs=model_inputs, timesteps=timesteps, extra_args=extra_args) for key, val_loss in val_losses.items(): tracker.update(val_loss.item(), key=f"val/{key}") - def save_model( - self, - optimizer: Optimizer, - diffuser: "Diffuser", - ema_denoiser: EMA | None = None, - scheduler: LRScheduler | None = None, - ) -> None: - """ - Saves model checkpoints and training state. - This method saves the current state of the model, optimizer, EMA model (if used), and - scheduler (if used) to the specified save path. It handles distributed training states - by properly unwrapping models before saving. - Args: - optimizer (Optimizer): The optimizer used for training. - diffuser (Diffuser): The diffusion model wrapper containing the denoiser model. - ema_denoiser (Denoiser | None, optional): EMA version of the denoiser model. - If provided, its state will be saved. Defaults to None. - scheduler (LRScheduler | None, optional): Learning rate scheduler. - If provided, its state will be saved. Defaults to None. - Note: - The following files are created in self.save_path: - - denoiser.pt: Main model state dict - - optimizer.pt: Optimizer state dict - - ema.pt: EMA model state dict (if EMA is used) - - scheduler.pt: Scheduler state dict (if scheduler is used) - """ - unwrapped_denoiser: Denoiser = self.accelerator.unwrap_model(diffuser.denoiser) # type: ignore - state_dict = cast(dict[str, Tensor], unwrapped_denoiser.state_dict()) # type: ignore - if self.compile: - state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()} - self.accelerator.save(state_dict, self.save_path / "denoiser.pt") # type: ignore - - self.accelerator.save(optimizer.optimizer.state_dict(), self.save_path / "optimizer.pt") # type: ignore - - if ema_denoiser is not None: - unwrapped_ema = self.accelerator.unwrap_model(ema_denoiser) # type: ignore - self.accelerator.save(unwrapped_ema.ema_model.state_dict(), self.save_path / "ema.pt") # type: ignore - - if scheduler is not None: - self.accelerator.save(scheduler.scheduler.state_dict(), self.save_path / "scheduler.pt") # type: ignore - - for extra_loss in diffuser.extra_losses: - unwrapped_loss = self.accelerator.unwrap_model(extra_loss) # type: ignore - state_dict = cast(dict[str, Tensor], unwrapped_loss.state_dict()) # type: ignore - if self.compile: - state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()} - self.accelerator.save(state_dict, self.save_path / f"{unwrapped_loss.name}.pt") # type: ignore - - @torch.no_grad() # type: ignore - def log_images( - self, - diffuser: "Diffuser", - val_dataloader: Iterable[BatchData], - epoch: int, - val_steps: int = 50, - guidance_scale: float = 0, - ) -> None: - """ - Logs generated images during the validation phase. - This method generates and logs sample images using the current state of the diffusion model. - It temporarily adjusts the number of diffusion steps for faster validation image generation, - generates samples, and logs them using wandb (Weights & Biases). - Args: - diffuser (Diffuser): The diffusion model wrapper used for generating samples. - val_dataloader (Iterable[BatchData]): An iterator providing validation batches. - Each batch should contain at least an 'x' key with the input data. - epoch (int): Current training epoch number, used for logging. - val_steps (int, optional): Number of diffusion steps to use for validation - image generation. Using fewer steps speeds up generation. Defaults to 50. - guidance_scale (float, optional): Guidance scale for classifier-free guidance. - Defaults to 0. - Note: - - The method temporarily changes the number of diffusion steps to val_steps - for faster generation, then restores the original number of steps. - - Generated images are normalized from [-1, 1] to [0, 1] range before logging. - - Images are logged to wandb with the key 'val/images'. - """ - batch: ModelInput = next(iter(val_dataloader))["model_inputs"] - x: Tensor = batch.pop("x") # type: ignore - original_steps = diffuser.n_steps - diffuser.set_steps(val_steps) - images = diffuser.generate(data_shape=x.shape, model_inputs=batch, guidance_scale=guidance_scale) - images = (images * 0.5 + 0.5).clamp(0, 1).cpu() - images = wandb.Image(images, caption="Validation Images") - self.accelerator.log({"val/images": images}, step=epoch + 1, log_kwargs={"wandb": {"commit": True}}) # type: ignore - diffuser.set_steps(original_steps) - def train( self, diffuser: "Diffuser", @@ -302,7 +190,7 @@ def train( val_dataloader: Iterable[BatchData] | None = None, scheduler: LRScheduler | None = None, per_batch_scheduler: bool = False, - log_validation_images: bool = False, + log_validation_images: bool = True, train_embedder: bool = False, p_classifier_free_guidance: float = 0.2, val_steps: int = 50, @@ -327,7 +215,7 @@ def train( per_batch_scheduler (bool, optional): Whether to step scheduler after each batch instead of each epoch. Defaults to False. log_validation_images (bool, optional): Whether to generate and log sample images - during validation. Defaults to False. + during validation. Defaults to True. train_embedder (bool, optional): Whether to train the context embedder if present. Defaults to False. p_classifier_free_guidance (float, optional): Probability of using classifier-free @@ -391,12 +279,13 @@ def train( if scheduler is not None: scheduler = self.accelerator.prepare_scheduler(scheduler) # type: ignore - best_val_loss = float("inf") if diffuser.denoiser.context_embedder is not None and not train_embedder: # type: ignore for param in diffuser.denoiser.context_embedder.parameters(): # type: ignore param.requires_grad = False + best_val_loss = float("inf") + tracker = AverageMeter() tq_epoch = tqdm( range(epoch_start, self.n_epoch), disable=not self.accelerator.is_main_process, leave=False, position=0 @@ -407,7 +296,7 @@ def train( tq_epoch.set_description(f"Epoch {epoch + 1}/{self.n_epoch}") tq_batch = tqdm(train_dataloader, disable=not self.accelerator.is_main_process, leave=False) # type: ignore - for i, batch in enumerate(tq_batch): + for batch in tq_batch: with self.accelerator.accumulate(diffuser.denoiser, *diffuser.extra_losses): # type: ignore with self.accelerator.autocast(): self.training_step( @@ -423,6 +312,8 @@ def train( tq_batch.set_description( f"Loss: {sum(v for k, v in tracker.avg.items() if k.startswith('train/')):.4f}" ) + if scheduler is not None and not per_batch_scheduler: + scheduler.step() for key, value in tracker.avg.items(): if key.startswith("train/"): @@ -488,7 +379,7 @@ def train( for loss in diffuser.extra_losses: loss.set_model(original_model) # type: ignore - if total_loss < best_val_loss: # type: ignore + if total_loss < best_val_loss: best_val_loss = total_loss self.save_model(optimizer, diffuser, ema_denoiser, scheduler) # type: ignore tracker.reset() diff --git a/src/diffulab/training/trainers/common.py b/src/diffulab/training/trainers/common.py new file mode 100644 index 0000000..e6292c2 --- /dev/null +++ b/src/diffulab/training/trainers/common.py @@ -0,0 +1,247 @@ +import os +from abc import ABC, abstractmethod +from datetime import datetime +from math import ceil, sqrt +from pathlib import Path +from typing import TYPE_CHECKING, Any, Iterable, cast + +import numpy as np +import torch +import wandb +from accelerate import Accelerator # type: ignore [stub file not found] +from accelerate.utils import TorchDynamoPlugin # type: ignore [stub file not found] +from ema_pytorch import EMA # type: ignore [stub file not found] +from numpy.typing import NDArray +from torch import Tensor +from torch.optim.lr_scheduler import LRScheduler +from torch.optim.optimizer import Optimizer +from torchvision.utils import make_grid # type: ignore + +from diffulab.datasets.base import BatchData +from diffulab.networks.denoisers.common import Denoiser, ModelInput + +if TYPE_CHECKING: + from diffulab.diffuse import Diffuser +HOME_PATH = Path.home() + + +class Trainer(ABC): + """ + An abstract training class for diffusion models with support for distributed training, mixed precision, gradient accumulation, + and EMA model averaging. + It uses the Hugging Face Accelerate library for distributed training. + Training and validation loops need to be implemented in subclasses. + Args: + n_epoch (int): Number of training epochs. + gradient_accumulation_step (int, optional): Number of steps to accumulate gradients. Defaults to 1. + precision_type (str, optional): Type of precision for mixed precision training ("no", "fp16", "bf16"). + Defaults to "no". + save_path (str | Path, optional): Path to save model checkpoints and logs. Defaults to a timestamped + directory under ~/experiments. + project_name (str, optional): Name of the project for logging purposes. Defaults to "my_project". + run_config (dict[str, Any] | None, optional): Configuration dictionary for the training run. + Defaults to None. + init_kwargs (dict[str, Any], optional): Additional initialization arguments for trackers. + Defaults to {}. + use_ema (bool, optional): Whether to use Exponential Moving Average of the model weights. + Defaults to False. + ema_rate (float, optional): Decay rate for EMA. Defaults to 0.999. + ema_update_after_step (int, optional): Number of steps before starting EMA updates. + Defaults to 100. + ema_update_every (int, optional): Frequency of EMA updates. Defaults to 1. + compile (bool, optional): Whether to compile the model using TorchDynamo. Defaults to True. + dynam_plugin_kwargs (dict[str, Any], optional): Additional arguments for the TorchDynamo plugin. + Defaults to {}. + Attributes: + n_epoch (int): Number of training epochs. + use_ema (bool): Whether EMA is enabled. + ema_rate (float): EMA decay rate. + ema_update_after_step (int): Steps before EMA updates begin. + ema_update_every (int): EMA update frequency. + accelerator (Accelerator): Hugging Face Accelerator instance. + save_path (Path): Path for saving outputs. + Methods: + training_step: Performs a single training step. + move_dict_to_device: Moves batch data to the appropriate device. + validation_step: Performs a single validation step. + save_model: Saves model checkpoints. + log_images: Logs generated images during validation. + train: Main training loop. + """ + + def __init__( + self, + n_epoch: int, + gradient_accumulation_step: int = 1, + precision_type: str = "no", + save_path: str | Path = Path.home() / "experiments" / f"{datetime.now().strftime('%Y%m%d_%H%M%S')}", + project_name: str = "my_project", + run_config: dict[str, Any] | None = None, + init_kwargs: dict[str, Any] = {}, + use_ema: bool = False, + ema_rate: float = 0.999, + ema_update_after_step: int = 0, + ema_update_every: int = 10, + compile: bool = False, + dynamo_plugin_kwargs: dict[str, Any] = { + "backend": "inductor", + "mode": "default", + "fullgraph": False, + "dynamic": True, + }, + ): + assert (HOME_PATH / ".cache" / "huggingface" / "accelerate" / "default_config.yaml").exists(), ( + "please run `accelerate config` first in the CLI and save the config at the default location" + ) + self.n_epoch = n_epoch + self.use_ema = use_ema + self.ema_rate = ema_rate + self.ema_update_after_step = ema_update_after_step * gradient_accumulation_step + self.ema_update_every = ema_update_every * gradient_accumulation_step + dynamo_plugin = TorchDynamoPlugin(**dynamo_plugin_kwargs) if compile else None + self.compile = compile + self.accelerator = Accelerator( + split_batches=True, + mixed_precision=precision_type, + gradient_accumulation_steps=gradient_accumulation_step, + log_with="wandb", + dynamo_plugin=dynamo_plugin, + ) + self.save_path = Path(save_path) / project_name + Path(self.save_path).mkdir(parents=True, exist_ok=True) + os.environ["WANDB_DIR"] = str(self.save_path / "wandb") + Path(os.environ["WANDB_DIR"]).mkdir(parents=True, exist_ok=True) + self.accelerator.init_trackers(project_name=project_name, config=run_config, init_kwargs=init_kwargs) # type: ignore + + def move_dict_to_device(self, batch: dict[str, Any]) -> dict[str, Any]: + """ + Moves all tensor values in a dictionary to the appropriate device. + This method recursively traverses a dictionary and moves any tensor values to the + device specified by the accelerator, while leaving non-tensor values unchanged. + Args: + batch (dict[str, Any]): A dictionary where values may be tensors + that need to be moved to the appropriate device. + Returns: + dict[str, Any]: A new dictionary with the same structure as the input, but with all + tensor values moved to the accelerator's device. + """ + return {k: v.to(self.accelerator.device) if isinstance(v, Tensor) else v for k, v in batch.items()} + + def save_model( + self, + optimizer: Optimizer, + diffuser: "Diffuser", + ema_denoiser: EMA | None = None, + scheduler: LRScheduler | None = None, + ) -> None: + """ + Saves model checkpoints and training state. + This method saves the current state of the model, optimizer, EMA model (if used), and + scheduler (if used) to the specified save path. It handles distributed training states + by properly unwrapping models before saving. + Args: + optimizer (Optimizer): The optimizer used for training. + diffuser (Diffuser): The diffusion model wrapper containing the denoiser model. + ema_denoiser (Denoiser | None, optional): EMA version of the denoiser model. + If provided, its state will be saved. Defaults to None. + scheduler (LRScheduler | None, optional): Learning rate scheduler. + If provided, its state will be saved. Defaults to None. + Note: + The following files are created in self.save_path: + - denoiser.pt: Main model state dict + - optimizer.pt: Optimizer state dict + - ema.pt: EMA model state dict (if EMA is used) + - scheduler.pt: Scheduler state dict (if scheduler is used) + """ + unwrapped_denoiser: Denoiser = self.accelerator.unwrap_model(diffuser.denoiser) # type: ignore + state_dict = cast(dict[str, Tensor], unwrapped_denoiser.state_dict()) # type: ignore + if self.compile: + state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()} + self.accelerator.save(state_dict, self.save_path / "denoiser.pt") # type: ignore + + self.accelerator.save(optimizer.optimizer.state_dict(), self.save_path / "optimizer.pt") # type: ignore + + if ema_denoiser is not None: + unwrapped_ema = self.accelerator.unwrap_model(ema_denoiser) # type: ignore + self.accelerator.save(unwrapped_ema.ema_model.state_dict(), self.save_path / "ema.pt") # type: ignore + + if scheduler is not None: + self.accelerator.save(scheduler.scheduler.state_dict(), self.save_path / "scheduler.pt") # type: ignore + + for extra_loss in diffuser.extra_losses: + unwrapped_loss = self.accelerator.unwrap_model(extra_loss) # type: ignore + state_dict = cast(dict[str, Tensor], unwrapped_loss.state_dict()) # type: ignore + if self.compile: + state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()} + self.accelerator.save(state_dict, self.save_path / f"{unwrapped_loss.name}.pt") # type: ignore + + @torch.no_grad() # type: ignore + def log_images( + self, + diffuser: "Diffuser", + val_dataloader: Iterable[BatchData], + epoch: int, + val_steps: int = 50, + guidance_scale: float = 0, + ) -> None: + """ + Logs generated images during the validation phase. + This method generates and logs sample images using the current state of the diffusion model. + It temporarily adjusts the number of diffusion steps for faster validation image generation, + generates samples, and logs them using wandb (Weights & Biases). + Args: + diffuser (Diffuser): The diffusion model wrapper used for generating samples. + val_dataloader (Iterable[BatchData]): An iterator providing validation batches. + Each batch should contain at least an 'x' key with the input data. + epoch (int): Current training epoch number, used for logging. + val_steps (int, optional): Number of diffusion steps to use for validation + image generation. Using fewer steps speeds up generation. Defaults to 50. + guidance_scale (float, optional): Guidance scale for classifier-free guidance. + Defaults to 0. + Note: + - The method temporarily changes the number of diffusion steps to val_steps + for faster generation, then restores the original number of steps. + - Generated images are normalized from [-1, 1] to [0, 1] range before logging. + - Images are logged to wandb with the key 'val/images'. + """ + batch: ModelInput = next(iter(val_dataloader))["model_inputs"] + x: Tensor = batch.pop("x") # type: ignore + original_steps = diffuser.n_steps + diffuser.set_steps(val_steps) + images = diffuser.generate(data_shape=x.shape, model_inputs=batch, guidance_scale=guidance_scale)["x"] + images = (images * 0.5 + 0.5).clamp(0, 1).cpu() + + grid = make_grid(images, nrow=int(ceil(sqrt(images.shape[0]))), padding=2) + np_grid: NDArray[np.uint8] = (grid * 255).round().byte().permute(1, 2, 0).numpy() # type: ignore + to_log = wandb.Image(np_grid, caption="Validation Images") + self.accelerator.log({"val/images": to_log}, step=epoch + 1, log_kwargs={"wandb": {"commit": True}}) # type: ignore + diffuser.set_steps(original_steps) + + @abstractmethod + def training_step(self, *args: Any, **kwargs: Any) -> None: + """ + Performs a single training step. + This method should be implemented in subclasses to define the specific operations + that occur during a single training step, including forward pass, loss computation, + backward pass, and optimizer step. + """ + ... + + @abstractmethod + def validation_step(self, *args: Any, **kwargs: Any) -> None: + """ + Performs a single validation step. + This method should be implemented in subclasses to define the specific operations + that occur during a single validation step, including forward pass and loss computation. + """ + ... + + @abstractmethod + def train(self, *args: Any, **kwargs: Any) -> None: + """ + Main training loop. + This method should be implemented in subclasses to define the overall training process, + including iterating over epochs and batches, calling training and validation steps, + logging metrics, and saving model checkpoints. + """ + ... diff --git a/src/diffulab/training/trainers/extra/__init__.py b/src/diffulab/training/trainers/extra/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/diffulab/training/trainers/extra/rae_trainer.py b/src/diffulab/training/trainers/extra/rae_trainer.py new file mode 100644 index 0000000..c303755 --- /dev/null +++ b/src/diffulab/training/trainers/extra/rae_trainer.py @@ -0,0 +1,552 @@ +import logging +import os +import random +from datetime import datetime +from math import ceil, sqrt +from pathlib import Path +from typing import Any, Iterable, cast + +import numpy as np +import torch +import torchvision.transforms.v2 as v2 # type: ignore[reportMissingTypeStubs] +import wandb +from accelerate import Accelerator # type: ignore [stub file not found] +from accelerate.utils import TorchDynamoPlugin # type: ignore [stub file not found] +from ema_pytorch import EMA # type: ignore[reportMissingTypeStubs] +from numpy.typing import NDArray +from torch import Tensor +from torch.optim.lr_scheduler import LRScheduler +from torch.optim.optimizer import Optimizer +from torchvision.utils import make_grid # type: ignore +from tqdm import tqdm + +from diffulab.datasets.base import BatchData +from diffulab.networks.disc.rae import RAEDiscriminator +from diffulab.networks.vision_towers.rae import RAE, RAEDecoder +from diffulab.training.losses.rae import LPIPS, GANLoss +from diffulab.training.utils import AverageMeter + +HOME_PATH = Path.home() + + +class RAETrainer: + """ + A training class that handles the supervised training loop for the RAE rae with support + for distributed training, mixed precision, gradient accumulation, and EMA model averaging. + This class provides a complete training pipeline including validation, logging, and model checkpointing. + It uses the Hugging Face Accelerate library for distributed training. + Args: + n_epoch (int): Number of training epochs. + gradient_accumulation_step (int, optional): Number of steps to accumulate gradients. Defaults to 1. + precision_type (str, optional): Type of precision for mixed precision training ("no", "fp16", "bf16"). + Defaults to "no". + save_path (str | Path, optional): Path to save model checkpoints and logs. Defaults to a timestamped + directory under ~/experiments. + project_name (str, optional): Name of the project for logging purposes. Defaults to "my_project". + run_config (dict[str, Any] | None, optional): Configuration dictionary for the training run. + Defaults to None. + init_kwargs (dict[str, Any], optional): Additional initialization arguments for trackers. + Defaults to {}. + use_ema (bool, optional): Whether to use Exponential Moving Average of the model weights. + Defaults to False. + ema_rate (float, optional): Decay rate for EMA. Defaults to 0.999. + ema_update_after_step (int, optional): Number of steps before starting EMA updates. + Defaults to 100. + ema_update_every (int, optional): Frequency of EMA updates. Defaults to 1. + compile (bool, optional): Whether to compile the model using TorchDynamo. Defaults to True. + dynam_plugin_kwargs (dict[str, Any], optional): Additional arguments for the TorchDynamo plugin. + Defaults to {}. + Attributes: + n_epoch (int): Number of training epochs. + use_ema (bool): Whether EMA is enabled. + ema_rate (float): EMA decay rate. + ema_update_after_step (int): Steps before EMA updates begin. + ema_update_every (int): EMA update frequency. + accelerator (Accelerator): Hugging Face Accelerator instance. + save_path (Path): Path for saving outputs. + Methods: + training_step: Performs a single training step. + move_dict_to_device: Moves batch data to the appropriate device. + validation_step: Performs a single validation step. + save_model: Saves model checkpoints. + log_images: Logs generated images during validation. + train: Main training loop. + """ + + def __init__( + self, + n_epoch: int, + gradient_accumulation_step: int = 1, + precision_type: str = "no", + save_path: str | Path = Path.home() / "experiments" / f"{datetime.now().strftime('%Y%m%d_%H%M%S')}", + project_name: str = "my_project", + run_config: dict[str, Any] | None = None, + init_kwargs: dict[str, Any] = {}, + use_ema: bool = False, + ema_rate: float = 0.999, + ema_update_after_step: int = 0, + ema_update_every: int = 10, + compile: bool = False, + dynamo_plugin_kwargs: dict[str, Any] = { + "backend": "inductor", + "mode": "default", + "fullgraph": False, + "dynamic": True, + }, + ): + assert (HOME_PATH / ".cache" / "huggingface" / "accelerate" / "default_config.yaml").exists(), ( + "please run `accelerate config` first in the CLI and save the config at the default location" + ) + self.n_epoch = n_epoch + self.use_ema = use_ema + self.ema_rate = ema_rate + self.ema_update_after_step = ema_update_after_step * gradient_accumulation_step + self.ema_update_every = ema_update_every * gradient_accumulation_step + dynamo_plugin = TorchDynamoPlugin(**dynamo_plugin_kwargs) if compile else None + self.compile = compile + self.accelerator = Accelerator( + split_batches=True, + mixed_precision=precision_type, + gradient_accumulation_steps=gradient_accumulation_step, + log_with="wandb", + dynamo_plugin=dynamo_plugin, + ) + self.save_path = Path(save_path) / project_name + Path(self.save_path).mkdir(parents=True, exist_ok=True) + os.environ["WANDB_DIR"] = str(self.save_path / "wandb") + Path(os.environ["WANDB_DIR"]).mkdir(parents=True, exist_ok=True) + self.accelerator.init_trackers(project_name=project_name, config=run_config, init_kwargs=init_kwargs) # type: ignore + + def disc_augment( + self, x: Tensor, prob: float = 1.0, cutout_ratio: float = 0.2, translate_ratio: float = 0.125 + ) -> Tensor: + """Apply data augmentation to the input tensor for the discriminator. + + Args: + x (Tensor): Input tensor of shape (B, C, H, W) in the range [-1, 1]. + Returns: + Tensor: Augmented tensor of shape (B, C, H, W) in the range [-1, 1]. + """ + x = x.float() + x = (x + 1.0) * 0.5 # rescale to [0, 1] + _, _, H, W = x.shape + dtype = x.dtype + # crop + if random.random() < prob: + delta = int(round(translate_ratio * min(H, W))) + translate = v2.RandomCrop(size=(H, W), padding=(delta, delta, delta, delta), pad_if_needed=False) + x = translate(x) + # color + if random.random() < prob: + bias = AddPerChannelBias(0.5) + color = v2.ColorJitter(brightness=0.5, contrast=(0.5, 1.5)) + x = bias(x) + x = color(x) + x = x.clamp(0.0, 1.0).to(dtype) + # cutout + if random.random() < prob: + area = cutout_ratio**2 + cutout = v2.RandomErasing(p=1.0, scale=(area, area), ratio=(W / H, W / H), value=0.0, inplace=False) + x = cutout(x) + x = x * 2.0 - 1.0 # rescale to [-1, 1] + x = x.to(dtype) + return x + + @torch.no_grad() # type: ignore + def log_images( + self, + rae: RAE, + val_dataloader: Iterable[BatchData], + epoch: int, + ) -> None: + batch = next(iter(val_dataloader)) + real = cast(torch.Tensor | None, batch.get("extra", {}).get("x0")).cpu() # type: ignore + assert real is not None, "Validation batch must contain 'x0' in extra" + reconstruct = rae.decode(batch["model_inputs"]["x"]) + reconstruct = (reconstruct * 0.5 + 0.5).clamp(0, 1).cpu() + paired = torch.cat([real, reconstruct], dim=-1) + grid = make_grid(paired, nrow=int(ceil(sqrt(real.shape[0]))), padding=2) + np_grid: NDArray[np.uint8] = (grid * 255).round().byte().permute(1, 2, 0).numpy() # type: ignore + to_log = wandb.Image(np_grid, caption="Validation: Real (left) vs Reconstruction (right)") + self.accelerator.log({"val/images_comparison": to_log}, step=epoch + 1, log_kwargs={"wandb": {"commit": True}}) # type: ignore + + def save_model( + self, + rae: RAE, + disc: RAEDiscriminator, + rae_optimizer: Optimizer, + disc_optimizer: Optimizer, + ema: EMA | None = None, + rae_scheduler: LRScheduler | None = None, + disc_scheduler: LRScheduler | None = None, + ) -> None: + unwrapped_rae: RAE = self.accelerator.unwrap_model(rae) # type: ignore + state_dict = cast(dict[str, Tensor], unwrapped_rae.decoder.state_dict()) # type: ignore + if self.compile: + state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()} + self.accelerator.save(state_dict, self.save_path / "rae_decoder.pt") # type: ignore + + unwrapped_disc: RAEDiscriminator = self.accelerator.unwrap_model(disc) # type: ignore + state_dict = cast(dict[str, Tensor], unwrapped_disc.state_dict()) # type: ignore + if self.compile: + state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()} + self.accelerator.save(state_dict, self.save_path / "rae_discriminator.pt") # type: ignore + + self.accelerator.save(rae_optimizer.optimizer.state_dict(), self.save_path / "rae_optimizer.pt") # type: ignore + self.accelerator.save(disc_optimizer.optimizer.state_dict(), self.save_path / "disc_optimizer.pt") # type: ignore + + if ema is not None: + unwrapped_ema = self.accelerator.unwrap_model(ema) # type: ignore + self.accelerator.save(unwrapped_ema.ema_model.state_dict(), self.save_path / "ema.pt") # type: ignore + + if rae_scheduler is not None: + self.accelerator.save(rae_scheduler.scheduler.state_dict(), self.save_path / "rae_scheduler.pt") # type: ignore + if disc_scheduler is not None: + self.accelerator.save(disc_scheduler.scheduler.state_dict(), self.save_path / "disc_scheduler.pt") # type: ignore + + def training_step( + self, + rae: RAE, + disc: RAEDiscriminator, + rae_optimizer: Optimizer, + disc_optimizer: Optimizer, + batch: BatchData, + lpips_loss: LPIPS, + gan_loss: GANLoss, + train_gan: bool, + train_disc: bool, + tracker: AverageMeter, + rae_scheduler: LRScheduler | None = None, + disc_scheduler: LRScheduler | None = None, + per_batch_scheduler: bool = False, + ema: EMA | None = None, + lambda_lpips: float = 1.0, + lambda_gan: float = 0.75, + use_adaptive_weight_loss: bool = True, + use_lpips: bool = True, + ) -> None: + # Train generator (RAE) + for p in disc.heads.parameters(): + p.requires_grad = False + rae_optimizer.zero_grad(set_to_none=True) + real = cast(torch.Tensor | None, batch.get("extra", {}).get("x0")) + assert real is not None, "Training batch must contain 'x0' in extra" + real_rescaled = real * 2.0 - 1.0 # rescale to [-1, 1] + # rae input is assumed to be in [0, 1] and output in [-1, 1] for consistency in our framework + fake = rae.decode(batch["model_inputs"]["x"]) + loss_rae = torch.nn.functional.l1_loss(fake, real_rescaled) + tracker.update(key="train/loss_l1", val=loss_rae.item()) + if use_lpips: + loss_lpips: Tensor = lpips_loss(fake, real_rescaled) + tracker.update(key="train/loss_lpips", val=loss_lpips.item()) + loss_rae = loss_rae + lambda_lpips * loss_lpips + if train_gan: + # Compute adversarial loss for RAE + disc.eval() + fake_aug = self.disc_augment(fake) + fake_pred = disc(fake_aug * 0.5 + 0.5) + loss_gan_rae: Tensor = gan_loss(logits_fake=fake_pred, is_disc=False) + if use_adaptive_weight_loss: + # Compute adaptive weight for GAN loss + loss_rae_grads = torch.autograd.grad( + loss_rae, cast(torch.nn.Linear, rae.decoder.last_layer[1]).weight, retain_graph=True + )[0] + loss_gan_grads = torch.autograd.grad( + loss_gan_rae, cast(torch.nn.Linear, rae.decoder.last_layer[1]).weight, retain_graph=True + )[0] + adaptive_weight = cast(Tensor, torch.norm(loss_rae_grads) / (torch.norm(loss_gan_grads) + 1e-6)) # type: ignore + adaptive_weight = torch.clamp(adaptive_weight, 0.0, 1e4).detach() + loss_rae = loss_rae + adaptive_weight * lambda_gan * loss_gan_rae + else: + loss_rae = loss_rae + lambda_gan * loss_gan_rae + tracker.update(key="train/loss_gan_rae", val=loss_gan_rae.item()) + self.accelerator.backward(loss_rae) # type: ignore + rae_optimizer.step() + if rae_scheduler is not None and per_batch_scheduler: + rae_scheduler.step() + if ema is not None: + ema.update() + + # train discriminator + for p in disc.heads.parameters(): + p.requires_grad = True + if train_disc: + disc.train() + disc_optimizer.zero_grad(set_to_none=True) + fake = fake.detach() + fake_aug = self.disc_augment(fake) + real_aug = self.disc_augment(real_rescaled) + fake_pred = disc(fake_aug * 0.5 + 0.5) # rescale to [0, 1] + real_pred = disc(real_aug * 0.5 + 0.5) # rescale to [0, 1] + loss_disc: Tensor = gan_loss(logits_real=real_pred, logits_fake=fake_pred, is_disc=True) + tracker.update(key="train_disc/loss_disc", val=loss_disc.item()) + self.accelerator.backward(loss_disc) # type: ignore + disc_optimizer.step() + if disc_scheduler is not None and per_batch_scheduler: + disc_scheduler.step() + + def validation_step( + self, + rae: RAE, + disc: RAEDiscriminator, + batch: BatchData, + lpips_loss: LPIPS, + gan_loss: GANLoss, + train_gan: bool, + train_disc: bool, + tracker: AverageMeter, + use_lpips: bool = True, + ) -> None: + real = cast(torch.Tensor | None, batch.get("extra", {}).get("x0")) + assert real is not None, "Validation batch must contain 'x0' in extra" + real_rescaled = real * 2.0 - 1.0 # rescale to [-1, 1] + fake = rae.decode(batch["model_inputs"]["x"]) + loss_l1 = torch.nn.functional.l1_loss(fake, real_rescaled) + tracker.update(key="val/loss_l1", val=loss_l1.item()) + if use_lpips: + loss_lpips: Tensor = lpips_loss(fake, real_rescaled) + tracker.update(key="val/loss_lpips", val=loss_lpips.item()) + if train_gan: + fake_pred = disc(fake * 0.5 + 0.5) # rescale to [0, 1] + loss_gan_rae: Tensor = gan_loss(logits_fake=fake_pred, is_disc=False) + tracker.update(key="val/loss_gan_rae", val=loss_gan_rae.item()) + + # train discriminator + if train_disc: + fake_pred = disc(fake * 0.5 + 0.5) # rescale to [0, 1] + real_pred = disc(real) + loss_disc: Tensor = gan_loss(logits_real=real_pred, logits_fake=fake_pred, is_disc=True) + tracker.update(key="val_disc/loss_disc", val=loss_disc.item()) + + def train( + self, + rae: RAE, + disc: RAEDiscriminator, + rae_optimizer: Optimizer, + disc_optimizer: Optimizer, + train_dataloader: Iterable[BatchData], + val_dataloader: Iterable[BatchData] | None = None, + rae_scheduler: LRScheduler | None = None, + disc_scheduler: LRScheduler | None = None, + per_batch_scheduler: bool = False, + log_validation_images: bool = True, + rae_optimizer_ckpt: str | None = None, + disc_optimizer_ckpt: str | None = None, + rae_scheduler_ckpt: str | None = None, + disc_scheduler_ckpt: str | None = None, + rae_ckpt: str | None = None, + disc_ckpt: str | None = None, + ema_ckpt: str | None = None, + epoch_start: int = 0, + disc_epoch_start: int = 6, + gan_epoch_start: int = 8, + lpips_epoch_start: int = 1, + lambda_lpips: float = 1.0, + lambda_gan: float = 0.75, + use_adaptive_weight_loss: bool = True, + hinge_gan: bool = True, + ): + assert not (self.compile and use_adaptive_weight_loss), ( + "Adaptive weight loss is not supported with compiled mode (double backward)." + ) + if self.use_ema: + ema = EMA( + rae.decoder, + beta=self.ema_rate, + update_after_step=self.ema_update_after_step, + update_every=self.ema_update_every, + ).to(self.accelerator.device) + if ema_ckpt: + ema.ema_model.load_state_dict(torch.load(ema_ckpt, weights_only=True)) # type: ignore + ema = self.accelerator.prepare(ema) # type: ignore + else: + ema = None + + if rae_ckpt: + rae.decoder.load_state_dict( + torch.load(rae_ckpt), # type: ignore + ) + + if disc_ckpt: + disc.load_state_dict( + torch.load(disc_ckpt), # type: ignore + ) + + if rae_optimizer_ckpt: + rae_optimizer.load_state_dict( + torch.load(rae_optimizer_ckpt, weights_only=False), # type: ignore + ) + + if disc_optimizer_ckpt: + disc_optimizer.load_state_dict( + torch.load(disc_optimizer_ckpt, weights_only=False), # type: ignore + ) + + if rae_scheduler_ckpt and rae_scheduler is not None: + rae_scheduler.load_state_dict( + torch.load(rae_scheduler_ckpt, weights_only=False), # type: ignore + ) + if disc_scheduler_ckpt and disc_scheduler is not None: + disc_scheduler.load_state_dict( + torch.load(disc_scheduler_ckpt, weights_only=False), # type: ignore + ) + + gan_loss = GANLoss(gan_type="hinge" if hinge_gan else "vanilla") + lpips_loss = LPIPS() + + rae, disc, train_dataloader, val_dataloader, rae_optimizer, disc_optimizer, lpips_loss, gan_loss = ( # type: ignore + self.accelerator.prepare( # type: ignore + rae, disc, train_dataloader, val_dataloader, rae_optimizer, disc_optimizer, lpips_loss, gan_loss + ) + ) + + if rae_optimizer_ckpt: + device = self.accelerator.device + for state in rae_optimizer.state.values(): # type: ignore + for k, v in state.items(): # type: ignore + if isinstance(v, Tensor): + state[k] = v.to(device) + + if disc_optimizer_ckpt: + device = self.accelerator.device + for state in disc_optimizer.state.values(): # type: ignore + for k, v in state.items(): # type: ignore + if isinstance(v, Tensor): + state[k] = v.to(device) + + if rae_scheduler is not None: + rae_scheduler = self.accelerator.prepare_scheduler(rae_scheduler) + if disc_scheduler is not None: + disc_scheduler = self.accelerator.prepare_scheduler(disc_scheduler) + + tracker = AverageMeter() + tq_epoch = tqdm( + range(epoch_start, self.n_epoch), disable=not self.accelerator.is_main_process, leave=False, position=0 + ) + logging.info("Begin training") + for epoch in tq_epoch: + rae.train() # type: ignore + disc.train() # type: ignore + tq_epoch.set_description(f"Epoch {epoch + 1}/{self.n_epoch}") + + tq_batch = tqdm(train_dataloader, disable=not self.accelerator.is_main_process, leave=False) # type: ignore + for batch in tq_batch: + with self.accelerator.accumulate(rae, disc): # type: ignore + with self.accelerator.autocast(): + self.training_step( + rae=rae, # type: ignore + disc=disc, # type: ignore + rae_optimizer=rae_optimizer, # type: ignore + disc_optimizer=disc_optimizer, # type: ignore + batch=batch, + lpips_loss=lpips_loss, # type: ignore + gan_loss=gan_loss, # type: ignore + tracker=tracker, + rae_scheduler=rae_scheduler, + disc_scheduler=disc_scheduler, + per_batch_scheduler=per_batch_scheduler, + ema=ema, # type: ignore + lambda_lpips=lambda_lpips, + lambda_gan=lambda_gan, + use_adaptive_weight_loss=use_adaptive_weight_loss, + train_gan=(epoch + 1 >= gan_epoch_start), + train_disc=(epoch + 1 >= disc_epoch_start), + use_lpips=(epoch + 1 >= lpips_epoch_start), + ) + tq_batch.set_description( + f"Loss: {sum(v for k, v in tracker.avg.items() if k.startswith('train/')):.4f}" + ) + if rae_scheduler is not None and not per_batch_scheduler: + rae_scheduler.step() + if disc_scheduler is not None and not per_batch_scheduler and epoch + 1 >= disc_epoch_start: + disc_scheduler.step() + + for key, value in tracker.avg.items(): + if key.startswith("train"): + gathered_loss: Tensor = self.accelerator.gather( # type: ignore + torch.tensor(value, device=self.accelerator.device) + ) + self.accelerator.log( # type: ignore + {key: gathered_loss.mean().item()}, step=epoch + 1 + ) + tracker.reset() + + if val_dataloader is not None: + rae.eval() # type: ignore + disc.eval() # type: ignore + original_model: RAEDecoder = rae.decoder # type: ignore + if ema is not None: + rae.decoder = ema.ema_model.eval() # type: ignore + + tq_val_batch = tqdm( + val_dataloader, # type: ignore + disable=not self.accelerator.is_main_process, + leave=False, + position=1, + ) + for val_batch in tq_val_batch: + with self.accelerator.autocast(): + self.validation_step( + rae=rae, # type: ignore + disc=disc, # type: ignore + batch=val_batch, + lpips_loss=lpips_loss, # type: ignore + gan_loss=gan_loss, # type: ignore + train_gan=(epoch + 1 >= gan_epoch_start), + train_disc=(epoch + 1 >= disc_epoch_start), + tracker=tracker, + use_lpips=(epoch + 1 >= lpips_epoch_start), + ) + tq_val_batch.set_description( + f"Val Loss: {sum(v for k, v in tracker.avg.items() if k.startswith('val/')):.4f}" + ) + + for key, value in tracker.avg.items(): + if key.startswith("val"): + gathered_loss: Tensor = self.accelerator.gather( # type: ignore + torch.tensor(value, device=self.accelerator.device) + ) + self.accelerator.log( # type: ignore + {key: gathered_loss.mean().item()}, step=epoch + 1 + ) + + if log_validation_images: + logging.info("creating validation images") + if self.accelerator.is_main_process: + with self.accelerator.autocast(): + self.log_images( + rae=rae, # type: ignore + val_dataloader=val_dataloader, # type: ignore + epoch=epoch, + ) + + if ema is not None: + # Restore original model + rae.decoder = original_model + + self.save_model( + rae=rae, # type: ignore + disc=disc, # type: ignore + rae_optimizer=rae_optimizer, # type: ignore + disc_optimizer=disc_optimizer, # type: ignore + ema=ema, # type: ignore + rae_scheduler=rae_scheduler, + disc_scheduler=disc_scheduler, + ) + tracker.reset() + + self.accelerator.wait_for_everyone() + + self.accelerator.end_training() + logging.info("Training complete") + + +class AddPerChannelBias(torch.nn.Module): + def __init__(self, max_abs: float = 0.5): + super().__init__() # type: ignore + self.max_abs = max_abs + + def forward(self, x: Tensor) -> Tensor: + B, C, _, _ = x.shape + bias = (torch.rand(B, C, 1, 1, device=x.device, dtype=x.dtype) - 0.5) * 2 * self.max_abs + return x + bias diff --git a/src/diffulab/training/trainers/grpo_trainer.py b/src/diffulab/training/trainers/grpo_trainer.py new file mode 100644 index 0000000..135207f --- /dev/null +++ b/src/diffulab/training/trainers/grpo_trainer.py @@ -0,0 +1,633 @@ +import logging +from datetime import datetime +from pathlib import Path +from typing import TYPE_CHECKING, Any, Iterable, cast + +import torch +from ema_pytorch import EMA # type: ignore[reportMissingTypeStubs] +from torch import Tensor +from torch.optim.lr_scheduler import LRScheduler +from torch.optim.optimizer import Optimizer +from tqdm import tqdm + +from diffulab.datasets.base import BatchData, BatchDataGRPO +from diffulab.diffuse.utils import SamplingOutput +from diffulab.networks.denoisers.common import ModelInput +from diffulab.networks.rewards import RewardModel +from diffulab.training.trainers.common import Trainer +from diffulab.training.utils import AverageMeter + +if TYPE_CHECKING: + from diffulab.diffuse import Diffuser + from diffulab.networks.denoisers import Denoiser + + +class GRPOTrainer(Trainer): + """ + A training class that handles the GRPO alignment training loop for diffusion/flow models with support + for distributed training, mixed precision, gradient accumulation, and EMA model averaging. + This class provides a complete training pipeline including validation, logging, and model checkpointing. + It uses the Hugging Face Accelerate library for distributed training. + + Args: + n_epoch (int): Number of training epochs. + gradient_accumulation_step (int, optional): Number of steps to accumulate gradients. Defaults to 1. + precision_type (str, optional): Type of precision for mixed precision training ("no", "fp16", "bf16"). + Defaults to "no". + save_path (str | Path, optional): Path to save model checkpoints and logs. Defaults to a timestamped + directory under ~/experiments. + project_name (str, optional): Name of the project for logging purposes. Defaults to "my_project". + run_config (dict[str, Any] | None, optional): Configuration dictionary for the training run. + Defaults to None. + init_kwargs (dict[str, Any], optional): Additional initialization arguments for trackers. + Defaults to {}. + use_ema (bool, optional): Whether to use Exponential Moving Average of the model weights. + Defaults to False. + ema_rate (float, optional): Decay rate for EMA. Defaults to 0.999. + ema_update_after_step (int, optional): Number of steps before starting EMA updates. + Defaults to 100. + ema_update_every (int, optional): Frequency of EMA updates. Defaults to 1. + compile (bool, optional): Whether to compile the model using TorchDynamo. Defaults to True. + dynam_plugin_kwargs (dict[str, Any], optional): Additional arguments for the TorchDynamo plugin. + Defaults to {}. + eta (float, optional): The eta parameter for GRPO sampling. Defaults to 0.7. Controls the amount of + stochasticity during the sampling process. + timestep_fraction (float, optional): Fraction of the total timesteps to consider for GRPO loss + computation. Defaults to 0.6. + kl_beta (float, optional): Weight of the KL divergence term in the GRPO loss. Defaults to 0.0. + eps (float, optional): Small constant for numerical stability in GRPO loss computation. Defaults to 1e-4. + + Attributes: + n_epoch (int): Number of training epochs. + use_ema (bool): Whether EMA is enabled. + ema_rate (float): EMA decay rate. + ema_update_after_step (int): Steps before EMA updates begin. + ema_update_every (int): EMA update frequency. + accelerator (Accelerator): Hugging Face Accelerator instance. + save_path (Path): Path for saving outputs. + Methods: + training_step: Performs a single training step. + move_dict_to_device: Moves batch data to the appropriate device. + validation_step: Performs a single validation step. + save_model: Saves model checkpoints. + log_images: Logs generated images during validation. + train: Main training loop. + """ + + def __init__( + self, + n_epoch: int, + gradient_accumulation_step: int = 1, + precision_type: str = "no", + save_path: str | Path = Path.home() / "experiments" / f"{datetime.now().strftime('%Y%m%d_%H%M%S')}", + project_name: str = "my_project", + run_config: dict[str, Any] | None = None, + init_kwargs: dict[str, Any] = {}, + use_ema: bool = False, + ema_rate: float = 0.999, + ema_update_after_step: int = 0, + ema_update_every: int = 10, + compile: bool = False, + dynamo_plugin_kwargs: dict[str, Any] = { + "backend": "inductor", + "mode": "default", + "fullgraph": False, + "dynamic": True, + }, + timestep_fraction: float = 0.6, + kl_beta: float = 0.0, + eps: float = 1e-4, + ): + super().__init__( + n_epoch=n_epoch, + gradient_accumulation_step=gradient_accumulation_step, + precision_type=precision_type, + save_path=save_path, + project_name=project_name, + run_config=run_config, + init_kwargs=init_kwargs, + use_ema=use_ema, + ema_rate=ema_rate, + ema_update_after_step=ema_update_after_step, + ema_update_every=ema_update_every, + compile=compile, + dynamo_plugin_kwargs=dynamo_plugin_kwargs, + ) + self.timestep_fraction = timestep_fraction + self.kl_beta = kl_beta + self.eps = eps + + def repeat_batch(self, batch: BatchDataGRPO, n_repeat: int) -> BatchData: + """ + Repeat a GRPO batch n_repeat times along the batch dimension. + + This is used to expand each prompt into multiple samples (e.g., n_image_per_prompt) + while keeping per-prompt metadata aligned. Supports Tensor and list fields in + model_inputs and extra. Floats in model_inputs are kept unchanged, and None in extra + are preserved. + + Args: + batch (BatchDataGRPO): Input batch with keys: + - model_inputs: dict of tensors/lists used by the denoiser. + - extra: dict containing auxiliary fields (e.g., 'captions' for rewards). + n_repeat (int): Number of repetitions per original item. Must be > 0. + + Returns: + BatchData: A batch where each original item is repeated n_repeat times. + + Raises: + AssertionError: If n_repeat <= 0 or 'extra' is missing from the batch. + ValueError: If an unsupported type is encountered in model_inputs or extra. + """ + assert n_repeat > 0, "n_repeat must be a positive integer." + assert "extra" in batch, "extra field must be present in the batch for GRPO (captions for the)." + + repeated_inputs = {} + for k, v in batch["model_inputs"].items(): + if isinstance(v, Tensor): + repeated_inputs[k] = v.repeat_interleave(n_repeat, dim=0) + elif isinstance(v, list): + repeated_inputs[k] = [item for item in v for _ in range(n_repeat)] # type: ignore[reportUnknownVariableType] + elif isinstance(v, float): + repeated_inputs[k] = v + else: + raise ValueError(f"Unsupported type {type(v)} for key {k} in model_inputs.") + + repeated_extra = {} + for k, v in batch["extra"].items(): + if isinstance(v, Tensor): + repeated_extra[k] = v.repeat_interleave(n_repeat, dim=0) + elif isinstance(v, list): + repeated_extra[k] = [item for item in v for _ in range(n_repeat)] # type: ignore[reportUnknownVariableType] + elif v is None: + repeated_extra[k] = v + else: + raise ValueError(f"Unsupported type {type(v)} for key {k} in extra.") + + return {"model_inputs": cast(ModelInput, repeated_inputs), "extra": repeated_extra} + + def sample_model( + self, + diffuser: "Diffuser", + batch: BatchDataGRPO, + n_image_per_prompt: int, + image_resolution: tuple[int, int], + guidance_scale: float = 0, + ) -> tuple[BatchData, SamplingOutput]: + """ + Generate samples for GRPO by repeating the batch and calling the diffuser. + + The method: + - Ensures an input noise tensor is present (or samples one deterministically per prompt). + - Repeats the batch n_image_per_prompt times. + - Calls diffuser.generate in mini-batches of the original batch size. + - Concatenates SamplingOutput fields across repeats. + + Args: + diffuser (Diffuser): Diffusion/flow wrapper providing generate(). + batch (BatchDataGRPO): Original batch with model_inputs and extra (e.g., captions). + n_image_per_prompt (int): Number of samples to produce per prompt. + image_resolution (tuple[int, int]): Target (H, W) for sampling. + guidance_scale (float, optional): Guidance scale for generation. Defaults to 0. + + Returns: + tuple[BatchData, SamplingOutput]: + - repeated_batch: The batch repeated n_image_per_prompt times. + - samples: SamplingOutput with concatenated results for all repeats. + + Raises: + AssertionError: If no samples are produced. + """ + original_batch_size = batch["model_inputs"]["context"].shape[0] + + if diffuser.vision_tower: + if diffuser.vision_tower.compression_factor is not None: + data_shape = ( + original_batch_size, + diffuser.vision_tower.latent_channels, + image_resolution[0] // diffuser.vision_tower.compression_factor, + image_resolution[1] // diffuser.vision_tower.compression_factor, + ) + else: + assert diffuser.vision_tower.patch_size is not None, ( + "Patch size must be defined for vision tower if no compression factor" + ) + data_shape = ( + original_batch_size, + image_resolution[0] * image_resolution[1] // (diffuser.vision_tower.patch_size**2), + diffuser.vision_tower.latent_channels, + ) + else: + data_shape = ( + original_batch_size, + 3, + image_resolution[0], + image_resolution[1], + ) # only RGB images supported for now + + # We sample noise for each element of the batch if not provided + # same noises will be used for the sampling of same prompts + if "x" not in batch["model_inputs"]: + batch["model_inputs"]["x"] = torch.randn(data_shape, device=self.accelerator.device) + + repeated_batch = self.repeat_batch(batch, n_image_per_prompt) + + grpo_sampling: SamplingOutput | None = None + for mini_batch_idx in range(0, original_batch_size * n_image_per_prompt, original_batch_size): + model_inputs = cast( + ModelInput, + { + k: v[mini_batch_idx : mini_batch_idx + original_batch_size] + if isinstance(v, Tensor) or isinstance(v, list) + else v + for k, v in repeated_batch["model_inputs"].items() + }, + ) + + group_grpo_sampling = diffuser.generate( + model_inputs=model_inputs, + guidance_scale=guidance_scale, + return_intermediates=True, + return_latents=False, + ) + + if grpo_sampling is None: + grpo_sampling = group_grpo_sampling + else: + for k, v in group_grpo_sampling.items(): + grpo_sampling[k] = torch.cat((cast(Tensor, grpo_sampling[k]), cast(Tensor, v)), dim=0) + + assert grpo_sampling is not None, "No samples generated during GRPO sampling." + return repeated_batch, grpo_sampling + + def training_step( + self, + diffuser: "Diffuser", + optimizer: Optimizer, + batch: BatchDataGRPO, + tracker: AverageMeter, + reward_model: RewardModel, + n_image_per_prompt: int, + image_resolution: tuple[int, int], + scheduler: LRScheduler | None = None, + per_batch_scheduler: bool = False, + ema_denoiser: EMA | None = None, + guidance_scale: float = 0, + ): + """ + Perform a single GRPO training step. + + The method: + - Samples n_image_per_prompt images per prompt with diffuser.generate. + - Computes per-sample advantages via the reward_model. + - Computes GRPO loss via diffuser.compute_loss(grpo=True, grpo_args=...). + - Backpropagates and steps the optimizer (and scheduler if per-batch). + - Optionally updates EMA. + + Args: + diffuser (Diffuser): Diffusion/flow wrapper used for sampling and loss computation. + optimizer (Optimizer): Optimizer for model updates. + batch (BatchDataGRPO): Training batch with model_inputs and extra['captions']. + tracker (AverageMeter): Metric tracker updated with train/* losses. + reward_model (RewardModel): Callable that maps images and context to advantages. + n_image_per_prompt (int): Number of generated images per prompt. + image_resolution (tuple[int, int]): Sampling resolution (H, W). + scheduler (LRScheduler | None, optional): Learning rate scheduler. Defaults to None. + per_batch_scheduler (bool, optional): Step scheduler after each batch. Defaults to False. + ema_denoiser (EMA | None, optional): EMA wrapper for the denoiser. Defaults to None. + guidance_scale (float, optional): Guidance scale used during sampling. Defaults to 0. + + Returns: + None + """ + optimizer.zero_grad() + original_batch_size = batch["model_inputs"]["context"].shape[0] + repeated_batch, samples = self.sample_model( + diffuser, + batch, + n_image_per_prompt=n_image_per_prompt, + image_resolution=image_resolution, + guidance_scale=guidance_scale, + ) + + assert "extra" in repeated_batch and "captions" in repeated_batch["extra"], ( + "Captions are required in the extra field of the batch." + ) + advantages: Tensor = reward_model(images=samples["x"], context=repeated_batch["extra"]["captions"]) + for batch_idx in range(0, original_batch_size * n_image_per_prompt, original_batch_size): + batch_inputs = ModelInput( + **{ + k: cast(Tensor, v[batch_idx : batch_idx + original_batch_size]) # type: ignore + for k, v in repeated_batch["model_inputs"].items() + } + ) + batch_samples = SamplingOutput( + **{ + k: cast(Tensor, v[batch_idx : batch_idx + original_batch_size]) # type: ignore + for k, v in samples.items() + } + ) + batch_advantages = advantages[batch_idx : batch_idx + original_batch_size] + + losses = diffuser.compute_loss( + model_inputs=batch_inputs, + grpo=True, + grpo_args={ + "sampling": batch_samples, + "advantages": batch_advantages, + "kl_beta": self.kl_beta, + "eps": self.eps, + "timestep_fraction": self.timestep_fraction, + "guidance_scale": guidance_scale, + }, + ) + for key, loss in losses.items(): + tracker.update(loss.item(), key=f"train/{key}") + + self.accelerator.backward(loss) # type: ignore + optimizer.step() + if scheduler is not None and per_batch_scheduler: + scheduler.step() + if ema_denoiser is not None: + ema_denoiser.update() + + @torch.no_grad() # type: ignore + def validation_step( + self, + diffuser: "Diffuser", + batch: BatchDataGRPO, + tracker: AverageMeter, + reward_model: RewardModel, + n_image_per_prompt: int, + image_resolution: tuple[int, int], + guidance_scale: float = 4.0, + ): + """ + Perform a validation step for GRPO. + + The method: + - Generates n_image_per_prompt samples per prompt. + - Computes advantages with the reward model. + - Computes GRPO losses for logging (no grads). + + Args: + diffuser (Diffuser): Diffusion/flow wrapper used for sampling and loss computation. + batch (BatchDataGRPO): Validation batch with model_inputs and extra['captions']. + tracker (AverageMeter): Metric tracker updated with val/* losses. + reward_model (RewardModel): Callable that maps images and context to advantages. + n_image_per_prompt (int): Number of generated images per prompt. + image_resolution (tuple[int, int]): Sampling resolution (H, W). + guidance_scale (float, optional): Guidance scale used during sampling. Defaults to 4.0. + + Returns: + None + """ + original_batch_size = batch["model_inputs"]["context"].shape[0] + repeated_batch, samples = self.sample_model( + diffuser, + batch, + n_image_per_prompt=n_image_per_prompt, + image_resolution=image_resolution, + guidance_scale=guidance_scale, + ) + advantages: Tensor = reward_model(images=samples["x"], context=batch["extra"]["captions"]) + for batch_idx in range(0, original_batch_size * n_image_per_prompt, original_batch_size): + batch_inputs = ModelInput( + **{ + k: cast(Tensor, v[batch_idx : batch_idx + original_batch_size]) # type: ignore + for k, v in repeated_batch["model_inputs"].items() + } + ) + batch_samples = SamplingOutput( + **{ + k: cast(Tensor, v[batch_idx : batch_idx + original_batch_size]) # type: ignore + for k, v in samples.items() + } + ) + batch_advantages = advantages[batch_idx : batch_idx + original_batch_size] + + losses = diffuser.compute_loss( + model_inputs=batch_inputs, + grpo=True, + grpo_args={ + "sampling": batch_samples, + "advantages": batch_advantages, + "kl_beta": self.kl_beta, + "eps": self.eps, + "timestep_fraction": self.timestep_fraction, + "guidance_scale": guidance_scale, + }, + ) + + for key, loss in losses.items(): + tracker.update(loss.item(), key=f"val/{key}") + + def train( + self, + diffuser: "Diffuser", + reward_model: RewardModel, + optimizer: Optimizer, + train_dataloader: Iterable[BatchDataGRPO], + val_dataloader: Iterable[BatchDataGRPO] | None = None, + scheduler: LRScheduler | None = None, + per_batch_scheduler: bool = False, + log_validation_images: bool = True, + val_steps: int = 25, + optimizer_ckpt: str | None = None, + denoiser_ckpt: str | None = None, + ema_ckpt: str | None = None, + epoch_start: int = 0, + n_image_per_prompt: int = 16, + guidance_scale: float = 4.0, + image_resolution: tuple[int, int] = (512, 512), + ): + """ + Main training loop for GRPO alignment training. + + This orchestrates GRPO training with distributed/mixed precision support, gradient + accumulation, optional EMA, periodic validation, image logging, and checkpointing. + + Args: + diffuser (Diffuser): Diffusion/flow wrapper. Must have a context embedder in the denoiser. + reward_model (RewardModel): Callable producing per-sample advantages from images and context. + optimizer (Optimizer): Optimizer for parameter updates. + train_dataloader (Iterable[BatchDataGRPO]): Training data iterator yielding GRPO batches. + val_dataloader (Iterable[BatchDataGRPO] | None, optional): Validation iterator. Defaults to None. + scheduler (LRScheduler | None, optional): LR scheduler. Prepared with accelerator if provided. + per_batch_scheduler (bool, optional): Step scheduler after each batch instead of each epoch. + Defaults to False. + log_validation_images (bool, optional): Generate and log images during validation. Defaults to True. + val_steps (int, optional): Number of sampler steps for validation image generation. Defaults to 25. + optimizer_ckpt (str | None, optional): Path to optimizer state to resume. Defaults to None. + denoiser_ckpt (str | None, optional): Path to denoiser weights to load. Defaults to None. + ema_ckpt (str | None, optional): Path to EMA model weights to load. Defaults to None. + epoch_start (int, optional): Starting epoch index for resuming. Defaults to 0. + n_image_per_prompt (int, optional): Number of generated images per prompt for GRPO. Defaults to 16. + guidance_scale (float, optional): Guidance scale during sampling (train/val). Defaults to 4.0. + image_resolution (tuple[int, int], optional): Sampling resolution (H, W). Defaults to (512, 512). + + Returns: + None + + Notes: + - Freezes the denoiser's context_embedder parameters during GRPO (required for alignment). + - If EMA is enabled, validation can run on the EMA model and best checkpoints are saved + when validation loss improves. + - Uses Hugging Face Accelerate for preparation, device placement, mixed precision, and logging. + """ + assert diffuser.denoiser.context_embedder is not None, ( + "Alignment training requires a context embedder in the denoiser model." + ) + + if self.use_ema: + ema_denoiser = EMA( + diffuser.denoiser, + beta=self.ema_rate, + update_after_step=self.ema_update_after_step, + update_every=self.ema_update_every, + ).to(self.accelerator.device) + if ema_ckpt: + ema_denoiser.ema_model.load_state_dict(torch.load(ema_ckpt, weights_only=True)) # type: ignore + ema_denoiser = self.accelerator.prepare(ema_denoiser) # type: ignore + else: + ema_denoiser = None + + if denoiser_ckpt: + diffuser.denoiser.load_state_dict( + torch.load(denoiser_ckpt), # type: ignore + ) + + if optimizer_ckpt: + optimizer.load_state_dict( + torch.load(optimizer_ckpt, weights_only=False), # type: ignore + ) + + if diffuser.vision_tower: + diffuser.vision_tower = self.accelerator.prepare_model(diffuser.vision_tower) # type: ignore + + reward_model.set_n_image_per_prompt(n_image_per_prompt) + + diffuser.denoiser, train_dataloader, val_dataloader, optimizer, reward_model = self.accelerator.prepare( # type: ignore + diffuser.denoiser, train_dataloader, val_dataloader, optimizer, reward_model + ) + + if optimizer_ckpt: + device = self.accelerator.device + for state in optimizer.state.values(): # type: ignore + for k, v in state.items(): # type: ignore + if isinstance(v, Tensor): + state[k] = v.to(device) + + if scheduler is not None: + scheduler = self.accelerator.prepare_scheduler(scheduler) # type: ignore + + for param in diffuser.denoiser.context_embedder.parameters(): # type: ignore + param.requires_grad = False + + best_val_loss = float("inf") + + tracker = AverageMeter() + tq_epoch = tqdm( + range(epoch_start, self.n_epoch), disable=not self.accelerator.is_main_process, leave=False, position=0 + ) + logging.info("Begin training") + for epoch in tq_epoch: + diffuser.train() + tq_epoch.set_description(f"Epoch {epoch + 1}/{self.n_epoch}") + + tq_batch = tqdm(train_dataloader, disable=not self.accelerator.is_main_process, leave=False) # type: ignore + for batch in tq_batch: + with self.accelerator.accumulate(diffuser.denoiser): # type: ignore + with self.accelerator.autocast(): + self.training_step( + diffuser=diffuser, + optimizer=optimizer, # type: ignore + batch=batch, + tracker=tracker, + reward_model=reward_model, # type: ignore + n_image_per_prompt=n_image_per_prompt, + image_resolution=image_resolution, + scheduler=scheduler, + per_batch_scheduler=per_batch_scheduler, + ema_denoiser=ema_denoiser, # type: ignore + guidance_scale=guidance_scale, + ) + tq_batch.set_description( + f"Loss: {sum(v for k, v in tracker.avg.items() if k.startswith('train/')):.4f}" + ) + + if scheduler is not None and not per_batch_scheduler: + scheduler.step() + + for key, value in tracker.avg.items(): + if key.startswith("train/"): + gathered_loss: Tensor = self.accelerator.gather( # type: ignore + torch.tensor(value, device=self.accelerator.device) + ) + self.accelerator.log( # type: ignore + {key: gathered_loss.mean().item()}, step=epoch + 1 + ) + tracker.reset() + + if val_dataloader is not None: + diffuser.eval() + original_model: Denoiser = diffuser.denoiser # type: ignore + if ema_denoiser is not None: + diffuser.denoiser = ema_denoiser.ema_model.eval() # type: ignore + + tq_val_batch = tqdm( + val_dataloader, # type: ignore + disable=not self.accelerator.is_main_process, + leave=False, + position=1, + ) + for val_batch in tq_val_batch: + with self.accelerator.autocast(): + self.validation_step( + diffuser=diffuser, + batch=val_batch, + tracker=tracker, + reward_model=reward_model, # type: ignore + n_image_per_prompt=n_image_per_prompt, + image_resolution=image_resolution, + guidance_scale=guidance_scale, + ) + tq_val_batch.set_description( + f"Val Loss: {sum(v for k, v in tracker.avg.items() if k.startswith('val/')):.4f}" + ) + + total_loss = 0 + for key, value in tracker.avg.items(): + if key.startswith("val/"): + gathered_loss: Tensor = self.accelerator.gather( # type: ignore + torch.tensor(value, device=self.accelerator.device) + ) + self.accelerator.log( # type: ignore + {key: gathered_loss.mean().item()}, step=epoch + 1 + ) + total_loss += gathered_loss.mean().item() + + if log_validation_images: + logging.info("creating validation images") + if self.accelerator.is_main_process: + with self.accelerator.autocast(): + self.log_images( + diffuser, + val_dataloader, # type: ignore + epoch, + val_steps, + guidance_scale=4, + ) + + if ema_denoiser is not None: + diffuser.denoiser = original_model + + if total_loss < best_val_loss: + best_val_loss = total_loss + 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