From 4a7c6f90ef40abc7b0b7008780f151c54ea9725f Mon Sep 17 00:00:00 2001 From: LouisRouss <61450789+LouisRouss@users.noreply.github.com> Date: Sat, 9 Aug 2025 17:50:09 +0200 Subject: [PATCH 1/6] correct some logics for text to image training + clean code * add args in train to resume training + correct latent scale * properly set latent scale * add a safe check assertion for latent scale * fix double flow block * correctly add guidance * add save function for losses * change saving extensions * correct configs * clean datasets implem * clean modelization * correct Unet cross attn if context provided * improve docstring * clean embedders * clean networks for repa utils * refine docstring networks utils * update drop proba by default and use by default cfg when logging images * set latent scale automatically * update init files * fix docstring --- configs/dataset/imagenet_repa.yaml | 4 +- configs/model/{mmdit.yaml => dit.yaml} | 0 configs/train_cifar10_flow_matching.yaml | 2 +- .../train_imagenet_flow_matching_repa.yaml | 12 +- examples/train_repa.py | 41 +- src/diffulab/__init__.py | 26 ++ src/diffulab/datasets/__init__.py | 4 +- src/diffulab/datasets/base.py | 20 +- src/diffulab/datasets/cifar10.py | 35 +- src/diffulab/datasets/imagenet.py | 39 +- src/diffulab/datasets/mnist.py | 4 +- src/diffulab/diffuse/diffuser.py | 51 +-- .../diffuse/modelizations/diffusion.py | 8 +- src/diffulab/diffuse/modelizations/flow.py | 8 +- .../modelizations/gaussian_diffusion.py | 2 +- src/diffulab/networks/__init__.py | 14 +- src/diffulab/networks/denoisers/common.py | 2 + src/diffulab/networks/denoisers/mmdit.py | 4 +- src/diffulab/networks/denoisers/unet.py | 369 ++++++++++++++---- src/diffulab/networks/embedders/common.py | 22 +- src/diffulab/networks/embedders/text.py | 157 +++++++- src/diffulab/networks/repa/__init__.py | 9 + src/diffulab/networks/repa/common.py | 7 +- src/diffulab/networks/repa/dinov2.py | 7 +- .../networks/repa/perceiver_resampler.py | 96 ++++- src/diffulab/networks/utils/nn.py | 105 +++-- src/diffulab/networks/utils/utils.py | 14 +- .../networks/vision_towers/__init__.py | 2 + src/diffulab/networks/vision_towers/dc_ae.py | 10 +- src/diffulab/training/__init__.py | 3 +- src/diffulab/training/losses/common.py | 14 + src/diffulab/training/losses/repa.py | 26 ++ src/diffulab/training/trainer.py | 64 ++- 33 files changed, 919 insertions(+), 262 deletions(-) rename configs/model/{mmdit.yaml => dit.yaml} (100%) diff --git a/configs/dataset/imagenet_repa.yaml b/configs/dataset/imagenet_repa.yaml index dd54805..e4622b3 100644 --- a/configs/dataset/imagenet_repa.yaml +++ b/configs/dataset/imagenet_repa.yaml @@ -3,12 +3,12 @@ train: _target_: diffulab.datasets.ImageNetLatentREPA data_path: "data/imagenet" local: true - batch_size: 256 + batch_size: 128 split: "train" val: _target_: diffulab.datasets.ImageNetLatentREPA data_path: "data/imagenet" local: true - batch_size: 256 + batch_size: 128 split: "val" diff --git a/configs/model/mmdit.yaml b/configs/model/dit.yaml similarity index 100% rename from configs/model/mmdit.yaml rename to configs/model/dit.yaml diff --git a/configs/train_cifar10_flow_matching.yaml b/configs/train_cifar10_flow_matching.yaml index bc2ab6b..9289db3 100644 --- a/configs/train_cifar10_flow_matching.yaml +++ b/configs/train_cifar10_flow_matching.yaml @@ -1,7 +1,7 @@ # configs/train_cifar10_flow_matching.yaml # @package _global_ defaults: - - model: mmdit + - model: dit - diffuser: rectified_flow - trainer: default - dataset: cifar10 diff --git a/configs/train_imagenet_flow_matching_repa.yaml b/configs/train_imagenet_flow_matching_repa.yaml index 9ebcbaf..e78c91d 100644 --- a/configs/train_imagenet_flow_matching_repa.yaml +++ b/configs/train_imagenet_flow_matching_repa.yaml @@ -1,7 +1,7 @@ # configs/train_cifar10_flow_matching.yaml # @package _global_ defaults: - - model: mmdit + - model: dit - diffuser: rectified_flow - trainer: default - dataset: imagenet_repa @@ -13,8 +13,9 @@ defaults: # Override specific settings trainer: project_name: imagenet_repa_flow_matching - n_epoch: 200 + n_epoch: 150 precision_type: "bf16" + p_classifier_free_guidance: 0.2 model: input_channels: 32 @@ -23,17 +24,14 @@ model: hidden_dim: 768 embedding_dim: 256 num_heads: 12 - mlp_ratio: 1 + mlp_ratio: 4 patch_size: 1 depth: 12 n_classes: 1000 classifier_free: true -diffuser: - latent_scale: 0.18215 - dataloader: - batch_size: 256 + batch_size: 128 # Hydra configuration hydra: diff --git a/examples/train_repa.py b/examples/train_repa.py index 2d63346..ee860fa 100644 --- a/examples/train_repa.py +++ b/examples/train_repa.py @@ -17,23 +17,6 @@ def train(cfg: DictConfig): train_dataset = instantiate(cfg.dataset.train) val_dataset = instantiate(cfg.dataset.val) - 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), - ) - # Model denoiser = instantiate(cfg.model) @@ -54,6 +37,26 @@ def count_parameters(model: torch.nn.Module) -> int: ) vision_tower = instantiate(cfg.vision_tower) + train_dataset.set_latent_scale(vision_tower.latent_scale) + val_dataset.set_latent_scale(vision_tower.latent_scale) + + 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), + ) + # Diffuser diffuser = Diffuser( denoiser=denoiser, @@ -81,6 +84,9 @@ 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] + init_kwargs={ + "wandb": cfg.trainer.get("wandb", {}), + }, ) trainer.train( @@ -90,6 +96,7 @@ def count_parameters(model: torch.nn.Module) -> int: val_dataloader=val_loader, log_validation_images=cfg.trainer.log_validation_images, val_steps=cfg.trainer.get("val_steps", 50), + p_classifier_free_guidance=cfg.trainer.get("p_classifier_free_guidance", 0), ) diff --git a/src/diffulab/__init__.py b/src/diffulab/__init__.py index e69de29..67b4ea5 100644 --- a/src/diffulab/__init__.py +++ b/src/diffulab/__init__.py @@ -0,0 +1,26 @@ +from .datasets import BaseDataset, CIFAR10Dataset, ImageNetLatentREPA, 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 + +__all__ = [ + "BaseDataset", + "CIFAR10Dataset", + "ImageNetLatentREPA", + "MNISTDataset", + "Diffuser", + "Flow", + "GaussianDiffusion", + "DCAE", + "REPA", + "Denoiser", + "DinoV2", + "MMDiT", + "PerceiverResampler", + "SD3TextEmbedder", + "UNetModel", + "VisionTower", + "LossFunction", + "RepaLoss", + "Trainer", +] diff --git a/src/diffulab/datasets/__init__.py b/src/diffulab/datasets/__init__.py index ed1d873..35fa822 100644 --- a/src/diffulab/datasets/__init__.py +++ b/src/diffulab/datasets/__init__.py @@ -1,6 +1,6 @@ -from .base import DiffusionDataset +from .base import BaseDataset from .cifar10 import CIFAR10Dataset from .imagenet import ImageNetLatentREPA from .mnist import MNISTDataset -__all__ = ["DiffusionDataset", "MNISTDataset", "CIFAR10Dataset", "ImageNetLatentREPA"] +__all__ = ["BaseDataset", "MNISTDataset", "CIFAR10Dataset", "ImageNetLatentREPA"] diff --git a/src/diffulab/datasets/base.py b/src/diffulab/datasets/base.py index b164a43..2146243 100644 --- a/src/diffulab/datasets/base.py +++ b/src/diffulab/datasets/base.py @@ -15,8 +15,8 @@ class BatchData(TypedDict, total=False): extra: NotRequired[dict[str, Tensor | None]] -class DiffusionDataset(Dataset[BatchData], ABC): - """Base class for datasets used in diffusion models. +class BaseDataset(Dataset[BatchData], ABC): + """Base class for basic datasets used in diffusion models. This abstract class defines the common interface that all diffusion datasets should implement, ensuring consistency across different data sources. @@ -32,7 +32,7 @@ def load_data(self) -> tuple[NDArray[np.uint8 | np.float32], NDArray[np.int64]]: """Load and preprocess the dataset images and labels. Returns: - Tuple containing (images, labels) arrays + tuple: tuple containing (images, labels) arrays """ pass @@ -44,12 +44,20 @@ def preprocess_image(self, image: NDArray[np.uint8 | np.float32]) -> NDArray[np. image: The raw image data Returns: - Preprocessed image data + NDArray: Preprocessed image data """ pass def __len__(self) -> int: - """Return the number of samples in the dataset.""" + """ + Return the number of samples in the dataset. + + Returns: + int: Number of samples in the dataset + Raises: + ValueError: If the dataset has not been initialized properly (e.g., images are None) + """ + if self.images is None: raise ValueError("Dataset has not been initialized properly. Images are None.") return len(self.images) @@ -61,7 +69,7 @@ def __getitem__(self, idx: int) -> BatchData: idx: Index of the sample to get Returns: - Dictionary containing 'x' (image tensor) and 'y' (label tensor) + BatchData: Dictionary containing 'model_inputs' and 'extra' (eventual additional data for loss functions) """ if self.images is None or self.labels is None: raise ValueError("Dataset has not been initialized properly. Images or labels are None.") diff --git a/src/diffulab/datasets/cifar10.py b/src/diffulab/datasets/cifar10.py index 7da4149..0fd5cab 100644 --- a/src/diffulab/datasets/cifar10.py +++ b/src/diffulab/datasets/cifar10.py @@ -1,22 +1,22 @@ import pickle from pathlib import Path -from typing import List import numpy as np from numpy.typing import NDArray -from diffulab.datasets.base import DiffusionDataset +from diffulab.datasets.base import BaseDataset -class CIFAR10Dataset(DiffusionDataset): +class CIFAR10Dataset(BaseDataset): """CIFAR10 dataset for diffusion models.""" def __init__( self, data_path: str, - batches_to_load: List[str] = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"], + batches_to_load: list[str] = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"], ): - """Initialize the CIFAR10 dataset. + """ + Initialize the CIFAR10 dataset. Args: data_path: Path to the CIFAR10 data directory @@ -28,7 +28,12 @@ def __init__( self.images, self.labels = self.load_data() def load_data(self) -> tuple[NDArray[np.uint8], NDArray[np.int64]]: - """Load CIFAR10 data from files.""" + """ + Load CIFAR10 data from files. + + Returns: + tuple: Tuple containing (images, labels) arrays + """ images: list[NDArray[np.uint8]] = [] labels: list[NDArray[np.int64]] = [] @@ -40,7 +45,14 @@ def load_data(self) -> tuple[NDArray[np.uint8], NDArray[np.int64]]: return np.concatenate(images, axis=0), np.concatenate(labels, axis=0) def _load_cifar10_batch(self, file: Path) -> tuple[NDArray[np.uint8], NDArray[np.int64]]: - """Load a single CIFAR10 batch file.""" + """ + Load a single CIFAR10 batch file. + + Args: + file: Path to the CIFAR10 batch file + Returns: + tuple: Tuple containing (images, labels) arrays + """ with open(file, "rb") as f: batch = pickle.load(f, encoding="latin1") @@ -56,7 +68,14 @@ def _load_cifar10_batch(self, file: Path) -> tuple[NDArray[np.uint8], NDArray[np return images, labels def preprocess_image(self, image: NDArray[np.uint8 | np.float32]) -> NDArray[np.float32]: - """Preprocess CIFAR10 image: normalize and transpose to (C, H, W).""" + """ + Preprocess CIFAR10 image: normalize and transpose to (C, H, W). + + Args: + image: The raw image data + Returns: + NDArray: Preprocessed image data in shape (C, H, W) + """ # Convert to float and normalize to [-1, 1] normalized = (image.astype(np.float32) / 255.0 - 0.5) / 0.5 diff --git a/src/diffulab/datasets/imagenet.py b/src/diffulab/datasets/imagenet.py index 5bd3d9d..d1c4291 100644 --- a/src/diffulab/datasets/imagenet.py +++ b/src/diffulab/datasets/imagenet.py @@ -16,17 +16,16 @@ def __init__( data_path: str, local: bool = True, batch_size: int = 64, - latent_scale: float = 0.18215, split: str = "train", ) -> None: """Initialize the MNIST dataset. 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) + 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) """ super().__init__() self.data_path = Path(data_path) @@ -36,9 +35,13 @@ def __init__( batch_size=batch_size, split=split, ) - self.latent_scale = latent_scale + self.latent_scale: float | None = None self.transform = transforms.ToTensor() + def set_latent_scale(self, scale: float) -> None: + """Set the latent scale for the dataset.""" + self.latent_scale = scale + def __len__(self) -> int: """Return the number of samples in the dataset.""" return len(self.dataset) @@ -50,20 +53,11 @@ def __getitem__(self, idx: int) -> BatchData: Returns: BatchData: A dictionary containing model inputs and extra features """ - + 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" - 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" - x0 = self.transform(sample["image"]) - else: - assert "dst_features" in sample, "Batch must contain either 'dst_features' or 'image' key" - dst_features = torch.tensor(sample["dst_features"], dtype=torch.float32) - latent = torch.tensor(sample["latent"], dtype=torch.float32) y = torch.tensor(sample["label"], dtype=torch.long) @@ -72,9 +66,14 @@ def __getitem__(self, idx: int) -> BatchData: "extra": {}, } - if dst_features is not None: - batch_data["extra"]["dst_features"] = dst_features - if x0 is not None: + 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" + x0 = self.transform(sample["image"]) batch_data["extra"]["x0"] = x0 + else: + dst_features = torch.tensor(sample["dst_features"], dtype=torch.float32) + batch_data["extra"]["dst_features"] = dst_features return batch_data diff --git a/src/diffulab/datasets/mnist.py b/src/diffulab/datasets/mnist.py index 57f5d33..ebec548 100644 --- a/src/diffulab/datasets/mnist.py +++ b/src/diffulab/datasets/mnist.py @@ -5,10 +5,10 @@ import numpy as np from numpy.typing import NDArray -from diffulab.datasets.base import DiffusionDataset +from diffulab.datasets.base import BaseDataset -class MNISTDataset(DiffusionDataset): +class MNISTDataset(BaseDataset): """MNIST dataset for diffusion models.""" def __init__(self, data_path: str, train: bool = True): diff --git a/src/diffulab/diffuse/diffuser.py b/src/diffulab/diffuse/diffuser.py index 3693f67..c715cc8 100644 --- a/src/diffulab/diffuse/diffuser.py +++ b/src/diffulab/diffuse/diffuser.py @@ -16,29 +16,29 @@ class Diffuser: This class provides a unified interface to work with different diffusion model architectures by encapsulating the specific diffusion implementation details. Args: - - denoiser (Denoiser): The neural network model used for denoising. - - sampling_method (str, optional): Method used for sampling. - - model_type (str, optional): Type of diffusion model to use. Defaults to "rectified_flow". + denoiser (Denoiser): The neural network model used for denoising. + sampling_method (str, optional): Method used for sampling. + model_type (str, optional): Type of diffusion model to use. Defaults to "rectified_flow". Available options: - "rectified_flow": Flow-based diffusion model. - "gaussian_diffusion": Gaussian diffusion model. - - n_steps (int, optional): Number of diffusion steps. Defaults to 1000. - - extra_args (dict[str, Any], optional): Additional arguments to pass to the diffusion model. Defaults to {}. + n_steps (int, optional): Number of diffusion steps. Defaults to 1000. + extra_args (dict[str, Any], optional): Additional arguments to pass to the diffusion model. 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. - - diffusion (Diffusion): The diffusion model implementation. + model_type (str): Type of diffusion model used. + denoiser (Denoiser): The neural network model used for denoising. + n_steps (int): Number of diffusion steps. + diffusion (Diffusion): The diffusion model implementation. Methods: - - __init__: Initialize the diffuser with specified parameters. - - eval: Set the denoiser model to evaluation mode. - - train: Set the denoiser model to training mode. - - draw_timesteps: Draw a batch of timesteps for diffusion. - - compute_loss: Calculate the loss for the diffusion model. - - set_steps: Update the number of diffusion steps and related parameters. - - generate: Generate new samples using the diffusion model. + __init__: Initialize the diffuser with specified parameters. + eval: Set the denoiser model to evaluation mode. + train: Set the denoiser model to training mode. + draw_timesteps: Draw a batch of timesteps for diffusion. + compute_loss: Calculate the loss for the diffusion model. + set_steps: Update the number of diffusion steps and related parameters. + generate: Generate new samples using the diffusion model. """ model_registry: dict[str, type[Diffusion]] = {"rectified_flow": Flow, "gaussian_diffusion": GaussianDiffusion} @@ -105,11 +105,12 @@ def compute_loss( This method serves as a bridge between the Diffuser class and the underlying diffusion implementation by forwarding the loss computation to the diffusion model. Args: - - model_inputs (ModelInput): A dictionary containing the model inputs, + model_inputs (ModelInput): A dictionary containing the model inputs, including the data tensor keyed as 'x' and any conditional information. - - timesteps (Tensor): A tensor of timesteps for the batch. - - noise (Tensor | None, optional): Pre-defined noise to add to the input. + 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. Returns: dict[str, Tensor]: A dictionary containing the loss value and any additional losses """ @@ -140,7 +141,7 @@ def generate( data_shape: tuple[int, ...], model_inputs: ModelInput, use_tqdm: bool = True, - clamp_x: bool = True, + clamp_x: bool = False, guidance_scale: float = 0, **kwargs: dict[str, Any], ) -> Tensor: @@ -150,13 +151,13 @@ def generate( 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). + 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, + 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. - - 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 True. - - guidance_scale (float, optional): Scale for classifier or classifier-free guidance. + 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. + 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. diff --git a/src/diffulab/diffuse/modelizations/diffusion.py b/src/diffulab/diffuse/modelizations/diffusion.py index c26e7d4..48b74b8 100644 --- a/src/diffulab/diffuse/modelizations/diffusion.py +++ b/src/diffulab/diffuse/modelizations/diffusion.py @@ -116,6 +116,10 @@ def compute_loss( which to compute the loss for each example in the batch. noise (Tensor | None, optional): Pre-defined 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 + alongside the main loss. Defaults to an empty list. + extra_args (dict[str, Any], optional): Additional arguments for the loss computation. + Defaults to an empty dictionary. Returns: dict[str, Tensor]: A dictionary containing the loss value and any additional losses """ @@ -151,7 +155,7 @@ def denoise( data_shape: tuple[int, ...], model_inputs: ModelInput, use_tqdm: bool = True, - clamp_x: bool = True, + clamp_x: bool = False, guidance_scale: float = 0, ) -> Tensor: """ @@ -166,7 +170,7 @@ def denoise( 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 True. + Defaults to False. guidance_scale (float, optional): Scale factor for classifier or classifier-free guidance. Values greater than 0 enable guidance. Defaults to 0. Returns: diff --git a/src/diffulab/diffuse/modelizations/flow.py b/src/diffulab/diffuse/modelizations/flow.py index 3e467a2..c7ef48c 100644 --- a/src/diffulab/diffuse/modelizations/flow.py +++ b/src/diffulab/diffuse/modelizations/flow.py @@ -9,8 +9,6 @@ from diffulab.training.losses.common import LossFunction -# replace function at, bt etc ... By actually precomputing the values and storing them for every timestep -# more efficient class Flow(Diffusion): """ Flow-based diffusion model. @@ -224,6 +222,10 @@ def compute_loss( in the range [0, 1]. 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 + alongside the main loss. Defaults to an empty list. + extra_args (dict[str, Any], optional): Additional arguments for the loss computation. + Defaults to an empty dictionary. Returns: dict[str, Tensor]: A dictionary containing the loss value and any additional losses Note: @@ -235,11 +237,13 @@ def compute_loss( x_0 = model_inputs["x"].clone() model_inputs["x"], noise = self.add_noise(model_inputs["x"], timesteps, noise) prediction: ModelOutput = model(**model_inputs, timesteps=timesteps) + # Compute flow matching loss losses = ((noise - x_0) - prediction["x"]) ** 2 losses = losses.reshape(losses.shape[0], -1).mean(dim=-1) loss = losses.mean() loss_dict = {"loss": loss} + # Compute extra losses if any for extra_loss in extra_losses: e_loss = cast(Tensor, extra_loss(**extra_args)) diff --git a/src/diffulab/diffuse/modelizations/gaussian_diffusion.py b/src/diffulab/diffuse/modelizations/gaussian_diffusion.py index a6753c3..1ee8fd6 100644 --- a/src/diffulab/diffuse/modelizations/gaussian_diffusion.py +++ b/src/diffulab/diffuse/modelizations/gaussian_diffusion.py @@ -582,7 +582,7 @@ def denoise( data_shape: tuple[int, ...], model_inputs: ModelInput, use_tqdm: bool = True, - clamp_x: bool = True, + clamp_x: bool = False, guidance_scale: float = 0, classifier_free: bool = True, classifier: Callable[[Tensor, Tensor], Tensor] | None = None, diff --git a/src/diffulab/networks/__init__.py b/src/diffulab/networks/__init__.py index 406fb9c..c628f62 100644 --- a/src/diffulab/networks/__init__.py +++ b/src/diffulab/networks/__init__.py @@ -1,4 +1,16 @@ from .denoisers import Denoiser, MMDiT, UNetModel from .embedders import SD3TextEmbedder +from .repa import REPA, DinoV2, PerceiverResampler +from .vision_towers import DCAE, VisionTower -__all__ = ["Denoiser", "UNetModel", "MMDiT", "SD3TextEmbedder"] +__all__ = [ + "Denoiser", + "UNetModel", + "MMDiT", + "SD3TextEmbedder", + "REPA", + "DinoV2", + "PerceiverResampler", + "DCAE", + "VisionTower", +] diff --git a/src/diffulab/networks/denoisers/common.py b/src/diffulab/networks/denoisers/common.py index 8375e24..c5dd63b 100644 --- a/src/diffulab/networks/denoisers/common.py +++ b/src/diffulab/networks/denoisers/common.py @@ -19,6 +19,8 @@ class ModelOutput(TypedDict, total=False): class Denoiser(nn.Module, ABC): + classifier_free: bool + def __init__(self): super().__init__() # type: ignore diff --git a/src/diffulab/networks/denoisers/mmdit.py b/src/diffulab/networks/denoisers/mmdit.py index 286eadf..29a6039 100644 --- a/src/diffulab/networks/denoisers/mmdit.py +++ b/src/diffulab/networks/denoisers/mmdit.py @@ -380,7 +380,7 @@ def forward( modulated_context = context + modulated_context * modulation_context.gamma modulated_input = ( - modulated_input + input + self.mlp_input( modulate( self.input_norm_2(modulated_input), scale=modulation_input.delta, shift=modulation_input.epsilon @@ -389,7 +389,7 @@ def forward( * modulation_input.zeta ) modulated_context = ( - modulated_context + context + self.mlp_context( modulate( self.context_norm_2(modulated_context), diff --git a/src/diffulab/networks/denoisers/unet.py b/src/diffulab/networks/denoisers/unet.py index 1595d3f..bb29960 100644 --- a/src/diffulab/networks/denoisers/unet.py +++ b/src/diffulab/networks/denoisers/unet.py @@ -24,7 +24,9 @@ class TimestepBlock(nn.Module): """ @abstractmethod - def forward(self, x: Tensor, emb: Tensor) -> Tensor: + def forward( + self, x: Float[Tensor, "batch_size channels height width"], emb: Float[Tensor, "batch_size emb_channels"] + ) -> Float[Tensor, "batch_size channels height width"]: """ Apply the module to `x` given `emb` timestep embeddings. """ @@ -36,7 +38,11 @@ class ContextBlock(nn.Module): """ @abstractmethod - def forward(self, x: Tensor, context: Tensor) -> Tensor: + def forward( + self, + x: Float[Tensor, "batch_size channels height width"], + context: Float[Tensor, "batch_size context_channels context_length"], + ) -> Float[Tensor, "batch_size channels height width"]: """ Apply the module to `x` given `context` embeddings. """ @@ -48,7 +54,12 @@ class EmbedSequential(nn.Sequential, TimestepBlock, ContextBlock): # type: igno support it as an extra input. """ - def forward(self, x: Tensor, emb: Tensor, context: Tensor | None = None) -> Tensor: # type:ignore + def forward( # type:ignore + self, + 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, + ) -> Float[Tensor, "batch_size channels height width"]: for layer in self: if isinstance(layer, TimestepBlock) and isinstance(layer, ContextBlock): x = layer(x, emb, context) @@ -63,19 +74,45 @@ def forward(self, x: Tensor, emb: Tensor, context: Tensor | None = None) -> Tens class ResBlock(TimestepBlock): """ - From https://github.com/openai/guided-diffusion under MIT license as of 2024-18-08 - - A residual block that can optionally change the number of channels. - :param channels: the number of input channels. - :param emb_channels: the number of timestep embedding channels. - :param dropout: the rate of dropout. - :param out_channels: if specified, the number of out channels. - :param use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - :param use_checkpoint: if True, use gradient checkpointing on this module. - :param up: if True, use this block for upsampling. - :param down: if True, use this block for downsampling. + Residual block used in the U-Net denoiser, optionally performing up/down sampling + and channel projection. Adapted (with modifications) from OpenAI's guided-diffusion + implementation (MIT License). + + This block: + * Applies GroupNorm + SiLU + Conv2d to the input. + * Optionally upsamples or downsamples the residual and main branch in a synchronized way. + * Incorporates a timestep (or generic conditioning) embedding through an MLP that produces + either additive bias or (scale, shift) parameters for Feature-wise Linear Modulation + (FiLM-like) when use_scale_shift_norm is True. + * Applies dropout and a zero-initialized output convolution (zero_module) to stabilize training. + * Adds a learned (or identity) skip connection; can use a 1x1 or 3x3 projection if the + channel count changes (configurable via use_conv). + * Supports gradient checkpointing to reduce memory usage at the cost of extra compute. + + Args: + channels (int): Number of input feature channels. + emb_channels (int): Dimensionality of the conditioning (timestep) embedding provided to forward. + dropout (float): Dropout probability applied before the final convolution in the main branch. + out_channels (int | None, optional): Number of output channels. Defaults to channels if None. + use_conv (bool, optional): If True and out_channels differs from channels, use a 3x3 convolution in the + skip path; otherwise a 1x1 convolution is used. + use_scale_shift_norm (bool, optional): If True, interpret the embedding MLP output as (scale, shift) and apply them + after normalization (FiLM style). If False, embedding is added directly to the + feature map (broadcast). + use_checkpoint (bool, optional): If True, wrap the forward pass in torch.utils.checkpoint to trade compute for + reduced memory. + up (bool, optional): If True, perform learned nearest-neighbor-like upsampling on both residual and + skip paths before the main convolution. + down (bool, optional): If True, perform strided (anti-aliased) downsampling on both residual and skip + paths before the main convolution. + Notes: + * zero_module on the final convolution helps the network start as an identity, + often stabilizing diffusion model training. + * use_scale_shift_norm implements the "scale-shift" variant seen in certain + diffusion implementations (similar to adaptive GroupNorm). + + Raises: + RuntimeError: If both up and down are True (mutually exclusive expectation). """ def __init__( @@ -137,16 +174,40 @@ def __init__( else: self.skip_connection = nn.Conv2d(channels, self.out_channels, 1) - def forward(self, x: Tensor, emb: Tensor) -> Tensor: + def forward( + self, x: Float[Tensor, "batch_size channels height width"], emb: Float[Tensor, "batch_size emb_channels"] + ) -> Float[Tensor, "batch_size channels height width"]: """ - Apply the block to a Tensor, conditioned on a timestep embedding. - :param x: an [N x C x ...] Tensor of features. - :param emb: an [N x emb_channels] Tensor of timestep embeddings. - :return: an [N x C x ...] Tensor of outputs. + Apply the module to an input feature map, conditioned on a (timestep) embedding. + + This method optionally uses gradient checkpointing (torch.utils.checkpoint.checkpoint) + to reduce memory consumption during the backward pass if self.use_checkpoint is True. + + Args: + x (torch.Tensor): Input feature tensor of shape (batch_size, channels, height, width). + Represents the current latent / feature map to be processed. + emb (torch.Tensor): Conditioning embedding tensor of shape (batch_size, emb_channels), + typically a timestep or diffusion/noise level embedding already projected to the + expected dimensionality. + + Returns: + torch.Tensor: Output feature tensor of shape (batch_size, channels, height, width), + transformed by the underlying block while incorporating the conditioning embedding. + + Raises: + RuntimeError: If checkpointing fails due to incompatible inputs. + ValueError: If input dimensionalities do not match model expectations. + + Notes: + - Gradient checkpointing trades extra forward compute for reduced activation memory. + - The underlying computation is delegated to self._forward(x, emb). + - The channel dimension of x is preserved. """ return checkpoint(self._forward, (x, emb), self.parameters(), self.use_checkpoint) - def _forward(self, x: Tensor, emb: Tensor) -> Tensor: + def _forward( + self, x: Float[Tensor, "batch_size channels height width"], emb: Float[Tensor, "batch_size emb_channels"] + ) -> Float[Tensor, "batch_size channels height width"]: if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) @@ -173,6 +234,7 @@ class AttentionBlock(ContextBlock): def __init__( self, channels: int, + context_channels: int | None = None, num_heads: int = 8, inner_channels: int = -1, dropout: float = 0.0, @@ -185,22 +247,28 @@ def __init__( self.use_checkpoint = use_checkpoint 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.scale = self.dim_head**-0.5 - self.norm = norm(channels) + self.norm_x = norm(self.channels) + self.norm_context = norm(self.context_channels) self.attend = nn.Softmax(dim=-1) self.dropout = nn.Dropout(dropout) - self.to_q = nn.Conv1d(channels, self.inner_channels, 1, bias=q_bias) - self.to_kv = nn.Conv1d(channels, self.inner_channels * 2, 1, bias=kv_bias) + self.to_q = nn.Conv1d(self.channels, self.inner_channels, 1, bias=q_bias) + self.to_kv = nn.Conv1d(self.context_channels, self.inner_channels * 2, 1, bias=kv_bias) self.to_out = nn.Sequential(nn.Conv1d(self.inner_channels, self.channels, 1), nn.Dropout(dropout)) - def forward(self, x: Tensor, context: Tensor | None = None) -> Tensor: + def forward( + self, + x: Float[Tensor, "batch_size channels height width"], + context: Float[Tensor, "batch_size context_channels context_length"] | None = None, + ) -> Float[Tensor, "batch_size channels height width"]: return checkpoint( self._forward, (x, context), @@ -208,56 +276,160 @@ def forward(self, x: Tensor, context: Tensor | None = None) -> Tensor: True if self.use_checkpoint else False, ) - def _forward(self, x: Tensor, context: Tensor | None = None) -> Tensor: + def _forward( + self, + x: Float[Tensor, "batch_size channels height width"], + context: Float[Tensor, "batch_size context_channels context_length"] | None = None, + ) -> Float[Tensor, "batch_size channels height width"]: b, c, *spatial = x.shape x = x.reshape(b, c, -1) - context = default(context, self.norm(x)) + context = default(context, x) - qkv = torch.cat((self.to_q(self.norm(x)), *self.to_kv(context).chunk(2, dim=1)), dim=1) - length = qkv.shape[-1] - q, k, v = qkv.chunk(3, dim=1) + 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] - dots = torch.einsum( - "bct,bcs->bts", - (q * self.scale).view(b * self.num_heads, self.dim_head, length), - (k * self.scale).view(b * self.num_heads, self.dim_head, length), - ) + # 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) + + 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) - out = torch.einsum("bts,bcs->bct", attn, v.reshape(b * self.num_heads, self.dim_head, length)) - out = out.reshape(b, -1, length) + out = torch.einsum("bts,bcs->bct", attn, v) + out = out.reshape(b, -1, x_len) # (b, inner_channels, x_len) out = self.to_out(out) return (x + out).reshape(b, c, *spatial) class UNetModel(Denoiser): """ - Inspired by https://github.com/openai/guided-diffusion under MIT license as of 2024-18-08 - - The full UNet model with attention and timestep embedding. - :param in_channels: channels in the input Tensor - :param model_channels: base channel count for the model. - :param out_channels: channels in the output Tensor. - :param num_res_blocks: number of residual blocks per downsample. - :param attention_resolutions: a collection of downsample rates at which - attention will take place. May be a set, list, or tuple. - For example, if this contains 4, then at 4x downsampling, attention - will be used. - :param dropout: the dropout probability. - :param channel_mult: channel multiplier for each level of the UNet. - :param conv_resample: if True, use learned convolutions for upsampling and - downsampling. - :param use_checkpoint: use gradient checkpointing to reduce memory usage. - :param num_heads: the number of attention heads in each attention layer. - :param num_heads_channels: if specified, ignore num_heads and instead use - a fixed channel width per attention head. - :param num_heads_upsample: works with num_heads to set a different number - of heads for upsampling. Deprecated. - :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. - :param resblock_updown: use residual blocks for up/downsampling. - :param use_new_attention_order: use a different attention pattern for potentially - increased efficiency. + 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. + + 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. + + 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. + + Notes: + - channel_mult is evaluated via Python eval; ensure trusted input. + - When both n_classes and context_embedder are provided (invalid), initialization fails. + + 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"] + ``` """ def __init__( @@ -284,6 +456,11 @@ def __init__( assert not (n_classes is not None and context_embedder is not None), ( "n_classes and context_embedder cannot both be specified" ) + + if context_embedder: + assert context_embedder.n_output == 1 + self.context_channels = None if context_embedder is None else context_embedder.output_size[0] + self.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels @@ -335,6 +512,7 @@ def __init__( layers.append( AttentionBlock( ch, + context_channels=self.context_channels, num_heads=num_heads, dropout=dropout, use_checkpoint=use_checkpoint, @@ -375,6 +553,7 @@ def __init__( ), AttentionBlock( ch, + context_channels=self.context_channels, num_heads=num_heads, dropout=dropout, use_checkpoint=use_checkpoint, @@ -408,6 +587,7 @@ def __init__( layers.append( AttentionBlock( ch, + context_channels=self.context_channels, num_heads=num_heads, dropout=dropout, use_checkpoint=use_checkpoint, @@ -448,14 +628,61 @@ def forward( x_context: Float[Tensor, "batch_size channels height width"] | None = None, ) -> ModelOutput: """ - Apply the model to an input batch. - :param x: a [N x C x ...] Tensor of noisy image. - :param timesteps: a 1-D batch of timesteps. - :param y: a [N x C x ...] Tensor of labels (embedding computed and added with timestep embedding). - :param p: the probability of dropping the ground-truth label in the label embedding. - :param context: a [N x C x ...] Tensor of context for CrossAttention, can be images, text etc... - :param x_context: a [N x C x ...] Tensor of context for the input image that will be concatenated with the noisy data - :return: an [N x C x ...] Tensor of outputs. + Apply the U-Net denoiser to a batch of (noisy) images, optionally conditioned on + class labels and/or external context (e.g. text or images) with support for + classifier-free guidance and auxiliary input concatenation. + + Args: + x (torch.Tensor): Noisy input images of shape (N, C, H, W). The spatial + size (H, W) must match the model's configured image_size. + timesteps (torch.Tensor): 1D tensor of shape (N,) containing diffusion + timesteps (typically integers or floats in a predefined range). + y (torch.Tensor | None): Optional class label tensor of shape (N,) or a + shape accepted by the underlying label embedding module. Must be + provided iff the model is class-conditional (self.n_classes is not None). + context (Any | None): Optional conditioning input consumed by the + context_embedder (e.g. tokenized text, image features). Must be + provided if the model is context-conditional (self.context_embedder + is not None). + p (float, default=0.0): Probability of dropping labels (and/or context, + depending on embedder implementation) for classifier-free guidance. + Requires classifier_free = True and n_classes to be set when > 0. + x_context (torch.Tensor | None): Optional tensor of shape (N, C_ctx, H, W) + concatenated channel-wise to x before the first block. Useful for + conditioning on an aligned spatial map (e.g. segmentation, guidance + image, mask). + + Returns: + ModelOutput: A dictionary with key: + - "x": torch.Tensor of denoised outputs (shape (N, C_out, H, W)), + where C_out is determined by the final convolution/output head. + + Raises: + AssertionError: + - If (H, W) of x does not match model image_size. + - If y is (not) provided inconsistently with class-conditional setup. + - If context is (not) provided inconsistently with context-conditional setup. + - If p > 0 but classifier-free guidance or n_classes are not properly configured. + + Notes: + - Time embeddings are generated via timestep_embedding and passed through + a learned projection (time_embed). + - If label embedding is enabled, its contribution is added to the time embedding. + - When classifier-free guidance is active (p > 0), labels (and/or context) + may be randomly replaced/dropped during embedding to enable guidance at sampling. + - Intermediate activations are stored (hs) to form skip connections in the + decoder path. + + Example: + ```python + output = model.forward( + x=noisy_images, + timesteps=torch.randint(0, T, (batch_size,)), + y=labels, + context=text_tokens, + p=0.1, + pred = output["x"] + ``` """ assert list(x.shape[2:]) == self.image_size, ( f"Input shape {x.shape[2:]} does not match model image size {self.image_size}" diff --git a/src/diffulab/networks/embedders/common.py b/src/diffulab/networks/embedders/common.py index e47118a..90888b9 100644 --- a/src/diffulab/networks/embedders/common.py +++ b/src/diffulab/networks/embedders/common.py @@ -26,16 +26,19 @@ def output_size(self) -> tuple[int, ...]: pass @abstractmethod - def drop_context( + def drop_conditions( self, - drop_context: Any, + context: Any, p: float, ) -> Any: """ Randomly drop drop_context from a batch. - :param drop_context: an [N] sequence of context. - :param p: the probability of dropping a context. - :return: an [N] sequence of modified context. + + Args: + drop_context (Any): a sequence of context. + p (float): the probability of dropping a context. + Returns + Any: the same sequence of context with some elements randomly dropped. """ pass @@ -43,8 +46,11 @@ def drop_context( def forward(self, context: Any, p: float) -> tuple[Tensor, ...]: """ Apply the model to an input batch. - :param context: context, can be a tensor or a list of str for example. - :param p: the probability of dropping the context. - :return: an [N x C x ...] Tensor of outputs. + + Args: + 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. """ pass diff --git a/src/diffulab/networks/embedders/text.py b/src/diffulab/networks/embedders/text.py index d9093ec..c22db4c 100644 --- a/src/diffulab/networks/embedders/text.py +++ b/src/diffulab/networks/embedders/text.py @@ -3,6 +3,7 @@ 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 @@ -11,6 +12,31 @@ 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: @@ -45,23 +71,67 @@ def __init__( @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[Tensor, Tensor]: + 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]) -> Tensor: + 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 @@ -76,7 +146,15 @@ def get_g14_embeddings(self, context: list[str]) -> Tensor: return last_hidden_state, pooled_output # type: ignore - def get_t5_embeddings(self, context: list[str]) -> Tensor: + 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) @@ -85,31 +163,50 @@ def get_t5_embeddings(self, context: list[str]) -> Tensor: 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_l14: list[str], context_g14: list[str], context_t5: list[str] - ) -> dict[str, Tensor]: + 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_l14) # [batch_size, n_ctx, 768] + 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_g14) # [batch_size, n_ctx, 1280] + 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_t5) # [batch_size, n_ctx, 4096] + 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("*.pth")) + 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 @@ -124,21 +221,43 @@ def create_cache_from_list( 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}.pth", + 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) -> tuple[list[str], list[str], list[str]]: - context_l14 = ["" if random.random() < p else c for c in context] - context_g14 = ["" if random.random() < p else c for c in context] - context_t5 = ["" if random.random() < p else c for c in context] - return context_l14, context_g14, context_t5 + 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. - def forward(self, context: list[str], p: float) -> tuple[Tensor, Tensor]: + 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_l14, context_g14, context_t5 = self.drop_conditions(context, p) - embeddings = self.get_embeddings(context_l14, context_g14, context_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( [ diff --git a/src/diffulab/networks/repa/__init__.py b/src/diffulab/networks/repa/__init__.py index e69de29..6e225a7 100644 --- a/src/diffulab/networks/repa/__init__.py +++ b/src/diffulab/networks/repa/__init__.py @@ -0,0 +1,9 @@ +from .common import REPA +from .dinov2 import DinoV2 +from .perceiver_resampler import PerceiverResampler + +__all__ = [ + "REPA", + "DinoV2", + "PerceiverResampler", +] diff --git a/src/diffulab/networks/repa/common.py b/src/diffulab/networks/repa/common.py index cc916b8..a877a06 100644 --- a/src/diffulab/networks/repa/common.py +++ b/src/diffulab/networks/repa/common.py @@ -5,6 +5,7 @@ import torch import torch.nn as nn import torchvision.transforms as transforms # type: ignore[reportMissingTypeStub] +from jaxtyping import Float from PIL import Image from streaming import StreamingDataset from streaming.base import MDSWriter @@ -34,7 +35,9 @@ def embedding_dim(self) -> int: """ @abstractmethod - def preprocess(self, x: Tensor) -> Tensor: + def preprocess( + self, x: Float[Tensor, "batch_size channels height width"] + ) -> Float[Tensor, "batch_size channels height width"]: """ Preprocess the input tensor before encoding. @@ -47,7 +50,7 @@ def preprocess(self, x: Tensor) -> Tensor: """ @abstractmethod - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Float[Tensor, "batch_size channels height width"]) -> Float[Tensor, "batch_size seq_len dim"]: """ Forward pass of the encoder. Preprocesses the input tensor and returns the encoded representation. diff --git a/src/diffulab/networks/repa/dinov2.py b/src/diffulab/networks/repa/dinov2.py index f1dd76a..7b95b69 100644 --- a/src/diffulab/networks/repa/dinov2.py +++ b/src/diffulab/networks/repa/dinov2.py @@ -3,6 +3,7 @@ import timm import torch import torch.nn as nn +from jaxtyping import Float from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from torch import Tensor from torchvision.transforms import Normalize # type: ignore[reportMissingTypeStub] @@ -64,7 +65,9 @@ def embedding_dim(self) -> int: """ return self._embedding_dim - def preprocess(self, x: Tensor) -> Tensor: + def preprocess( + self, x: Float[Tensor, "batch_size channels height width"] + ) -> Float[Tensor, "batch_size channels height width"]: # Normalize the input tensor to be between 0 and 1 if x.min() >= 0 and x.max() <= 255: # Case: 0-255 x = x.float() / 255.0 @@ -86,7 +89,7 @@ def preprocess(self, x: Tensor) -> Tensor: ) return x - def forward(self, x: Tensor) -> Tensor: + def forward(self, x: Float[Tensor, "batch_size channels height width"]) -> Float[Tensor, "batch_size seq_len dim"]: """ Forward pass of the encoder. Args: diff --git a/src/diffulab/networks/repa/perceiver_resampler.py b/src/diffulab/networks/repa/perceiver_resampler.py index 25871b2..8b9b3a8 100644 --- a/src/diffulab/networks/repa/perceiver_resampler.py +++ b/src/diffulab/networks/repa/perceiver_resampler.py @@ -12,14 +12,29 @@ def exists(val: Any) -> bool: - """ - Check if an object is not None. + """Return True if ``val`` is not ``None``. + + Args: + val: Object to test. + + Returns: + bool: ``True`` when the object is not ``None`` else ``False``. """ return val is not None -# ROPE only on k class PerceiverRotaryPositionalEmbedding(RotaryPositionalEmbedding): + """Rotary positional embedding applied only to keys. + + This subclass restricts rotary application to the key tensor (``k``) while + passing queries and values through unchanged. It caches precomputed cosine + and sine tables via the parent implementation. + + Args: + dim (int): Rotary embedding dimension (applied to the leading slice of the key head dimension). + base (int): Base for rotary frequency computation. + """ + def __init__(self, dim: int = 32, base: int = 10_000) -> None: super().__init__(dim, base) # type: ignore @@ -33,6 +48,17 @@ def forward( Float[Tensor, "batch_size seq_len n_heads head_dim"], Float[Tensor, "batch_size seq_len n_heads head_dim"], ]: + """Apply rotary embedding to the key tensor only. + + Args: + q (Tensor): Query tensor of shape ``[B, N, H, D]``. + k (Tensor): Key tensor of shape ``[B, N, H, D]``. + v (Tensor): Value tensor of shape ``[B, N, H, D]``. + + Returns: + tuple: ``(q, k_rot, v)`` with identical shapes where ``k_rot`` has + 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) @@ -54,13 +80,16 @@ def forward( def FeedForward(dim: int, mult: float = 4) -> nn.Sequential: - """ - A simple feed-forward module with a GELU activation function. + """Feed-forward MLP block with GELU activation. + + Structure: LayerNorm -> Linear(dim, dim*mult) -> GELU -> Linear(dim*mult, dim). + Args: - dim (int): Input and output dimension of the network. - mult (float): Multiplier for the inner dimension. + dim (int): Input / output embedding dimension. + mult (int): Width multiplier for the hidden layer. + Returns: - nn.Sequential: A sequential model containing LayerNorm, Linear, GELU, and another Linear layer. + nn.Sequential: Configured feed-forward network. """ inner_dim = int(dim * mult) return nn.Sequential( @@ -69,7 +98,22 @@ def FeedForward(dim: int, mult: float = 4) -> nn.Sequential: class PerceiverAttention(nn.Module): - def __init__(self, dim: int, head_dim: int = 64, num_heads: int = 8, partial_rotary_factor: int = 1) -> None: + """Cross-attention layer between inputs and a set of latent tokens. + + Queries are derived from latent tokens; keys/values are concatenation of + (projected) input tokens and latent tokens (self-attention augmentation). + + Rotary positional embedding (optionally partial) is applied only to the key + originating from the input tokens. + + Args: + dim (int): Embedding dimension of inputs and latents. + head_dim (int): Dimension per attention head. + num_heads (int): Number of attention heads. + partial_rotary_factor (float): Fraction (0..1] of ``head_dim`` receiving rotary embedding. + """ + + def __init__(self, dim: int, head_dim: int = 64, num_heads: int = 8, partial_rotary_factor: float = 1) -> None: super().__init__() # type: ignore[reportUnknownMemberType] self.scale = head_dim**-0.5 self.num_heads = num_heads @@ -88,6 +132,16 @@ def __init__(self, dim: int, head_dim: int = 64, num_heads: int = 8, partial_rot def forward( self, x: Float[Tensor, "batch n dim"], latents: Float[Tensor, "batch m dim"] ) -> Float[Tensor, "batch m dim"]: + """ + Perform Perceiver cross/self attention update on latent tokens. + + Args: + x (Tensor): Input sequence embeddings of shape ``[B, N, D]``. + latents (Tensor): Current latent tokens of shape ``[B, M, D]``. + + Returns: + Tensor: Updated latent tokens of shape ``[B, M, D]``. + """ x = self.norm_x(x) latents = self.norm_latents(latents) @@ -124,6 +178,22 @@ def forward( class PerceiverResampler(nn.Module): + """ + Stack of Perceiver attention + feed-forward layers producing latents. + + A learned set of ``num_latents`` tokens is iteratively refined via cross- + attention with the input sequence followed by a feed-forward block (each + with residual connections). The final normalized latent set is returned. + + Args: + dim (int): Embedding dimension for inputs and latents. + depth (int): Number of attention + feed-forward layers. + head_dim (int): Dimension per attention head. + num_heads (int): Number of attention heads. + ff_mult (int): Multiplier for hidden dimension in feed-forward layers. + num_latents (int): Number of learned latent tokens to maintain. + """ + def __init__( self, dim: int, depth: int, head_dim: int = 64, num_heads: int = 8, ff_mult: int = 4, num_latents: int = 16 ): @@ -144,6 +214,14 @@ def __init__( self.norm = nn.LayerNorm(dim) def forward(self, x: Float[Tensor, "batch n dim"]) -> Float[Tensor, "batch m dim"]: + """Encode an input sequence into a fixed set of latent tokens. + + Args: + x: Input embeddings of shape ``[B, N, D]``. + + Returns: + Tensor: Latent tokens of shape ``[B, M, D]`` where ``M = num_latents``. + """ latents = repeat(self.latents, "n d -> b n d", b=x.shape[0]) for attn, ff in self.layers: # type: ignore diff --git a/src/diffulab/networks/utils/nn.py b/src/diffulab/networks/utils/nn.py index e60cb45..7727739 100644 --- a/src/diffulab/networks/utils/nn.py +++ b/src/diffulab/networks/utils/nn.py @@ -5,7 +5,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from jaxtyping import Float +from jaxtyping import Float, Int from torch import Tensor @@ -17,8 +17,11 @@ def forward(self, input: Tensor) -> Tensor: def normalization(channels: int) -> GroupNorm32: """ Make a standard normalization layer. - :param channels: number of input channels. - :return: an nn.Module for normalization. + + Args: + channels (int): number of input channels. + Returns: + GroupNorm32: an nn.Module for normalization. """ return GroupNorm32(32, channels) @@ -28,12 +31,15 @@ class Upsample(nn.Module): From https://github.com/openai/guided-diffusion under MIT license as of 2024-18-08 An upsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - """ def __init__(self, channels: int, use_conv: bool, out_channels: int | None = None) -> None: + """ + Args: + channels (int): channels in the inputs and outputs. + use_conv (bool): a bool determining if a convolution is applied. + out_channels (int | None): if provided, the number of output channels. + """ super().__init__() # type:ignore self.channels = channels self.out_channels = out_channels or channels @@ -41,7 +47,9 @@ def __init__(self, channels: int, use_conv: bool, out_channels: int | None = Non if use_conv: self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1) - def forward(self, x: Tensor) -> Tensor: + def forward( + self, x: Float[Tensor, "batch_size channels height width"] + ) -> Float[Tensor, "batch_size channels 2*height 2*width"]: assert x.shape[1] == self.channels x = F.interpolate(x, scale_factor=2, mode="nearest") # type: ignore if self.use_conv: @@ -54,11 +62,15 @@ class Downsample(nn.Module): From https://github.com/openai/guided-diffusion under MIT license as of 2024-18-08 A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. """ def __init__(self, channels: int, use_conv: bool, out_channels: int | None = None) -> None: + """ + Args: + channels (int): channels in the inputs and outputs. + use_conv (bool): a bool determining if a convolution is applied. + out_channels (int | None): if provided, the number of output channels. + """ super().__init__() # type:ignore self.channels = channels self.out_channels = out_channels or channels @@ -70,21 +82,27 @@ def __init__(self, channels: int, use_conv: bool, out_channels: int | None = Non assert self.channels == self.out_channels self.op = nn.AvgPool2d(kernel_size=stride, stride=stride) - def forward(self, x: Tensor) -> Tensor: + def forward( + self, x: Float[Tensor, "batch_size channels height width"] + ) -> Float[Tensor, "batch_size channels height//2 width//2"]: assert x.shape[1] == self.channels return self.op(x) -def timestep_embedding(timesteps: Tensor, dim: int, max_period: int = 10000) -> Tensor: +def timestep_embedding( + timesteps: Float[Tensor, "batch_size"], dim: int, max_period: int = 10000 +) -> Float[Tensor, "batch_size dim"]: """ From https://github.com/openai/guided-diffusion under MIT license as of 2024-18-08 Create sinusoidal timestep embeddings. - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. + + Args: + timesteps (Tensor): a 1D tensor of timesteps. + dim (int): the dimension of the output embeddings. + max_period (int): the maximum period for the sinusoidal functions. + Returns: + Tensor: a 2D tensor of shape [len(timesteps), dim] containing the sinusoidal embeddings. """ half = dim // 2 freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( @@ -99,6 +117,12 @@ def timestep_embedding(timesteps: Tensor, dim: int, max_period: int = 10000) -> class LabelEmbed(nn.Module): def __init__(self, num_classes: int, embed_dim: int, classifier_free_guidance: bool = False) -> None: + """ + Args: + num_classes (int): the number of classes. + embed_dim (int): the dimension of the embeddings. + classifier_free_guidance (bool): if True, the embedding layer will have an extra class for classifier-free guidance. + """ super().__init__() # type: ignore self.num_classes = num_classes self.embed_dim = embed_dim @@ -111,23 +135,28 @@ def __init__(self, num_classes: int, embed_dim: int, classifier_free_guidance: b def drop_labels( self, - labels: Tensor, + labels: Int[Tensor, "batch_size"], p: float, ) -> Tensor: """ Randomly drop labels from a batch. - :param labels: an [N] tensor of labels. - :param p: the probability of dropping a label. - :return: an [N] tensor of modified labels. + + Args: + labels (Tensor): an [N] tensor of labels. + p (float): the probability of dropping a label. + Returns: + Tensor: a tensor of labels with some labels randomly replaced by the extra class """ return torch.where(torch.rand(labels.size(), device=labels.device) < p, self.num_classes, labels) - def forward(self, labels: Tensor, p: float = 0) -> Tensor: + def forward(self, labels: Int[Tensor, "batch_size"], p: float = 0) -> Float[Tensor, "batch_size embed_dim"]: """ Embed a batch of labels. - :param labels: an [N] tensor of labels. - :param p: the probability of dropping a label. - :return: an [N x embed_dim] tensor of embeddings. + Args: + labels (Tensor): a [N] tensor of labels. + p (float): the probability of dropping a label. If greater than 0, labels will be randomly replaced by the extra class. + Returns: + Tensor: an [N, embed_dim] tensor of embeddings. """ if p > 0: assert self.classifier_free_guidance, "Label dropout is only supported with classifier-free guidance." @@ -140,8 +169,17 @@ class RotaryPositionalEmbedding(nn.Module): theta: Tensor cos: Tensor sin: Tensor + """ + Rotary Positional Embedding (RoPE) module. + This module applies rotary positional encoding to the query and key tensors in a multi-head attention mechanism. + """ def __init__(self, dim: int = 32, base: int = 10_000) -> None: + """ + Args: + dim (int): the dimension of the positional encoding. + base (int): the base for the exponential decay of frequencies. + """ super().__init__() # type: ignore self.dim = dim self.base = base @@ -155,6 +193,11 @@ def __init__(self, dim: int = 32, base: int = 10_000) -> None: self.sin = torch.empty(0, requires_grad=False) def _cache(self, seq_len: int) -> None: + """ + Cache the cosine and sine values for the given sequence length. + Args: + seq_len (int): the length of the sequence for which to cache the values. + """ if seq_len <= self.cos.shape[0]: return t = torch.arange(seq_len, dtype=torch.float32, device=self.theta.device) # type: ignore @@ -164,7 +207,9 @@ def _cache(self, seq_len: int) -> None: self.cos = embs.cos().float() self.sin = embs.sin().float() - def _neg_half(self, x: Tensor) -> Tensor: + def _neg_half( + self, x: Float[Tensor, "batch_size, num_heads, seq_length, head_dim"] + ) -> Float[Tensor, "batch_size, num_heads, seq_length, head_dim"]: return torch.cat([-x[:, :, :, self.dim // 2 :], x[:, :, :, : self.dim // 2]], dim=-1) def forward( @@ -177,6 +222,16 @@ def forward( Float[Tensor, "batch_size seq_len n_heads head_dim"], Float[Tensor, "batch_size seq_len n_heads head_dim"], ]: + """ + Apply 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]. + Returns: + Tuple[Tensor, Tensor, Tensor]: the rotated query and key tensors, and the unchanged value tensor. + """ seq_len = q.shape[1] self._cache(seq_len) cos = self.cos.to(device=q.device, dtype=q.dtype) diff --git a/src/diffulab/networks/utils/utils.py b/src/diffulab/networks/utils/utils.py index 903d754..abb00a3 100644 --- a/src/diffulab/networks/utils/utils.py +++ b/src/diffulab/networks/utils/utils.py @@ -19,11 +19,15 @@ def checkpoint(func: Callable[..., Tensor], inputs: Any, params: Any, flag: bool Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. - :param func: the function to evaluate. - :param inputs: the argument sequence to pass to `func`. - :param params: a sequence of parameters `func` depends on but does not - explicitly take as arguments. - :param flag: if False, disable gradient checkpointing. + + Args: + func (Callable[..., Tensor]): the function to evaluate. + inputs (Any): the argument sequence to pass to `func`. + params (Any): a sequence of parameters `func` depends on but does not + explicitly take as arguments. + flag (bool): if False, disable gradient checkpointing. + Returns: + Any: the output of the function, either directly or through checkpointing. """ if flag: args = tuple(inputs) + tuple(params) diff --git a/src/diffulab/networks/vision_towers/__init__.py b/src/diffulab/networks/vision_towers/__init__.py index 22f496f..a490be6 100644 --- a/src/diffulab/networks/vision_towers/__init__.py +++ b/src/diffulab/networks/vision_towers/__init__.py @@ -1,5 +1,7 @@ +from .common import VisionTower from .dc_ae import DCAE __all__ = [ "DCAE", + "VisionTower", ] diff --git a/src/diffulab/networks/vision_towers/dc_ae.py b/src/diffulab/networks/vision_towers/dc_ae.py index e63f44a..433a97b 100644 --- a/src/diffulab/networks/vision_towers/dc_ae.py +++ b/src/diffulab/networks/vision_towers/dc_ae.py @@ -6,11 +6,11 @@ class DCAE(VisionTower): - def __init__( - self, model_name: str = "mit-han-lab/dc-ae-f32c32-in-1.0-diffusers", latent_scale: float = 0.18215 - ) -> None: - super().__init__(latent_scale=latent_scale) - self.model: AutoencoderDC = AutoencoderDC.from_pretrained(model_name) # type: ignore[reportUnknownMemberType] + def __init__(self, model_name: str = "mit-han-lab/dc-ae-f32c32-in-1.0-diffusers") -> None: + model: AutoencoderDC = AutoencoderDC.from_pretrained(model_name) # type: ignore[reportUnknownMemberType] + super().__init__(latent_scale=model.config.scaling_factor) # type: ignore + + self.model = model self.model.eval() self.model.requires_grad_(False) diff --git a/src/diffulab/training/__init__.py b/src/diffulab/training/__init__.py index 81618ca..4db4c5f 100644 --- a/src/diffulab/training/__init__.py +++ b/src/diffulab/training/__init__.py @@ -1,3 +1,4 @@ +from .losses import LossFunction, RepaLoss from .trainer import Trainer -__all__ = ["Trainer"] +__all__ = ["Trainer", "LossFunction", "RepaLoss"] diff --git a/src/diffulab/training/losses/common.py b/src/diffulab/training/losses/common.py index 3ddab0a..ef09440 100644 --- a/src/diffulab/training/losses/common.py +++ b/src/diffulab/training/losses/common.py @@ -1,8 +1,22 @@ from abc import ABC +from pathlib import Path import torch.nn as nn +from accelerate import Accelerator # type: ignore class LossFunction(ABC, nn.Module): # to be completed def __init__(self) -> None: super().__init__() # type: ignore + + def save(self, path: str | Path, accelerator: Accelerator | None) -> None: + """ + Save eventual learnable parameters of the loss function. + + Args: + path (str | Path): Path to save the loss function. + accelerator (Accelerator | None): Accelerator instance for distributed training. Uses + accelerator.save if provided + """ + # By default, doesn't save anything. + pass diff --git a/src/diffulab/training/losses/repa.py b/src/diffulab/training/losses/repa.py index e179514..675ff8e 100644 --- a/src/diffulab/training/losses/repa.py +++ b/src/diffulab/training/losses/repa.py @@ -1,6 +1,8 @@ +from pathlib import Path from typing import Any, TypedDict import torch +from accelerate import Accelerator # type: ignore from jaxtyping import Float from torch import Tensor, nn @@ -74,6 +76,30 @@ def __init__( self.src_features: Tensor | None = None self.coeff = coeff + def save(self, path: str | Path, accelerator: Accelerator | None = None) -> None: + """ + Save state dict containing projection (and resampler if present). + + Args: + path (str | Path): Path to save the loss function. + accelerator (Accelerator | None): Accelerator instance for distributed training. Uses + accelerator.save if provided. + """ + file_path = Path(path) / "RepaLoss.pt" + + merged_state = {} + for k, v in self.proj.state_dict().items(): + merged_state[f"proj.{k}"] = v + if self.resampler is not None: + for k, v in self.resampler.state_dict().items(): + merged_state[f"resampler.{k}"] = v + + if accelerator: + accelerator.save(merged_state, file_path) # type: ignore + return + + torch.save(merged_state, file_path) # type: ignore + def _register_hook(self, model: MMDiT) -> None: """Register the forward hook on the specified layer of the model.""" self._unregister_hook() # Ensure no previous hook is registered diff --git a/src/diffulab/training/trainer.py b/src/diffulab/training/trainer.py index b024b37..b16e0e5 100644 --- a/src/diffulab/training/trainer.py +++ b/src/diffulab/training/trainer.py @@ -2,7 +2,7 @@ import os from datetime import datetime from pathlib import Path -from typing import TYPE_CHECKING, Any, Iterable +from typing import TYPE_CHECKING, Any, Iterable, cast import torch import wandb @@ -19,6 +19,7 @@ if TYPE_CHECKING: from diffulab.diffuse.diffuser import Diffuser + from diffulab.training.losses.common import LossFunction HOME_PATH = Path.home() @@ -236,19 +237,22 @@ def save_model( If provided, its state will be saved. Defaults to None. Note: The following files are created in self.save_path: - - denoiser.pth: Main model state dict - - optimizer.pth: Optimizer state dict - - ema.pth: EMA model state dict (if EMA is used) - - scheduler.pth: Scheduler state dict (if scheduler is used) + - 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 - self.accelerator.save(unwrapped_denoiser.state_dict(), self.save_path / "denoiser.pth") # type: ignore - self.accelerator.save(optimizer.optimizer.state_dict(), self.save_path / "optimizer.pth") # type: ignore + self.accelerator.save(unwrapped_denoiser.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: Denoiser = self.accelerator.unwrap_model(ema_denoiser) # type: ignore - self.accelerator.save(unwrapped_ema.ema_model.state_dict(), self.save_path / "ema.pth") # 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.pth") # type: ignore + self.accelerator.save(scheduler.scheduler.state_dict(), self.save_path / "scheduler.pt") # type: ignore + for extra_loss in diffuser.extra_losses: + unwrapped_loss: "LossFunction" = self.accelerator.unwrap_model(extra_loss) # type: ignore + unwrapped_loss.save(self.save_path, self.accelerator) # type: ignore @torch.no_grad() # type: ignore def log_images( @@ -284,14 +288,15 @@ def log_images( x: Tensor = batch.pop("x") # type: ignore original_steps = diffuser.n_steps diffuser.set_steps(val_steps) + guidance_scale = 4 if diffuser.denoiser.classifier_free else 0 if self.use_ema and ema_eval is not None: original_model = diffuser.denoiser diffuser.denoiser = ema_eval - images = diffuser.generate(data_shape=x.shape, model_inputs=batch) + images = diffuser.generate(data_shape=x.shape, model_inputs=batch, guidance_scale=guidance_scale) diffuser.denoiser = original_model else: - images = diffuser.generate(data_shape=x.shape, model_inputs=batch) + 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") @@ -308,8 +313,10 @@ def train( per_batch_scheduler: bool = False, log_validation_images: bool = False, train_embedder: bool = False, - p_classifier_free_guidance: float = 0, + p_classifier_free_guidance: float = 0.2, val_steps: int = 50, + optimizer_ckpt: str | None = None, + denoiser_ckpt: str | None = None, ema_ckpt: str | None = None, epoch_start: int = 0, ): @@ -333,7 +340,7 @@ def train( 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 - guidance during training. Defaults to 0. + guidance during training. Defaults to 0.2 val_steps (int, optional): Number of steps to use for validation image generation. Defaults to 50. ema_ckpt (str | None, optional): Path to EMA model checkpoint for loading pretrained @@ -347,6 +354,9 @@ def train( - Model checkpoints are saved when validation loss improves. - EMA model is used for validation if enabled. """ + if not diffuser.denoiser.classifier_free: + p_classifier_free_guidance = 0 + if self.use_ema: ema_denoiser = EMA( diffuser.denoiser, @@ -356,16 +366,34 @@ def train( ).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 + ema_denoiser = cast(EMA, 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(diffuser.vision_tower) # type: ignore diffuser.denoiser, train_dataloader, val_dataloader, optimizer = self.accelerator.prepare( # type: ignore diffuser.denoiser, train_dataloader, val_dataloader, optimizer ) + + 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, torch.Tensor): + state[k] = v.to(device) + for loss_idx in range(len(diffuser.extra_losses)): diffuser.extra_losses[loss_idx] = self.accelerator.prepare(diffuser.extra_losses[loss_idx]) # type: ignore @@ -416,10 +444,12 @@ def train( if val_dataloader is not None: diffuser.eval() # type: ignore + ema_eval: Denoiser | None = None if ema_denoiser is not None: - ema_eval = ema_denoiser.eval() # type: ignore - else: - ema_eval = None + ema_eval = cast( + Denoiser, + ema_denoiser.eval(), + ) tq_val_batch = tqdm( val_dataloader, # type: ignore disable=not self.accelerator.is_main_process, From bebc6ab6bf77a544c33db7df005ba0e8b626b1aa Mon Sep 17 00:00:00 2001 From: LouisRouss Date: Sat, 9 Aug 2025 17:59:53 +0200 Subject: [PATCH 2/6] add mmdit unittest --- src/diffulab/networks/utils/nn.py | 4 +- tests/conftest.py | 14 + tests/unit_tests/networks/conftest.py | 137 ++ .../networks/denoisers/test_mmdit.py | 1676 +++++++++++++++++ 4 files changed, 1829 insertions(+), 2 deletions(-) create mode 100644 tests/conftest.py create mode 100644 tests/unit_tests/networks/conftest.py create mode 100644 tests/unit_tests/networks/denoisers/test_mmdit.py diff --git a/src/diffulab/networks/utils/nn.py b/src/diffulab/networks/utils/nn.py index 7727739..02d1aa0 100644 --- a/src/diffulab/networks/utils/nn.py +++ b/src/diffulab/networks/utils/nn.py @@ -208,8 +208,8 @@ def _cache(self, seq_len: int) -> None: self.sin = embs.sin().float() def _neg_half( - self, x: Float[Tensor, "batch_size, num_heads, seq_length, head_dim"] - ) -> Float[Tensor, "batch_size, num_heads, seq_length, head_dim"]: + self, x: Float[Tensor, "batch_size num_heads seq_length head_dim"] + ) -> Float[Tensor, "batch_size num_heads seq_length head_dim"]: return torch.cat([-x[:, :, :, self.dim // 2 :], x[:, :, :, : self.dim // 2]], dim=-1) def forward( diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..828dc06 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,14 @@ +"""Pytest configuration and fixtures for the entire test suite.""" + +import pytest +import torch + + +@pytest.fixture(autouse=True) +def set_random_seed(): + """Set random seed for reproducible testing.""" + torch.manual_seed(42) # type: ignore + if torch.cuda.is_available(): + torch.cuda.manual_seed(42) + yield + pass diff --git a/tests/unit_tests/networks/conftest.py b/tests/unit_tests/networks/conftest.py new file mode 100644 index 0000000..60a19b5 --- /dev/null +++ b/tests/unit_tests/networks/conftest.py @@ -0,0 +1,137 @@ +"""Network-specific fixtures for testing.""" + +from typing import Any + +import pytest +import torch + +from diffulab.networks.embedders.common import ContextEmbedder + + +class MockContextEmbedder(ContextEmbedder): + """Mock context embedder for testing.""" + + def __init__(self, pooled_dim: int = 256, sequence_dim: int = 512, sequence_len: int = 32): + super().__init__() + self.pooled_dim = pooled_dim + self.sequence_dim = sequence_dim + self.sequence_len = sequence_len + + @property + def n_output(self) -> int: + return 2 + + @property + def output_size(self) -> tuple[int, int]: + return (self.pooled_dim, self.sequence_dim) + + def drop_conditions(self, context: Any, p: float) -> Any: + return context + + def forward(self, context: Any = None, p: float = 0.0) -> tuple[torch.Tensor, torch.Tensor]: + batch_size = 4 if context is None else (context.shape[0] if hasattr(context, "shape") else 4) + pooled = torch.randn(batch_size, self.pooled_dim) + sequence = torch.randn(batch_size, self.sequence_len, self.sequence_dim) + return pooled, sequence + + +@pytest.fixture(scope="module") +def mock_context_embedder(): + """Create a mock context embedder for testing.""" + return MockContextEmbedder() + + +@pytest.fixture(scope="module") +def input_dim(): + """Standard input dimension for testing.""" + return 512 + + +@pytest.fixture(scope="module") +def hidden_dim(): + """Standard hidden dimension for testing.""" + return 512 + + +@pytest.fixture(scope="module") +def embedding_dim(): + """Standard embedding dimension for testing.""" + return 256 + + +@pytest.fixture(scope="module") +def context_dim(): + """Standard context dimension for testing.""" + return 512 + + +@pytest.fixture(scope="module") +def num_heads(): + """Standard number of attention heads for testing.""" + return 8 + + +@pytest.fixture(scope="module") +def mlp_ratio(): + """Standard MLP ratio for testing.""" + return 4 + + +@pytest.fixture(scope="module") +def patch_size(): + """Standard patch size for testing.""" + return 16 + + +@pytest.fixture(scope="module") +def input_channels(): + """Standard number of input channels for testing.""" + return 3 + + +@pytest.fixture(scope="module") +def image_size(): + """Standard image size for testing.""" + return 256 + + +@pytest.fixture(scope="module") +def sample_image(batch_size: int, input_channels: int, image_size: int) -> torch.Tensor: + """Create a sample image tensor for testing.""" + return torch.randn(batch_size, input_channels, image_size, image_size) + + +@pytest.fixture(scope="module") +def sample_timesteps(batch_size: int) -> torch.Tensor: + """Create sample timesteps for testing.""" + return torch.randn(batch_size) + + +@pytest.fixture +def sample_labels(batch_size: int) -> torch.Tensor: + """Create sample class labels for testing.""" + return torch.randint(0, 10, (batch_size,)) + + +@pytest.fixture(scope="module") +def device(): + """Return the device to use for testing.""" + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +@pytest.fixture(scope="module") +def batch_size(): + """Standard batch size for testing.""" + return 4 + + +@pytest.fixture(scope="module") +def seq_len(): + """Standard sequence length for testing.""" + return 25 + + +@pytest.fixture(scope="module") +def context_seq_len(): + """Standard context sequence length for testing.""" + return 32 diff --git a/tests/unit_tests/networks/denoisers/test_mmdit.py b/tests/unit_tests/networks/denoisers/test_mmdit.py new file mode 100644 index 0000000..989db9a --- /dev/null +++ b/tests/unit_tests/networks/denoisers/test_mmdit.py @@ -0,0 +1,1676 @@ +""" +Comprehensive unit tests for MMDiT module components. + +This module provides exhaustive test coverage for all classes and functions in the MMDiT (Multimodal Diffusion Transformer) +implementation, including: +- DiTAttention: Self-attention mechanism with rotary positional embeddings +- MMDiTAttention: Multi-modal attention for input and context tensors +- DiTBlock: Complete transformer block with modulation for simple DiT +- MMDiTBlock: Multi-modal transformer block with separate input/context processing +- ModulatedLastLayer: Final output layer with adaptive layer normalization +- MMDiT: Main diffusion transformer model supporting both simple and multi-modal modes + +The tests cover initialization, forward pass shapes, gradient flow, edge cases, and error conditions +to ensure robust and reliable behavior across different configurations and inputs. + +Classes: + TestDiTAttention: Tests for the DiTAttention self-attention mechanism. + TestMMDiTAttention: Tests for the MMDiTAttention multi-modal attention mechanism. + TestDiTBlock: Tests for the DiTBlock transformer block. + TestMMDiTBlock: Tests for the MMDiTBlock multi-modal transformer block. + TestModulatedLastLayer: Tests for the ModulatedLastLayer output layer. + TestMMDiT: Tests for the main MMDiT model. + TestMMDiTEdgeCases: Tests for edge cases and error conditions. +""" + +from typing import cast +from unittest.mock import MagicMock, patch + +import pytest +import torch +import torch.nn as nn + +from diffulab.networks.denoisers.common import ModelOutput +from diffulab.networks.denoisers.mmdit import ( + DiTAttention, + DiTBlock, + MMDiT, + MMDiTAttention, + MMDiTBlock, + ModulatedLastLayer, +) +from diffulab.networks.embedders.common import ContextEmbedder + + +class TestDiTAttention: + """ + Test class for DiTAttention module. + + This class provides comprehensive testing for the DiTAttention self-attention mechanism, + which implements multi-head attention with rotary positional embeddings for diffusion transformers. + + Attributes: + None + + Methods: + dit_attention: Fixture that creates a DiTAttention instance for testing. + test_init: Tests proper initialization of DiTAttention components. + test_forward_shape: Tests output shape consistency in forward pass. + test_forward_different_batch_sizes: Tests behavior with various batch sizes. + test_forward_different_seq_lengths: Tests behavior with various sequence lengths. + test_partial_rotary_factor: Tests rotary embedding dimension calculation. + test_gradient_flow: Tests gradient propagation through the attention mechanism. + """ + + @pytest.fixture + def dit_attention(self, input_dim: int, hidden_dim: int, num_heads: int): + """ + Create DiTAttention instance for testing. + + Args: + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + num_heads (int): Number of attention heads. + + Returns: + DiTAttention: Configured attention module for testing. + """ + return DiTAttention( + input_dim=input_dim, + dim=hidden_dim, + num_heads=num_heads, + partial_rotary_factor=1.0, + base=10000, + ) + + def test_init(self, dit_attention: DiTAttention, input_dim: int, hidden_dim: int, num_heads: int): + """ + Test DiTAttention initialization. + + Verifies that all components are properly initialized with correct dimensions + and that attributes match the expected values based on input parameters. + + Args: + dit_attention (DiTAttention): The attention module to test. + input_dim (int): Expected input dimension. + hidden_dim (int): Expected hidden dimension. + num_heads (int): Expected number of attention heads. + + Raises: + AssertionError: If any component is not initialized correctly. + """ + assert dit_attention.num_heads == num_heads + assert dit_attention.head_dim == hidden_dim // num_heads + assert dit_attention.scale == (hidden_dim // num_heads) ** -0.5 + assert dit_attention.partial_rotary_factor == 1.0 + assert dit_attention.rotary_dim == dit_attention.head_dim + + # Check layer dimensions + assert dit_attention.qkv.in_features == input_dim + assert dit_attention.qkv.out_features == 3 * hidden_dim + assert dit_attention.proj_out.in_features == hidden_dim + assert dit_attention.proj_out.out_features == input_dim + + def test_forward_shape(self, dit_attention: DiTAttention, batch_size: int, seq_len: int, input_dim: int): + """ + Test DiTAttention forward pass output shape. + + Verifies that the attention mechanism produces output tensors with the correct + shape and data type, maintaining input dimensions while processing attention. + + Args: + dit_attention (DiTAttention): The attention module to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of input features. + + Raises: + AssertionError: If output shape or dtype doesn't match expectations. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim) + output: torch.Tensor = dit_attention(input_tensor) + + assert output.shape == (batch_size, seq_len, input_dim) + assert output.dtype == input_tensor.dtype + + def test_forward_different_batch_sizes(self, dit_attention: DiTAttention, seq_len: int, input_dim: int): + """ + Test DiTAttention with different batch sizes. + + Ensures the attention mechanism works correctly across various batch sizes, + which is important for both training and inference scenarios. + + Args: + dit_attention (DiTAttention): The attention module to test. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of input features. + + Raises: + AssertionError: If output shapes are incorrect for any batch size. + """ + for batch_size in [1, 2, 8, 16]: + input_tensor = torch.randn(batch_size, seq_len, input_dim) + output: torch.Tensor = dit_attention(input_tensor) + assert output.shape == (batch_size, seq_len, input_dim) + + def test_forward_different_seq_lengths(self, dit_attention: DiTAttention, batch_size: int, input_dim: int): + """ + Test DiTAttention with different sequence lengths. + + Verifies that the attention mechanism handles variable sequence lengths correctly, + which is crucial for processing images of different sizes and patch configurations. + + Args: + dit_attention (DiTAttention): The attention module to test. + batch_size (int): Size of the input batch. + input_dim (int): Dimension of input features. + + Raises: + AssertionError: If output shapes are incorrect for any sequence length. + """ + for seq_len in [10, 25, 50, 100]: + input_tensor = torch.randn(batch_size, seq_len, input_dim) + output: torch.Tensor = dit_attention(input_tensor) + assert output.shape == (batch_size, seq_len, input_dim) + + def test_partial_rotary_factor(self, input_dim: int, hidden_dim: int, num_heads: int): + """ + Test DiTAttention with different partial rotary factors. + + Validates that the rotary embedding dimension is calculated correctly + based on the partial rotary factor, which controls how much of each + attention head uses rotary positional embeddings. + + Args: + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + num_heads (int): Number of attention heads. + + Raises: + AssertionError: If rotary dimension calculation is incorrect. + """ + partial_rotary_factor = 0.5 + attention = DiTAttention( + input_dim=input_dim, + dim=hidden_dim, + num_heads=num_heads, + partial_rotary_factor=partial_rotary_factor, + ) + + expected_rotary_dim = int((hidden_dim // num_heads) * partial_rotary_factor) + assert attention.rotary_dim == expected_rotary_dim + + def test_gradient_flow(self, dit_attention: DiTAttention, batch_size: int, seq_len: int, input_dim: int): + """ + Test gradient flow through DiTAttention. + + Ensures that gradients can properly flow backward through the attention mechanism, + which is essential for training the diffusion transformer model. + + Args: + dit_attention (DiTAttention): The attention module to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of input features. + + Raises: + AssertionError: If gradients are not computed or have incorrect shapes. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim, requires_grad=True) + output: torch.Tensor = dit_attention(input_tensor) + loss = output.sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert input_tensor.grad is not None + assert input_tensor.grad.shape == input_tensor.shape + + +class TestMMDiTAttention: + """ + Test class for MMDiTAttention module. + + This class provides comprehensive testing for the MMDiTAttention multi-modal attention mechanism, + which processes both input and context tensors simultaneously with cross-attention and rotary + positional embeddings for multi-modal diffusion transformers. + + Attributes: + None + + Methods: + mmdit_attention: Fixture that creates an MMDiTAttention instance for testing. + test_init: Tests proper initialization of MMDiTAttention components. + test_forward_shape: Tests output shapes for both input and context tensors. + test_forward_different_dimensions: Tests behavior with different input/context dimensions. + test_gradient_flow: Tests gradient propagation through the multi-modal attention. + """ + + @pytest.fixture + def mmdit_attention(self, context_dim: int, input_dim: int, hidden_dim: int, num_heads: int): + """ + Create MMDiTAttention instance for testing. + + Args: + context_dim (int): Dimension of the context features. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + num_heads (int): Number of attention heads. + + Returns: + MMDiTAttention: Configured multi-modal attention module for testing. + """ + return MMDiTAttention( + context_dim=context_dim, + input_dim=input_dim, + dim=hidden_dim, + num_heads=num_heads, + partial_rotary_factor=1.0, + base=10000, + ) + + def test_init( + self, mmdit_attention: MMDiTAttention, context_dim: int, input_dim: int, hidden_dim: int, num_heads: int + ): + """ + Test MMDiTAttention initialization. + + Verifies that all components for multi-modal attention are properly initialized + with correct dimensions for both input and context processing paths. + + Args: + mmdit_attention (MMDiTAttention): The multi-modal attention module to test. + context_dim (int): Expected context dimension. + input_dim (int): Expected input dimension. + hidden_dim (int): Expected hidden dimension. + num_heads (int): Expected number of attention heads. + + Raises: + AssertionError: If any component is not initialized correctly. + """ + assert mmdit_attention.num_heads == num_heads + assert mmdit_attention.head_dim == hidden_dim // num_heads + assert mmdit_attention.scale == (hidden_dim // num_heads) ** -0.5 + + # Check layer dimensions + assert mmdit_attention.qkv_input.in_features == input_dim + assert mmdit_attention.qkv_input.out_features == 3 * hidden_dim + assert mmdit_attention.qkv_context.in_features == context_dim + assert mmdit_attention.qkv_context.out_features == 3 * hidden_dim + assert mmdit_attention.input_proj_out.out_features == input_dim + assert mmdit_attention.context_proj_out.out_features == context_dim + + def test_forward_shape( + self, + mmdit_attention: MMDiTAttention, + batch_size: int, + seq_len: int, + context_seq_len: int, + input_dim: int, + context_dim: int, + ): + """ + Test MMDiTAttention forward pass output shapes. + + Verifies that the multi-modal attention produces output tensors with correct + shapes for both input and context, maintaining their respective dimensions. + + Args: + mmdit_attention (MMDiTAttention): The multi-modal attention module to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + context_seq_len (int): Length of the context sequence. + input_dim (int): Dimension of input features. + context_dim (int): Dimension of context features. + + Raises: + AssertionError: If output shapes don't match expected dimensions. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim) + context_tensor = torch.randn(batch_size, context_seq_len, context_dim) + + input_output, context_output = cast( + tuple[torch.Tensor, torch.Tensor], mmdit_attention(input_tensor, context_tensor) + ) + + assert input_output.shape == (batch_size, seq_len, input_dim) + assert context_output.shape == (batch_size, context_seq_len, context_dim) + + def test_forward_different_dimensions(self, batch_size: int, seq_len: int): + """ + Test MMDiTAttention with different input and context dimensions. + + Validates that the multi-modal attention can handle different dimensional inputs + for context and input tensors, which is common in multi-modal scenarios where + different modalities may have different feature dimensions. + + Args: + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + + Raises: + AssertionError: If output shapes don't match input tensor shapes. + """ + input_dim, context_dim = 256, 512 + hidden_dim = 384 + num_heads = 6 + + attention = MMDiTAttention( + context_dim=context_dim, + input_dim=input_dim, + dim=hidden_dim, + num_heads=num_heads, + ) + + input_tensor = torch.randn(batch_size, seq_len, input_dim) + context_tensor = torch.randn(batch_size, seq_len + 5, context_dim) + + input_output, context_output = cast(tuple[torch.Tensor, torch.Tensor], attention(input_tensor, context_tensor)) + + assert input_output.shape == input_tensor.shape + assert context_output.shape == context_tensor.shape + + def test_gradient_flow( + self, + mmdit_attention: MMDiTAttention, + batch_size: int, + seq_len: int, + context_seq_len: int, + input_dim: int, + context_dim: int, + ): + """ + Test gradient flow through MMDiTAttention. + + Ensures that gradients can properly flow backward through the multi-modal attention + mechanism for both input and context paths, which is essential for training. + + Args: + mmdit_attention (MMDiTAttention): The multi-modal attention module to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + context_seq_len (int): Length of the context sequence. + input_dim (int): Dimension of input features. + context_dim (int): Dimension of context features. + + Raises: + AssertionError: If gradients are not computed for input or context tensors. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim, requires_grad=True) + context_tensor = torch.randn(batch_size, context_seq_len, context_dim, requires_grad=True) + + input_output, context_output = cast( + tuple[torch.Tensor, torch.Tensor], mmdit_attention(input_tensor, context_tensor) + ) + loss = input_output.sum() + context_output.sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert input_tensor.grad is not None + assert context_tensor.grad is not None + + +class TestDiTBlock: + """ + Test class for DiTBlock module. + + This class provides comprehensive testing for the DiTBlock transformer block, + which combines modulation, normalization, self-attention, and MLP operations + for simple diffusion transformers (without multi-modal capabilities). + + Attributes: + None + + Methods: + dit_block: Fixture that creates a DiTBlock instance for testing. + test_init: Tests proper initialization of all DiTBlock components. + test_forward_shape: Tests output shape consistency in forward pass. + test_modulation_calls: Tests that modulation is applied correctly. + test_gradient_flow: Tests gradient propagation through the transformer block. + """ + + @pytest.fixture + def dit_block(self, input_dim: int, hidden_dim: int, embedding_dim: int, num_heads: int, mlp_ratio: int): + """ + Create DiTBlock instance for testing. + + Args: + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + + Returns: + DiTBlock: Configured transformer block for testing. + """ + return DiTBlock( + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + partial_rotary_factor=1.0, + ) + + def test_init( + self, dit_block: DiTBlock, input_dim: int, hidden_dim: int, embedding_dim: int, num_heads: int, mlp_ratio: int + ): + """ + Test DiTBlock initialization. + + Verifies that all components of the transformer block are properly initialized, + including modulation layers, normalization layers, attention mechanism, and MLP. + Validates that MLP dimensions are correctly computed based on the mlp_ratio. + + Args: + dit_block (DiTBlock): The transformer block to test. + input_dim (int): Expected input dimension. + hidden_dim (int): Expected hidden dimension. + embedding_dim (int): Expected embedding dimension. + num_heads (int): Expected number of attention heads. + mlp_ratio (int): Expected MLP expansion ratio. + + Raises: + AssertionError: If any component is not initialized correctly or has wrong dimensions. + """ + assert isinstance(dit_block.modulation, nn.Module) + assert isinstance(dit_block.norm_1, nn.RMSNorm) + assert isinstance(dit_block.attention, DiTAttention) + assert isinstance(dit_block.norm_2, nn.RMSNorm) + assert len(dit_block.mlp_input) == 3 # Linear, GELU, Linear + + # Check MLP dimensions + assert dit_block.mlp_input[0].in_features == input_dim + assert dit_block.mlp_input[0].out_features == mlp_ratio * input_dim + assert dit_block.mlp_input[2].in_features == mlp_ratio * input_dim + assert dit_block.mlp_input[2].out_features == input_dim + + def test_forward_shape( + self, dit_block: DiTBlock, batch_size: int, seq_len: int, input_dim: int, embedding_dim: int + ): + """ + Test DiTBlock forward pass output shape. + + Verifies that the transformer block maintains input tensor shape and data type + throughout the forward pass, ensuring proper integration with the overall model. + + Args: + dit_block (DiTBlock): The transformer block to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of input features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If output shape or data type doesn't match input. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim) + y = torch.randn(batch_size, embedding_dim) + + output: torch.Tensor = dit_block(input_tensor, y) + + assert output.shape == input_tensor.shape + assert output.dtype == input_tensor.dtype + + @patch("diffulab.networks.denoisers.mmdit.modulate") + def test_modulation_calls( + self, + mock_modulate: MagicMock, + dit_block: DiTBlock, + batch_size: int, + seq_len: int, + input_dim: int, + embedding_dim: int, + ): + """ + Test that modulation is called correctly. + + Ensures that the modulate function is called the expected number of times + during the forward pass, which is crucial for proper conditioning in DiT models. + Uses mocking to verify the modulation behavior without relying on actual computations. + + Args: + mock_modulate (MagicMock): Mock object for the modulate function. + dit_block (DiTBlock): The transformer block to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of input features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If modulate is not called the expected number of times (twice). + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim) + y = torch.randn(batch_size, embedding_dim) + + # Mock modulate to return input unchanged + mock_modulate.return_value = input_tensor + + dit_block(input_tensor, y) + + # Should be called twice (for attention and MLP) + assert mock_modulate.call_count == 2 + + def test_gradient_flow( + self, dit_block: DiTBlock, batch_size: int, seq_len: int, input_dim: int, embedding_dim: int + ): + """ + Test gradient flow through DiTBlock. + + Ensures that gradients can properly flow backward through the transformer block + for both input tensors and conditioning embeddings, which is essential for training. + + Args: + dit_block (DiTBlock): The transformer block to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of input features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If gradients are not computed for input tensor or embeddings. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim, requires_grad=True) + y = torch.randn(batch_size, embedding_dim, requires_grad=True) + + output: torch.Tensor = dit_block(input_tensor, y) + loss = output.sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert input_tensor.grad is not None + assert y.grad is not None + + +class TestMMDiTBlock: + """ + Test class for MMDiTBlock module. + + This class provides comprehensive testing for the MMDiTBlock multi-modal transformer block, + which combines modulation, normalization, multi-modal attention, and MLP operations + for processing both input and context tensors in multi-modal diffusion transformers. + + Attributes: + None + + Methods: + mmdit_block: Fixture that creates an MMDiTBlock instance for testing. + test_init: Tests proper initialization of all MMDiTBlock components. + test_forward_shape: Tests output shapes for both input and context tensors. + test_residual_connections: Tests that residual connections work correctly. + test_gradient_flow: Tests gradient propagation through the multi-modal block. + """ + + @pytest.fixture + def mmdit_block( + self, context_dim: int, input_dim: int, hidden_dim: int, embedding_dim: int, num_heads: int, mlp_ratio: int + ): + """ + Create MMDiTBlock instance for testing. + + Args: + context_dim (int): Dimension of the context features. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + + Returns: + MMDiTBlock: Configured multi-modal transformer block for testing. + """ + return MMDiTBlock( + context_dim=context_dim, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + partial_rotary_factor=1.0, + ) + + def test_init(self, mmdit_block: MMDiTBlock, context_dim: int, input_dim: int, mlp_ratio: int): + """ + Test MMDiTBlock initialization. + + Verifies that all components for multi-modal processing are properly initialized, + including separate modulation paths for input and context, multi-modal attention, + and correctly sized MLP layers for both input and context processing. + + Args: + mmdit_block (MMDiTBlock): The multi-modal transformer block to test. + context_dim (int): Expected context dimension. + input_dim (int): Expected input dimension. + mlp_ratio (int): Expected MLP expansion ratio. + + Raises: + AssertionError: If any component is not initialized correctly or has wrong dimensions. + """ + assert isinstance(mmdit_block.modulation_context, nn.Module) + assert isinstance(mmdit_block.modulation_input, nn.Module) + assert isinstance(mmdit_block.attention, MMDiTAttention) + + # Check MLP dimensions + assert mmdit_block.mlp_context[0].in_features == context_dim + assert mmdit_block.mlp_context[0].out_features == mlp_ratio * context_dim + assert mmdit_block.mlp_input[0].in_features == input_dim + assert mmdit_block.mlp_input[0].out_features == mlp_ratio * input_dim + + def test_forward_shape( + self, + mmdit_block: MMDiTBlock, + batch_size: int, + seq_len: int, + context_seq_len: int, + input_dim: int, + context_dim: int, + embedding_dim: int, + ): + """ + Test MMDiTBlock forward pass output shapes. + + Verifies that the multi-modal transformer block maintains correct shapes for both + input and context tensors throughout the forward pass, ensuring proper integration + with the overall multi-modal architecture. + + Args: + mmdit_block (MMDiTBlock): The multi-modal transformer block to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + context_seq_len (int): Length of the context sequence. + input_dim (int): Dimension of input features. + context_dim (int): Dimension of context features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If output shapes don't match input tensor shapes. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim) + context_tensor = torch.randn(batch_size, context_seq_len, context_dim) + y = torch.randn(batch_size, embedding_dim) + + input_output, context_output = cast( + tuple[torch.Tensor, torch.Tensor], mmdit_block(input_tensor, y, context_tensor) + ) + assert input_output.shape == input_tensor.shape + assert context_output.shape == context_tensor.shape + + def test_residual_connections( + self, + mmdit_block: MMDiTBlock, + batch_size: int, + seq_len: int, + context_seq_len: int, + input_dim: int, + context_dim: int, + embedding_dim: int, + ): + """ + Test that residual connections work correctly. + + Verifies that the multi-modal transformer block implements proper residual connections + for both input and context paths, ensuring that outputs are modified versions of inputs + rather than completely independent transformations. + + Args: + mmdit_block (MMDiTBlock): The multi-modal transformer block to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + context_seq_len (int): Length of the context sequence. + input_dim (int): Dimension of input features. + context_dim (int): Dimension of context features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If outputs are identical to inputs (no processing) or + if residual connections are not working properly. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim) + context_tensor = torch.randn(batch_size, context_seq_len, context_dim) + y = torch.randn(batch_size, embedding_dim) + + # Save original tensors + input_orig = input_tensor.clone() + context_orig = context_tensor.clone() + + input_output, context_output = mmdit_block(input_tensor, y, context_tensor) + + # Outputs should be different from inputs due to residual connections + assert not torch.allclose(input_output, input_orig) + assert not torch.allclose(context_output, context_orig) + + def test_gradient_flow( + self, + mmdit_block: MMDiTBlock, + batch_size: int, + seq_len: int, + context_seq_len: int, + input_dim: int, + context_dim: int, + embedding_dim: int, + ): + """ + Test gradient flow through MMDiTBlock. + + Ensures that gradients can properly flow backward through the multi-modal transformer + block for input tensors, context tensors, and conditioning embeddings, which is + essential for training multi-modal diffusion models. + + Args: + mmdit_block (MMDiTBlock): The multi-modal transformer block to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + context_seq_len (int): Length of the context sequence. + input_dim (int): Dimension of input features. + context_dim (int): Dimension of context features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If gradients are not computed for any of the input tensors. + """ + input_tensor = torch.randn(batch_size, seq_len, input_dim, requires_grad=True) + context_tensor = torch.randn(batch_size, context_seq_len, context_dim, requires_grad=True) + y = torch.randn(batch_size, embedding_dim, requires_grad=True) + + input_output, context_output = cast( + tuple[torch.Tensor, torch.Tensor], mmdit_block(input_tensor, y, context_tensor) + ) + loss = input_output.sum() + context_output.sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert input_tensor.grad is not None + assert context_tensor.grad is not None + assert y.grad is not None + + +class TestModulatedLastLayer: + """ + Test class for ModulatedLastLayer module. + + This class provides comprehensive testing for the ModulatedLastLayer, which is the final + output layer of the diffusion transformer that converts hidden features back to pixel space + using adaptive layer normalization based on conditioning embeddings. + + Attributes: + None + + Methods: + last_layer: Fixture that creates a ModulatedLastLayer instance for testing. + test_init: Tests proper initialization of the output layer components. + test_forward_shape: Tests output shape matches expected patch dimensions. + test_gradient_flow: Tests gradient propagation through the output layer. + """ + + @pytest.fixture + def last_layer(self, embedding_dim: int, input_dim: int, patch_size: int, input_channels: int): + """ + Create ModulatedLastLayer instance for testing. + + Args: + embedding_dim (int): Dimension of the conditioning embeddings. + input_dim (int): Dimension of the hidden features. + patch_size (int): Size of image patches. + input_channels (int): Number of output channels. + + Returns: + ModulatedLastLayer: Configured output layer for testing. + """ + return ModulatedLastLayer( + embedding_dim=embedding_dim, + hidden_size=input_dim, + patch_size=patch_size, + out_channels=input_channels, + ) + + def test_init( + self, last_layer: ModulatedLastLayer, input_dim: int, patch_size: int, input_channels: int, embedding_dim: int + ): + """ + Test ModulatedLastLayer initialization. + + Verifies that the output layer is properly initialized with correct normalization, + linear projection, and adaptive layer normalization components. Validates that + output dimensions match the expected patch and channel configuration. + + Args: + last_layer (ModulatedLastLayer): The output layer to test. + input_dim (int): Expected hidden feature dimension. + patch_size (int): Expected patch size. + input_channels (int): Expected number of output channels. + embedding_dim (int): Expected embedding dimension. + + Raises: + AssertionError: If any component is not initialized correctly or has wrong dimensions. + """ + assert isinstance(last_layer.norm_final, nn.LayerNorm) + assert last_layer.norm_final.normalized_shape == (input_dim,) + assert not last_layer.norm_final.elementwise_affine + + expected_out_features = patch_size * patch_size * input_channels + assert last_layer.linear.out_features == expected_out_features + assert last_layer.linear.in_features == input_dim + + # Check adaLN_modulation + assert len(last_layer.adaLN_modulation) == 2 + assert last_layer.adaLN_modulation[1].out_features == 2 * input_dim + + def test_forward_shape( + self, + last_layer: ModulatedLastLayer, + batch_size: int, + seq_len: int, + input_dim: int, + embedding_dim: int, + patch_size: int, + input_channels: int, + ): + """ + Test ModulatedLastLayer forward pass output shape. + + Verifies that the output layer produces tensors with the correct shape for + reconstruction into image patches, ensuring proper dimensionality for the + unpatchify operation in the main model. + + Args: + last_layer (ModulatedLastLayer): The output layer to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of hidden features. + embedding_dim (int): Dimension of conditioning embeddings. + patch_size (int): Size of image patches. + input_channels (int): Number of output channels. + + Raises: + AssertionError: If output shape doesn't match expected patch dimensions. + """ + x = torch.randn(batch_size, seq_len, input_dim) + vec = torch.randn(batch_size, embedding_dim) + + output: torch.Tensor = last_layer(x, vec) + + expected_features = patch_size * patch_size * input_channels + assert output.shape == (batch_size, seq_len, expected_features) + + def test_gradient_flow( + self, last_layer: ModulatedLastLayer, batch_size: int, seq_len: int, input_dim: int, embedding_dim: int + ): + """ + Test gradient flow through ModulatedLastLayer. + + Ensures that gradients can properly flow backward through the output layer + for both hidden features and conditioning embeddings, which is essential + for end-to-end training of the diffusion model. + + Args: + last_layer (ModulatedLastLayer): The output layer to test. + batch_size (int): Size of the input batch. + seq_len (int): Length of the input sequence. + input_dim (int): Dimension of hidden features. + embedding_dim (int): Dimension of conditioning embeddings. + + Raises: + AssertionError: If gradients are not computed for input features or embeddings. + """ + x = torch.randn(batch_size, seq_len, input_dim, requires_grad=True) + vec = torch.randn(batch_size, embedding_dim, requires_grad=True) + + output: torch.Tensor = last_layer(x, vec) + loss = output.sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert x.grad is not None + assert vec.grad is not None + + +class TestMMDiT: + """ + Test class for MMDiT module. + + This class provides comprehensive testing for the main MMDiT (Multimodal Diffusion Transformer) + model, which supports both simple DiT and multi-modal configurations. Tests cover initialization, + forward passes, patchification/unpatchification, different configurations, and error conditions. + + Attributes: + None + + Methods: + simple_mmdit: Fixture that creates a simple DiT configuration for testing. + mmdit_with_context: Fixture that creates a multi-modal DiT configuration for testing. + test_simple_dit_init: Tests initialization of simple DiT configuration. + test_mmdit_with_context_init: Tests initialization of multi-modal configuration. + test_invalid_init_both_classes_and_context: Tests error when conflicting configs provided. + test_invalid_init_mmdit_without_context: Tests error when context embedder missing. + test_patchify_unpatchify_roundtrip: Tests image-to-patches-to-image conversion. + test_simple_dit_forward: Tests forward pass for simple DiT mode. + test_mmdit_forward_with_context: Tests forward pass for multi-modal mode. + test_forward_with_intermediate_features: Tests intermediate feature extraction. + test_forward_with_x_context: Tests input concatenation with context. + test_forward_classifier_free_guidance: Tests classifier-free guidance probability. + test_forward_invalid_both_context_and_labels: Tests error with conflicting inputs. + test_forward_invalid_p_without_classifier_free: Tests error with invalid guidance config. + test_gradient_flow_simple_dit: Tests gradient flow in simple DiT mode. + test_gradient_flow_mmdit_with_context: Tests gradient flow in multi-modal mode. + test_different_output_channels: Tests model with different output channel counts. + test_different_depths: Tests model with various transformer depths. + test_different_patch_sizes: Tests model with various patch sizes. + test_weight_initialization: Tests proper weight initialization. + """ + + @pytest.fixture + def simple_mmdit( + self, + input_channels: int, + input_dim: int, + hidden_dim: int, + embedding_dim: int, + num_heads: int, + mlp_ratio: int, + patch_size: int, + ): + """ + Create simple MMDiT instance for testing. + + Args: + input_channels (int): Number of input image channels. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + patch_size (int): Size of image patches. + + Returns: + MMDiT: Configured simple DiT model for testing. + """ + return MMDiT( + simple_dit=True, + input_channels=input_channels, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + patch_size=patch_size, + depth=2, # Small depth for testing + n_classes=10, + classifier_free=True, + ) + + @pytest.fixture + def mmdit_with_context( + self, + input_channels: int, + input_dim: int, + hidden_dim: int, + embedding_dim: int, + num_heads: int, + mlp_ratio: int, + patch_size: int, + context_dim: int, + mock_context_embedder: ContextEmbedder, + ): + """ + Create MMDiT with context embedder for testing. + + Args: + input_channels (int): Number of input image channels. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + patch_size (int): Size of image patches. + context_dim (int): Dimension of context features. + mock_context_embedder (ContextEmbedder): Mock context embedder for testing. + + Returns: + MMDiT: Configured multi-modal DiT model for testing. + """ + return MMDiT( + simple_dit=False, + input_channels=input_channels, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + patch_size=patch_size, + depth=2, # Small depth for testing + context_dim=context_dim, + context_embedder=mock_context_embedder, + ) + + def test_simple_dit_init(self, simple_mmdit: MMDiT, input_channels: int, patch_size: int): + """ + Test simple MMDiT initialization. + + Verifies that the simple DiT configuration is properly initialized with correct + parameters, including class conditioning setup and absence of context embedder. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to test. + input_channels (int): Expected number of input channels. + patch_size (int): Expected patch size. + + Raises: + AssertionError: If any initialization parameter is incorrect. + """ + assert simple_mmdit.simple_dit is True + assert simple_mmdit.input_channels == input_channels + assert simple_mmdit.output_channels == input_channels # Should default to input_channels + assert simple_mmdit.patch_size == patch_size + assert simple_mmdit.n_classes == 10 + assert simple_mmdit.classifier_free is True + assert simple_mmdit.context_embedder is None + assert simple_mmdit.label_embed is not None + + def test_mmdit_with_context_init( + self, mmdit_with_context: MMDiT, input_channels: int, patch_size: int, mock_context_embedder: ContextEmbedder + ): + """ + Test MMDiT with context embedder initialization. + + Verifies that the multi-modal DiT configuration is properly initialized with + context embedder setup and absence of class conditioning components. + + Args: + mmdit_with_context (MMDiT): Multi-modal DiT model instance to test. + input_channels (int): Expected number of input channels. + patch_size (int): Expected patch size. + mock_context_embedder (ContextEmbedder): Expected context embedder instance. + + Raises: + AssertionError: If any initialization parameter is incorrect or missing components. + """ + assert mmdit_with_context.simple_dit is False + assert mmdit_with_context.context_embedder is mock_context_embedder + assert mmdit_with_context.label_embed is None + assert hasattr(mmdit_with_context, "mlp_pooled_context") + assert hasattr(mmdit_with_context, "context_embed") + + def test_invalid_init_both_classes_and_context(self, mock_context_embedder: ContextEmbedder): + """ + Test that MMDiT raises error when both n_classes and context_embedder are provided. + + Ensures that the model correctly validates initialization parameters and prevents + conflicting configurations that would be ambiguous during training. + + Args: + mock_context_embedder (ContextEmbedder): Mock context embedder for testing. + + Raises: + AssertionError: When both n_classes and context_embedder are specified, which is invalid. + """ + with pytest.raises(AssertionError, match="n_classes and context_embedder cannot both be specified"): + MMDiT( + n_classes=10, + context_embedder=mock_context_embedder, + ) + + def test_invalid_init_mmdit_without_context(self): + """ + Test that MMDiT raises error when not simple_dit but no context_embedder. + + Ensures that multi-modal DiT configurations require a context embedder, + preventing incomplete initialization that would cause runtime errors. + + Raises: + AssertionError: When simple_dit=False but no context_embedder is provided. + """ + with pytest.raises(AssertionError, match="for MMDiT context embedder must be provided"): + MMDiT(simple_dit=False) + + def test_patchify_unpatchify_roundtrip(self, simple_mmdit: MMDiT, sample_image: torch.Tensor): + """ + Test that patchify and unpatchify are inverse operations. + + Verifies that the image-to-patches conversion and patches-to-image reconstruction + maintain consistent shapes and allow proper processing through the transformer. + This is crucial for the diffusion model's ability to process images as sequences. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance for testing. + sample_image (torch.Tensor): Sample input image tensor. + + Raises: + AssertionError: If patch dimensions or reconstruction shapes are incorrect. + """ + # Patchify + patches = simple_mmdit.patchify(sample_image) + + # Check patchify output shape + batch_size, _, height, width = sample_image.shape + expected_num_patches = (height // simple_mmdit.patch_size) * (width // simple_mmdit.patch_size) + assert patches.shape == (batch_size, expected_num_patches, simple_mmdit.conv_proj.out_channels) + + # Create dummy output with correct channel dimension + dummy_output = torch.randn( + batch_size, + expected_num_patches, + simple_mmdit.patch_size * simple_mmdit.patch_size * simple_mmdit.output_channels, + ) + + # Unpatchify + reconstructed = simple_mmdit.unpatchify(dummy_output) + + # Check reconstructed shape + assert reconstructed.shape == sample_image.shape + + def test_simple_dit_forward( + self, + simple_mmdit: MMDiT, + sample_image: torch.Tensor, + sample_timesteps: torch.Tensor, + sample_labels: torch.Tensor, + ): + """ + Test simple DiT forward pass. + + Verifies that the simple DiT configuration can successfully process images with + class labels and timesteps, producing outputs with correct shapes and data types. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + sample_labels (torch.Tensor): Sample class label tensor. + + Raises: + AssertionError: If output shape, keys, or data type are incorrect. + """ + output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels) + + assert "x" in output + assert output["x"].shape == sample_image.shape + assert output["x"].dtype == sample_image.dtype + + def test_mmdit_forward_with_context( + self, mmdit_with_context: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor + ): + """ + Test MMDiT forward pass with context. + + Verifies that the multi-modal DiT configuration can successfully process images + with context information, producing outputs with correct shapes. + + Args: + mmdit_with_context (MMDiT): Multi-modal DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + + Raises: + AssertionError: If output shape or keys are incorrect. + """ + initial_context = "test context" # Mock context + output: ModelOutput = mmdit_with_context(sample_image, sample_timesteps, initial_context=initial_context) + + assert "x" in output + assert output["x"].shape == sample_image.shape + + def test_forward_with_intermediate_features( + self, + simple_mmdit: MMDiT, + sample_image: torch.Tensor, + sample_timesteps: torch.Tensor, + sample_labels: torch.Tensor, + ): + """ + Test forward pass with intermediate features enabled. + + Verifies that the model can output intermediate layer features in addition to + the final output, which is useful for analysis, visualization, and feature + extraction applications. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + sample_labels (torch.Tensor): Sample class label tensor. + + Raises: + AssertionError: If intermediate features are not returned or have incorrect format. + """ + # Need to access the internal forward methods + patches = simple_mmdit.patchify(sample_image) + output = simple_mmdit.simple_dit_forward(patches, sample_timesteps, y=sample_labels, intermediate_features=True) + + assert "x" in output + assert "features" in output + assert isinstance(output["features"], list) + assert len(output["features"]) == simple_mmdit.layers.__len__() + 1 # +1 for last layer + + def test_forward_with_x_context( + self, + simple_mmdit: MMDiT, + sample_image: torch.Tensor, + sample_timesteps: torch.Tensor, + sample_labels: torch.Tensor, + ): + """ + Test forward pass with x_context concatenation. + + Verifies that the model can handle additional context images concatenated + to the input, which is useful for conditional generation scenarios. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + sample_labels (torch.Tensor): Sample class label tensor. + + Raises: + AssertionError: If output shape is incorrect when using x_context. + """ + x_context = torch.randn_like(sample_image) + output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels, x_context=x_context) + + assert "x" in output + assert output["x"].shape == sample_image.shape + + def test_forward_classifier_free_guidance( + self, + simple_mmdit: MMDiT, + sample_image: torch.Tensor, + sample_timesteps: torch.Tensor, + sample_labels: torch.Tensor, + ): + """ + Test forward pass with classifier-free guidance probability. + + Verifies that the model can handle classifier-free guidance dropout probability, + which is used during training to enable guidance-free generation at inference. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + sample_labels (torch.Tensor): Sample class label tensor. + + Raises: + AssertionError: If output shape is incorrect when using guidance probability. + """ + output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels, p=0.1) + + assert "x" in output + assert output["x"].shape == sample_image.shape + + def test_forward_invalid_both_context_and_labels( + self, + mmdit_with_context: MMDiT, + sample_image: torch.Tensor, + sample_timesteps: torch.Tensor, + sample_labels: torch.Tensor, + ): + """ + Test that forward raises error when both initial_context and y are provided. + + Ensures that the model prevents ambiguous conditioning scenarios where both + context embeddings and class labels are provided simultaneously. + + Args: + mmdit_with_context (MMDiT): Multi-modal DiT model instance. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + sample_labels (torch.Tensor): Sample class label tensor. + + Raises: + AssertionError: When both initial_context and y parameters are specified. + """ + with pytest.raises(AssertionError, match="initial_context and y cannot both be specified"): + mmdit_with_context(sample_image, sample_timesteps, initial_context="test", y=sample_labels) + + def test_forward_invalid_p_without_classifier_free(self, input_channels: int): + """ + Test that forward raises error when p > 0 but classifier_free is False. + + Ensures that classifier-free guidance dropout probability can only be used + when the model is properly configured for classifier-free guidance training. + + Args: + input_channels (int): Number of input image channels. + + Raises: + AssertionError: When p > 0 but classifier_free=False in model configuration. + """ + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + n_classes=10, + classifier_free=False, + depth=1, + ) + + sample_image = torch.randn(2, input_channels, 64, 64) + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + with pytest.raises(AssertionError, match="probability of dropping for classifier free guidance"): + model(sample_image, sample_timesteps, y=sample_labels, p=0.1) + + def test_gradient_flow_simple_dit( + self, + simple_mmdit: MMDiT, + sample_image: torch.Tensor, + sample_timesteps: torch.Tensor, + sample_labels: torch.Tensor, + ): + """ + Test gradient flow through simple DiT. + + Ensures that gradients can properly flow backward through the simple DiT + configuration for both image inputs and timesteps, which is essential + for training the diffusion model. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + sample_labels (torch.Tensor): Sample class label tensor. + + Raises: + AssertionError: If gradients are not computed for inputs or timesteps. + """ + sample_image.requires_grad_(True) + sample_timesteps.requires_grad_(True) + + output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels) + loss = output["x"].sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert sample_image.grad is not None + assert sample_timesteps.grad is not None + + def test_gradient_flow_mmdit_with_context( + self, mmdit_with_context: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor + ): + """ + Test gradient flow through MMDiT with context. + + Ensures that gradients can properly flow backward through the multi-modal DiT + configuration for both image inputs and timesteps, which is essential for + training multi-modal diffusion models. + + Args: + mmdit_with_context (MMDiT): Multi-modal DiT model instance to test. + sample_image (torch.Tensor): Sample input image tensor. + sample_timesteps (torch.Tensor): Sample timestep tensor. + + Raises: + AssertionError: If gradients are not computed for inputs or timesteps. + """ + sample_image.requires_grad_(True) + sample_timesteps.requires_grad_(True) + + output: ModelOutput = mmdit_with_context(sample_image, sample_timesteps, initial_context="test") + loss = output["x"].sum() + loss.backward() # type: ignore[reportUnknownMemberType] + + assert sample_image.grad is not None + assert sample_timesteps.grad is not None + + def test_different_output_channels(self, input_channels: int, patch_size: int): + """ + Test MMDiT with different output channels. + + Validates that the model can be configured with a different number of output + channels than input channels, which is useful for various generation tasks. + + Args: + input_channels (int): Number of input image channels. + patch_size (int): Size of image patches. + + Raises: + AssertionError: If model doesn't handle different output channels correctly + or if output shape is wrong. + """ + output_channels = input_channels * 2 + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + output_channels=output_channels, + n_classes=10, + patch_size=patch_size, + depth=1, + ) + + assert model.output_channels == output_channels + + sample_image = torch.randn(2, input_channels, 64, 64) + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + output = model(sample_image, sample_timesteps, y=sample_labels) + expected_shape = (2, output_channels, 64, 64) + assert output["x"].shape == expected_shape + + @pytest.mark.parametrize("depth", [1, 2, 4, 8]) + def test_different_depths(self, depth: int, input_channels: int): + """ + Test MMDiT with different depths. + + Validates that the model can be configured with various numbers of transformer + layers and that each configuration produces valid outputs. This tests the + model's scalability and ensures deep configurations work correctly. + + Args: + depth (int): Number of transformer layers to test (1, 2, 4, or 8). + input_channels (int): Number of input image channels. + + Raises: + AssertionError: If the model doesn't have the expected number of layers + or if forward pass fails with the given depth. + """ + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + n_classes=10, + depth=depth, + ) + + assert len(model.layers) == depth + + sample_image = torch.randn(2, input_channels, 64, 64) + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) + assert output["x"].shape == sample_image.shape + + @pytest.mark.parametrize("patch_size", [8, 16, 32]) + def test_different_patch_sizes(self, patch_size: int, input_channels: int): + """ + Test MMDiT with different patch sizes. + + Validates that the model can handle various patch sizes for image tokenization, + which affects the sequence length and computational requirements of the model. + + Args: + patch_size (int): Size of image patches to test (8, 16, or 32). + input_channels (int): Number of input image channels. + + Raises: + AssertionError: If the model doesn't work correctly with the given patch size + or if output shape is incorrect. + """ + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + patch_size=patch_size, + n_classes=10, + depth=1, + ) + + # Image size must be divisible by patch size + image_size = patch_size * 4 + sample_image = torch.randn(2, input_channels, image_size, image_size) + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + output = model(sample_image, sample_timesteps, y=sample_labels) + assert output["x"].shape == sample_image.shape + + def test_weight_initialization(self, simple_mmdit: MMDiT): + """ + Test that weights are properly initialized. + + Ensures that all Linear and Conv2d layers have been properly initialized using + Xavier uniform initialization for weights and zero initialization for biases, + which is important for stable training convergence. + + Args: + simple_mmdit (MMDiT): Simple DiT model instance to check. + + Raises: + AssertionError: If any weights are improperly initialized (all zeros) or + if biases are not initialized to zero. + """ + for module in simple_mmdit.modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Check that weights are not all zeros (indicating initialization occurred) + assert not torch.allclose(module.weight, torch.zeros_like(module.weight)) + if module.bias is not None: + # Biases should be initialized to zero + assert torch.allclose(module.bias, torch.zeros_like(module.bias)) + + +class TestMMDiTEdgeCases: + """ + Test edge cases and error conditions for MMDiT. + + This class focuses on testing boundary conditions, edge cases, and error scenarios + that might occur in real-world usage of the MMDiT model. These tests ensure + robustness and proper error handling across various input conditions. + + Attributes: + None + + Methods: + test_zero_batch_size: Tests behavior with empty batch tensors. + test_non_square_images: Tests processing of non-square image inputs. + test_single_head_attention: Tests model with single attention head configuration. + test_very_small_dimensions: Tests model with minimal dimension settings. + """ + + def test_zero_batch_size(self, input_channels: int): + """ + Test behavior with zero batch size. + + Verifies that the model can handle empty batches gracefully without crashing, + which is important for robust deployment and edge case handling. + + Args: + input_channels (int): Number of input image channels. + + Raises: + AssertionError: If the model doesn't handle empty batches correctly or + if output batch dimension is not zero. + """ + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + n_classes=10, + depth=1, + ) + + # Empty tensor + sample_image = torch.empty((0, input_channels, 64, 64)) + sample_timesteps = torch.empty((0,)) + sample_labels = torch.empty((0,), dtype=torch.long) + + # Should handle empty batch gracefully + output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) + assert output["x"].shape[0] == 0 + + def test_non_square_images(self, input_channels: int): + """ + Test model behavior with non-square image inputs. + + Validates that the MMDiT model can correctly process rectangular images + with different width and height dimensions, which is important for handling + diverse image aspect ratios in real-world applications. + + Args: + input_channels (int): Number of input image channels. + + Raises: + AssertionError: If the model fails to process non-square images correctly + or if output dimensions don't match expected values. + ValueError: If the patch size doesn't divide evenly into the image dimensions. + """ + patch_size = 16 + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + patch_size=patch_size, + n_classes=10, + depth=1, + ) + + # Non-square but divisible by patch size + sample_image = torch.randn(2, input_channels, 64, 128) # 64x128 + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) + assert output["x"].shape == sample_image.shape + + def test_single_head_attention(self, input_channels: int): + """ + Test model with single attention head configuration. + + Verifies that the MMDiT model works correctly with the minimal attention + head configuration of one head, which can be useful for smaller models + or resource-constrained environments. + + Args: + input_channels (int): Number of input image channels. + + Raises: + AssertionError: If the model fails with single head attention or + if output shape is incorrect. + """ + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + num_heads=1, + n_classes=10, + depth=1, + ) + + sample_image = torch.randn(2, input_channels, 64, 64) + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) + assert output["x"].shape == sample_image.shape + + def test_very_small_dimensions(self, input_channels: int): + """ + Test model with very small dimension configurations. + + Validates that the MMDiT model can function with minimal dimension settings, + which is important for testing computational limits and ensuring the model + works across various configuration sizes. + + Args: + input_channels (int): Number of input image channels. + + Raises: + AssertionError: If the model fails with small dimensions or + if output shape is incorrect. + ValueError: If the dimension configuration is invalid or incompatible. + """ + model = MMDiT( + simple_dit=True, + input_channels=input_channels, + input_dim=64, + hidden_dim=64, + embedding_dim=64, + num_heads=2, + n_classes=10, + depth=1, + ) + + sample_image = torch.randn(2, input_channels, 32, 32) + sample_timesteps = torch.randn(2) + sample_labels = torch.randint(0, 10, (2,)) + + output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) + assert output["x"].shape == sample_image.shape From 1b5d1a495a3a58c2d92fbbc1b9ed4833f4ca66d9 Mon Sep 17 00:00:00 2001 From: LouisRouss Date: Sat, 9 Aug 2025 18:30:00 +0200 Subject: [PATCH 3/6] add unit test CI --- .github/workflows/ci.yaml | 1 - .github/workflows/unit_tests.yml | 67 ++++++++++++++++++++++++++++++++ 2 files changed, 67 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/unit_tests.yml diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 771ad45..9067a3b 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -30,7 +30,6 @@ jobs: uses: astral-sh/setup-uv@v4 with: enable-cache: true - cache-prefix: diffulab-uv-cache - name: "Set up Python" if: steps.changes.outputs.sources == 'true' diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml new file mode 100644 index 0000000..410c196 --- /dev/null +++ b/.github/workflows/unit_tests.yml @@ -0,0 +1,67 @@ +name: Unit Tests + +on: + push: + branches: [ main ] + paths: + - '**.py' + - 'pyproject.toml' + - 'uv.lock' + - '.github/workflows/unit_tests.yml' + - 'tests/**' + pull_request: + types: [opened, synchronize, reopened] + branches: [ main ] + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: unit-tests-${{ github.ref }} + cancel-in-progress: true + +jobs: + if: github.event.pull_request.draft == false + tests: + runs-on: ubuntu-latest + steps: + - name: checkout + uses: actions/checkout@v4 + + - name: check changes + uses: dorny/paths-filter@v3 + id: changes + with: + filters: | + sources: + - src/** + - tests/** + - .github/workflows/unit_tests.yml + + - name: Install uv + if: steps.changes.outputs.sources == 'true' + uses: astral-sh/setup-uv@v4 + with: + enable-cache: true + + - name: "Set up Python" + if: steps.changes.outputs.sources == 'true' + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" + + - name: Install the project + if: steps.changes.outputs.sources == 'true' + run: uv sync --all-extras + + - name: Run tests + if: steps.changes.outputs.sources == 'true' + run: uv run pytest --junitxml=results.xml + + - name: Upload test report + if: steps.changes.outputs.sources == 'true' + uses: actions/upload-artifact@v4 + with: + name: test-results + path: results.xml From 6de28b8d87e0c273e4bebd4a796ee58eebb5e518 Mon Sep 17 00:00:00 2001 From: LouisRouss Date: Sat, 9 Aug 2025 19:26:46 +0200 Subject: [PATCH 4/6] fix unit test and edge case for mmdit --- src/diffulab/networks/denoisers/mmdit.py | 18 +- .../networks/denoisers/test_mmdit.py | 191 +++++++----------- 2 files changed, 84 insertions(+), 125 deletions(-) diff --git a/src/diffulab/networks/denoisers/mmdit.py b/src/diffulab/networks/denoisers/mmdit.py index 29a6039..d1e8e20 100644 --- a/src/diffulab/networks/denoisers/mmdit.py +++ b/src/diffulab/networks/denoisers/mmdit.py @@ -457,6 +457,7 @@ def __init__( self.n_classes = n_classes self.classifier_free = classifier_free + self.label_embed: LabelEmbed | None = None 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" @@ -470,9 +471,8 @@ def __init__( ) self.context_embed = nn.Linear(self.context_embedder.output_size[1], context_dim) else: - self.label_embed = ( - LabelEmbed(self.n_classes, embedding_dim, self.classifier_free) if self.n_classes is not None else None - ) + if self.n_classes is not None: + self.label_embed = LabelEmbed(self.n_classes, embedding_dim, self.classifier_free) self.last_layer = ModulatedLastLayer( embedding_dim=embedding_dim, @@ -578,14 +578,14 @@ def mmdit_forward( # Pass through each layer sequentially for layer in self.layers: x, context = layer(x, context_pooled, context) - if features: + if features is not None: features.append(x) x = self.last_layer(x, context_pooled) - if features: + if features is not None: features.append(x) model_output: ModelOutput = {"x": x} - if features: + if features is not None: model_output["features"] = features return model_output @@ -610,14 +610,14 @@ def simple_dit_forward( # Pass through each layer sequentially for layer in self.layers: x = layer(x, emb) - if features: + if features is not None: features.append(x) x = self.last_layer(x, emb) - if features: + if features is not None: features.append(x) model_output: ModelOutput = {"x": x} - if features: + if features is not None: model_output["features"] = features return model_output diff --git a/tests/unit_tests/networks/denoisers/test_mmdit.py b/tests/unit_tests/networks/denoisers/test_mmdit.py index 989db9a..fa417e7 100644 --- a/tests/unit_tests/networks/denoisers/test_mmdit.py +++ b/tests/unit_tests/networks/denoisers/test_mmdit.py @@ -137,8 +137,7 @@ def test_forward_different_batch_sizes(self, dit_attention: DiTAttention, seq_le """ Test DiTAttention with different batch sizes. - Ensures the attention mechanism works correctly across various batch sizes, - which is important for both training and inference scenarios. + Ensures the attention mechanism works correctly across various batch sizes Args: dit_attention (DiTAttention): The attention module to test. @@ -157,9 +156,7 @@ def test_forward_different_seq_lengths(self, dit_attention: DiTAttention, batch_ """ Test DiTAttention with different sequence lengths. - Verifies that the attention mechanism handles variable sequence lengths correctly, - which is crucial for processing images of different sizes and patch configurations. - + Verifies that the attention mechanism handles variable sequence lengths correctly. Args: dit_attention (DiTAttention): The attention module to test. batch_size (int): Size of the input batch. @@ -178,8 +175,7 @@ def test_partial_rotary_factor(self, input_dim: int, hidden_dim: int, num_heads: Test DiTAttention with different partial rotary factors. Validates that the rotary embedding dimension is calculated correctly - based on the partial rotary factor, which controls how much of each - attention head uses rotary positional embeddings. + based on the partial rotary factor. Args: input_dim (int): Dimension of the input features. @@ -204,8 +200,7 @@ def test_gradient_flow(self, dit_attention: DiTAttention, batch_size: int, seq_l """ Test gradient flow through DiTAttention. - Ensures that gradients can properly flow backward through the attention mechanism, - which is essential for training the diffusion transformer model. + Ensures that gradients can properly flow backward through the attention mechanism. Args: dit_attention (DiTAttention): The attention module to test. @@ -229,9 +224,7 @@ class TestMMDiTAttention: """ Test class for MMDiTAttention module. - This class provides comprehensive testing for the MMDiTAttention multi-modal attention mechanism, - which processes both input and context tensors simultaneously with cross-attention and rotary - positional embeddings for multi-modal diffusion transformers. + This class provides comprehensive testing for the MMDiTAttention multi-modal attention mechanism. Attributes: None @@ -339,8 +332,7 @@ def test_forward_different_dimensions(self, batch_size: int, seq_len: int): Test MMDiTAttention with different input and context dimensions. Validates that the multi-modal attention can handle different dimensional inputs - for context and input tensors, which is common in multi-modal scenarios where - different modalities may have different feature dimensions. + for context and input tensors. Args: batch_size (int): Size of the input batch. @@ -381,7 +373,7 @@ def test_gradient_flow( Test gradient flow through MMDiTAttention. Ensures that gradients can properly flow backward through the multi-modal attention - mechanism for both input and context paths, which is essential for training. + mechanism for both input and context paths. Args: mmdit_attention (MMDiTAttention): The multi-modal attention module to test. @@ -524,7 +516,6 @@ def test_modulation_calls( Test that modulation is called correctly. Ensures that the modulate function is called the expected number of times - during the forward pass, which is crucial for proper conditioning in DiT models. Uses mocking to verify the modulation behavior without relying on actual computations. Args: @@ -556,7 +547,7 @@ def test_gradient_flow( Test gradient flow through DiTBlock. Ensures that gradients can properly flow backward through the transformer block - for both input tensors and conditioning embeddings, which is essential for training. + for both input tensors and conditioning embeddings. Args: dit_block (DiTBlock): The transformer block to test. @@ -594,7 +585,6 @@ class TestMMDiTBlock: mmdit_block: Fixture that creates an MMDiTBlock instance for testing. test_init: Tests proper initialization of all MMDiTBlock components. test_forward_shape: Tests output shapes for both input and context tensors. - test_residual_connections: Tests that residual connections work correctly. test_gradient_flow: Tests gradient propagation through the multi-modal block. """ @@ -692,50 +682,6 @@ def test_forward_shape( assert input_output.shape == input_tensor.shape assert context_output.shape == context_tensor.shape - def test_residual_connections( - self, - mmdit_block: MMDiTBlock, - batch_size: int, - seq_len: int, - context_seq_len: int, - input_dim: int, - context_dim: int, - embedding_dim: int, - ): - """ - Test that residual connections work correctly. - - Verifies that the multi-modal transformer block implements proper residual connections - for both input and context paths, ensuring that outputs are modified versions of inputs - rather than completely independent transformations. - - Args: - mmdit_block (MMDiTBlock): The multi-modal transformer block to test. - batch_size (int): Size of the input batch. - seq_len (int): Length of the input sequence. - context_seq_len (int): Length of the context sequence. - input_dim (int): Dimension of input features. - context_dim (int): Dimension of context features. - embedding_dim (int): Dimension of conditioning embeddings. - - Raises: - AssertionError: If outputs are identical to inputs (no processing) or - if residual connections are not working properly. - """ - input_tensor = torch.randn(batch_size, seq_len, input_dim) - context_tensor = torch.randn(batch_size, context_seq_len, context_dim) - y = torch.randn(batch_size, embedding_dim) - - # Save original tensors - input_orig = input_tensor.clone() - context_orig = context_tensor.clone() - - input_output, context_output = mmdit_block(input_tensor, y, context_tensor) - - # Outputs should be different from inputs due to residual connections - assert not torch.allclose(input_output, input_orig) - assert not torch.allclose(context_output, context_orig) - def test_gradient_flow( self, mmdit_block: MMDiTBlock, @@ -750,8 +696,7 @@ def test_gradient_flow( Test gradient flow through MMDiTBlock. Ensures that gradients can properly flow backward through the multi-modal transformer - block for input tensors, context tensors, and conditioning embeddings, which is - essential for training multi-modal diffusion models. + block for input tensors, context tensors, and conditioning embeddings. Args: mmdit_block (MMDiTBlock): The multi-modal transformer block to test. @@ -931,7 +876,7 @@ class TestMMDiT: None Methods: - simple_mmdit: Fixture that creates a simple DiT configuration for testing. + simple_dit: Fixture that creates a simple DiT configuration for testing. mmdit_with_context: Fixture that creates a multi-modal DiT configuration for testing. test_simple_dit_init: Tests initialization of simple DiT configuration. test_mmdit_with_context_init: Tests initialization of multi-modal configuration. @@ -954,7 +899,7 @@ class TestMMDiT: """ @pytest.fixture - def simple_mmdit( + def simple_dit( self, input_channels: int, input_dim: int, @@ -1032,12 +977,12 @@ def mmdit_with_context( num_heads=num_heads, mlp_ratio=mlp_ratio, patch_size=patch_size, - depth=2, # Small depth for testing + depth=2, context_dim=context_dim, context_embedder=mock_context_embedder, ) - def test_simple_dit_init(self, simple_mmdit: MMDiT, input_channels: int, patch_size: int): + def test_simple_dit_init(self, simple_dit: MMDiT, input_channels: int, patch_size: int): """ Test simple MMDiT initialization. @@ -1045,25 +990,23 @@ def test_simple_dit_init(self, simple_mmdit: MMDiT, input_channels: int, patch_s parameters, including class conditioning setup and absence of context embedder. Args: - simple_mmdit (MMDiT): Simple DiT model instance to test. + simple_dit (MMDiT): Simple DiT model instance to test. input_channels (int): Expected number of input channels. patch_size (int): Expected patch size. Raises: AssertionError: If any initialization parameter is incorrect. """ - assert simple_mmdit.simple_dit is True - assert simple_mmdit.input_channels == input_channels - assert simple_mmdit.output_channels == input_channels # Should default to input_channels - assert simple_mmdit.patch_size == patch_size - assert simple_mmdit.n_classes == 10 - assert simple_mmdit.classifier_free is True - assert simple_mmdit.context_embedder is None - assert simple_mmdit.label_embed is not None - - def test_mmdit_with_context_init( - self, mmdit_with_context: MMDiT, input_channels: int, patch_size: int, mock_context_embedder: ContextEmbedder - ): + assert simple_dit.simple_dit is True + assert simple_dit.input_channels == input_channels + assert simple_dit.output_channels == input_channels # Should default to input_channels + assert simple_dit.patch_size == patch_size + assert simple_dit.n_classes == 10 + assert simple_dit.classifier_free is True + assert simple_dit.context_embedder is None + assert simple_dit.label_embed is not None + + def test_mmdit_with_context_init(self, mmdit_with_context: MMDiT, mock_context_embedder: ContextEmbedder): """ Test MMDiT with context embedder initialization. @@ -1117,7 +1060,7 @@ def test_invalid_init_mmdit_without_context(self): with pytest.raises(AssertionError, match="for MMDiT context embedder must be provided"): MMDiT(simple_dit=False) - def test_patchify_unpatchify_roundtrip(self, simple_mmdit: MMDiT, sample_image: torch.Tensor): + def test_patchify_unpatchify_roundtrip(self, simple_dit: MMDiT, sample_image: torch.Tensor): """ Test that patchify and unpatchify are inverse operations. @@ -1126,36 +1069,36 @@ def test_patchify_unpatchify_roundtrip(self, simple_mmdit: MMDiT, sample_image: This is crucial for the diffusion model's ability to process images as sequences. Args: - simple_mmdit (MMDiT): Simple DiT model instance for testing. + simple_dit (MMDiT): Simple DiT model instance for testing. sample_image (torch.Tensor): Sample input image tensor. Raises: AssertionError: If patch dimensions or reconstruction shapes are incorrect. """ # Patchify - patches = simple_mmdit.patchify(sample_image) + patches = simple_dit.patchify(sample_image) # Check patchify output shape batch_size, _, height, width = sample_image.shape - expected_num_patches = (height // simple_mmdit.patch_size) * (width // simple_mmdit.patch_size) - assert patches.shape == (batch_size, expected_num_patches, simple_mmdit.conv_proj.out_channels) + expected_num_patches = (height // simple_dit.patch_size) * (width // simple_dit.patch_size) + assert patches.shape == (batch_size, expected_num_patches, simple_dit.conv_proj.out_channels) # Create dummy output with correct channel dimension dummy_output = torch.randn( batch_size, expected_num_patches, - simple_mmdit.patch_size * simple_mmdit.patch_size * simple_mmdit.output_channels, + simple_dit.patch_size * simple_dit.patch_size * simple_dit.output_channels, ) # Unpatchify - reconstructed = simple_mmdit.unpatchify(dummy_output) + reconstructed = simple_dit.unpatchify(dummy_output) # Check reconstructed shape assert reconstructed.shape == sample_image.shape def test_simple_dit_forward( self, - simple_mmdit: MMDiT, + simple_dit: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor, sample_labels: torch.Tensor, @@ -1167,7 +1110,7 @@ def test_simple_dit_forward( class labels and timesteps, producing outputs with correct shapes and data types. Args: - simple_mmdit (MMDiT): Simple DiT model instance to test. + simple_dit (MMDiT): Simple DiT model instance to test. sample_image (torch.Tensor): Sample input image tensor. sample_timesteps (torch.Tensor): Sample timestep tensor. sample_labels (torch.Tensor): Sample class label tensor. @@ -1175,7 +1118,7 @@ class labels and timesteps, producing outputs with correct shapes and data types Raises: AssertionError: If output shape, keys, or data type are incorrect. """ - output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels) + output: ModelOutput = simple_dit(sample_image, sample_timesteps, y=sample_labels) assert "x" in output assert output["x"].shape == sample_image.shape @@ -1206,7 +1149,7 @@ def test_mmdit_forward_with_context( def test_forward_with_intermediate_features( self, - simple_mmdit: MMDiT, + simple_dit: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor, sample_labels: torch.Tensor, @@ -1219,7 +1162,7 @@ def test_forward_with_intermediate_features( extraction applications. Args: - simple_mmdit (MMDiT): Simple DiT model instance to test. + simple_dit (MMDiT): Simple DiT model instance to test. sample_image (torch.Tensor): Sample input image tensor. sample_timesteps (torch.Tensor): Sample timestep tensor. sample_labels (torch.Tensor): Sample class label tensor. @@ -1228,20 +1171,26 @@ def test_forward_with_intermediate_features( AssertionError: If intermediate features are not returned or have incorrect format. """ # Need to access the internal forward methods - patches = simple_mmdit.patchify(sample_image) - output = simple_mmdit.simple_dit_forward(patches, sample_timesteps, y=sample_labels, intermediate_features=True) + patches = simple_dit.patchify(sample_image) + output = simple_dit.simple_dit_forward(patches, sample_timesteps, y=sample_labels, intermediate_features=True) assert "x" in output assert "features" in output assert isinstance(output["features"], list) - assert len(output["features"]) == simple_mmdit.layers.__len__() + 1 # +1 for last layer + assert len(output["features"]) == simple_dit.layers.__len__() + 1 # +1 for last layer def test_forward_with_x_context( self, - simple_mmdit: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor, sample_labels: torch.Tensor, + input_channels: int, + input_dim: int, + hidden_dim: int, + embedding_dim: int, + num_heads: int, + mlp_ratio: int, + patch_size: int, ): """ Test forward pass with x_context concatenation. @@ -1250,7 +1199,6 @@ def test_forward_with_x_context( to the input, which is useful for conditional generation scenarios. Args: - simple_mmdit (MMDiT): Simple DiT model instance to test. sample_image (torch.Tensor): Sample input image tensor. sample_timesteps (torch.Tensor): Sample timestep tensor. sample_labels (torch.Tensor): Sample class label tensor. @@ -1258,15 +1206,31 @@ def test_forward_with_x_context( Raises: AssertionError: If output shape is incorrect when using x_context. """ + # Create a simple DiT model with twice the input channels for x_context + simple_dit = MMDiT( + simple_dit=True, + input_channels=input_channels * 2, + output_channels=input_channels, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + patch_size=patch_size, + depth=2, # Small depth for testing + n_classes=10, + classifier_free=True, + ) + x_context = torch.randn_like(sample_image) - output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels, x_context=x_context) + output: ModelOutput = simple_dit(sample_image, sample_timesteps, y=sample_labels, x_context=x_context) assert "x" in output assert output["x"].shape == sample_image.shape def test_forward_classifier_free_guidance( self, - simple_mmdit: MMDiT, + simple_dit: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor, sample_labels: torch.Tensor, @@ -1278,7 +1242,7 @@ def test_forward_classifier_free_guidance( which is used during training to enable guidance-free generation at inference. Args: - simple_mmdit (MMDiT): Simple DiT model instance to test. + simple_dit (MMDiT): Simple DiT model instance to test. sample_image (torch.Tensor): Sample input image tensor. sample_timesteps (torch.Tensor): Sample timestep tensor. sample_labels (torch.Tensor): Sample class label tensor. @@ -1286,7 +1250,7 @@ def test_forward_classifier_free_guidance( Raises: AssertionError: If output shape is incorrect when using guidance probability. """ - output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels, p=0.1) + output: ModelOutput = simple_dit(sample_image, sample_timesteps, y=sample_labels, p=0.1) assert "x" in output assert output["x"].shape == sample_image.shape @@ -1346,7 +1310,7 @@ def test_forward_invalid_p_without_classifier_free(self, input_channels: int): def test_gradient_flow_simple_dit( self, - simple_mmdit: MMDiT, + simple_dit: MMDiT, sample_image: torch.Tensor, sample_timesteps: torch.Tensor, sample_labels: torch.Tensor, @@ -1355,11 +1319,9 @@ def test_gradient_flow_simple_dit( Test gradient flow through simple DiT. Ensures that gradients can properly flow backward through the simple DiT - configuration for both image inputs and timesteps, which is essential - for training the diffusion model. - + configuration for both image inputs and timesteps. Args: - simple_mmdit (MMDiT): Simple DiT model instance to test. + simple_dit (MMDiT): Simple DiT model instance to test. sample_image (torch.Tensor): Sample input image tensor. sample_timesteps (torch.Tensor): Sample timestep tensor. sample_labels (torch.Tensor): Sample class label tensor. @@ -1370,7 +1332,7 @@ def test_gradient_flow_simple_dit( sample_image.requires_grad_(True) sample_timesteps.requires_grad_(True) - output: ModelOutput = simple_mmdit(sample_image, sample_timesteps, y=sample_labels) + output: ModelOutput = simple_dit(sample_image, sample_timesteps, y=sample_labels) loss = output["x"].sum() loss.backward() # type: ignore[reportUnknownMemberType] @@ -1384,8 +1346,7 @@ def test_gradient_flow_mmdit_with_context( Test gradient flow through MMDiT with context. Ensures that gradients can properly flow backward through the multi-modal DiT - configuration for both image inputs and timesteps, which is essential for - training multi-modal diffusion models. + configuration for both image inputs and timesteps. Args: mmdit_with_context (MMDiT): Multi-modal DiT model instance to test. @@ -1506,7 +1467,7 @@ def test_different_patch_sizes(self, patch_size: int, input_channels: int): output = model(sample_image, sample_timesteps, y=sample_labels) assert output["x"].shape == sample_image.shape - def test_weight_initialization(self, simple_mmdit: MMDiT): + def test_weight_initialization(self, simple_dit: MMDiT): """ Test that weights are properly initialized. @@ -1515,13 +1476,13 @@ def test_weight_initialization(self, simple_mmdit: MMDiT): which is important for stable training convergence. Args: - simple_mmdit (MMDiT): Simple DiT model instance to check. + simple_dit (MMDiT): Simple DiT model instance to check. Raises: AssertionError: If any weights are improperly initialized (all zeros) or if biases are not initialized to zero. """ - for module in simple_mmdit.modules(): + for module in simple_dit.modules(): if isinstance(module, (nn.Linear, nn.Conv2d)): # Check that weights are not all zeros (indicating initialization occurred) assert not torch.allclose(module.weight, torch.zeros_like(module.weight)) @@ -1645,9 +1606,7 @@ def test_very_small_dimensions(self, input_channels: int): """ Test model with very small dimension configurations. - Validates that the MMDiT model can function with minimal dimension settings, - which is important for testing computational limits and ensuring the model - works across various configuration sizes. + Validates that the MMDiT model can function with minimal dimension settings. Args: input_channels (int): Number of input image channels. 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LouisRouss Date: Sun, 10 Aug 2025 17:33:11 +0200 Subject: [PATCH 6/6] add tests for unet and accordingly correct unet --- src/diffulab/networks/denoisers/unet.py | 13 +- tests/unit_tests/networks/conftest.py | 137 -- .../networks/denoisers/test_mmdit.py | 409 ++++- .../networks/denoisers/test_unet.py | 1314 +++++++++++++++++ 4 files changed, 1693 insertions(+), 180 deletions(-) create mode 100644 tests/unit_tests/networks/denoisers/test_unet.py diff --git a/src/diffulab/networks/denoisers/unet.py b/src/diffulab/networks/denoisers/unet.py index bb29960..a8a8fa5 100644 --- a/src/diffulab/networks/denoisers/unet.py +++ b/src/diffulab/networks/denoisers/unet.py @@ -291,9 +291,9 @@ def _forward( 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 = q.reshape(b * self.num_heads, self.dim_head, x_len) + k = k.reshape(b * self.num_heads, self.dim_head, context_len) + v = v.reshape(b * self.num_heads, self.dim_head, context_len) 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) @@ -368,7 +368,6 @@ class UNetModel(Denoiser): 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). @@ -444,7 +443,6 @@ 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, @@ -471,7 +469,6 @@ 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 @@ -708,10 +705,10 @@ class labels and/or external context (e.g. text or images) with support for if self.label_embed is not None: emb = emb + self.label_embed(y, p) if self.context_embedder is not None: - context = self.context_embedder(context, p) + context = self.context_embedder(context, p)[0] 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) hs.append(h) diff --git a/tests/unit_tests/networks/conftest.py b/tests/unit_tests/networks/conftest.py index 60a19b5..e69de29 100644 --- a/tests/unit_tests/networks/conftest.py +++ b/tests/unit_tests/networks/conftest.py @@ -1,137 +0,0 @@ -"""Network-specific fixtures for testing.""" - -from typing import Any - -import pytest -import torch - -from diffulab.networks.embedders.common import ContextEmbedder - - -class MockContextEmbedder(ContextEmbedder): - """Mock context embedder for testing.""" - - def __init__(self, pooled_dim: int = 256, sequence_dim: int = 512, sequence_len: int = 32): - super().__init__() - self.pooled_dim = pooled_dim - self.sequence_dim = sequence_dim - self.sequence_len = sequence_len - - @property - def n_output(self) -> int: - return 2 - - @property - def output_size(self) -> tuple[int, int]: - return (self.pooled_dim, self.sequence_dim) - - def drop_conditions(self, context: Any, p: float) -> Any: - return context - - def forward(self, context: Any = None, p: float = 0.0) -> tuple[torch.Tensor, torch.Tensor]: - batch_size = 4 if context is None else (context.shape[0] if hasattr(context, "shape") else 4) - pooled = torch.randn(batch_size, self.pooled_dim) - sequence = torch.randn(batch_size, self.sequence_len, self.sequence_dim) - return pooled, sequence - - -@pytest.fixture(scope="module") -def mock_context_embedder(): - """Create a mock context embedder for testing.""" - return MockContextEmbedder() - - -@pytest.fixture(scope="module") -def input_dim(): - """Standard input dimension for testing.""" - return 512 - - -@pytest.fixture(scope="module") -def hidden_dim(): - """Standard hidden dimension for testing.""" - return 512 - - -@pytest.fixture(scope="module") -def embedding_dim(): - """Standard embedding dimension for testing.""" - return 256 - - -@pytest.fixture(scope="module") -def context_dim(): - """Standard context dimension for testing.""" - return 512 - - -@pytest.fixture(scope="module") -def num_heads(): - """Standard number of attention heads for testing.""" - return 8 - - -@pytest.fixture(scope="module") -def mlp_ratio(): - """Standard MLP ratio for testing.""" - return 4 - - -@pytest.fixture(scope="module") -def patch_size(): - """Standard patch size for testing.""" - return 16 - - -@pytest.fixture(scope="module") -def input_channels(): - """Standard number of input channels for testing.""" - return 3 - - -@pytest.fixture(scope="module") -def image_size(): - """Standard image size for testing.""" - return 256 - - -@pytest.fixture(scope="module") -def sample_image(batch_size: int, input_channels: int, image_size: int) -> torch.Tensor: - """Create a sample image tensor for testing.""" - return torch.randn(batch_size, input_channels, image_size, image_size) - - -@pytest.fixture(scope="module") -def sample_timesteps(batch_size: int) -> torch.Tensor: - """Create sample timesteps for testing.""" - return torch.randn(batch_size) - - -@pytest.fixture -def sample_labels(batch_size: int) -> torch.Tensor: - """Create sample class labels for testing.""" - return torch.randint(0, 10, (batch_size,)) - - -@pytest.fixture(scope="module") -def device(): - """Return the device to use for testing.""" - return torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -@pytest.fixture(scope="module") -def batch_size(): - """Standard batch size for testing.""" - return 4 - - -@pytest.fixture(scope="module") -def seq_len(): - """Standard sequence length for testing.""" - return 25 - - -@pytest.fixture(scope="module") -def context_seq_len(): - """Standard context sequence length for testing.""" - return 32 diff --git a/tests/unit_tests/networks/denoisers/test_mmdit.py b/tests/unit_tests/networks/denoisers/test_mmdit.py index fa417e7..9045fc1 100644 --- a/tests/unit_tests/networks/denoisers/test_mmdit.py +++ b/tests/unit_tests/networks/denoisers/test_mmdit.py @@ -23,7 +23,7 @@ TestMMDiTEdgeCases: Tests for edge cases and error conditions. """ -from typing import cast +from typing import Any, cast from unittest.mock import MagicMock, patch import pytest @@ -42,6 +42,38 @@ from diffulab.networks.embedders.common import ContextEmbedder +class MockContextEmbedder(ContextEmbedder): + """Mock context embedder for testing.""" + + def __init__(self, pooled_dim: int = 64, sequence_dim: int = 128, sequence_len: int = 32): + super().__init__() + self.pooled_dim = pooled_dim + self.sequence_dim = sequence_dim + self.sequence_len = sequence_len + + @property + def n_output(self) -> int: + return 2 + + @property + def output_size(self) -> tuple[int, int]: + return (self.pooled_dim, self.sequence_dim) + + def drop_conditions(self, context: Any, p: float) -> Any: + return context + + def forward(self, context: Any = None, p: float = 0.0) -> tuple[torch.Tensor, torch.Tensor]: + pooled = torch.randn(context, self.pooled_dim) + sequence = torch.randn(context, self.sequence_len, self.sequence_dim) + return pooled, sequence + + +@pytest.fixture(scope="module") +def mock_context_embedder(): + """Create a mock context embedder for testing.""" + return MockContextEmbedder() + + class TestDiTAttention: """ Test class for DiTAttention module. @@ -62,6 +94,31 @@ class TestDiTAttention: test_gradient_flow: Tests gradient propagation through the attention mechanism. """ + @pytest.fixture(scope="class") + def input_dim(self): + """Standard input dimension for testing.""" + return 128 + + @pytest.fixture(scope="class") + def hidden_dim(self): + """Standard hidden dimension for testing.""" + return 128 + + @pytest.fixture(scope="class") + def num_heads(self): + """Standard number of attention heads for testing.""" + return 4 + + @pytest.fixture(scope="class") + def batch_size(self): + """Standard batch size for testing.""" + return 2 + + @pytest.fixture(scope="class") + def seq_len(self): + """Standard sequence length for testing.""" + return 32 + @pytest.fixture def dit_attention(self, input_dim: int, hidden_dim: int, num_heads: int): """ @@ -237,7 +294,42 @@ class TestMMDiTAttention: test_gradient_flow: Tests gradient propagation through the multi-modal attention. """ - @pytest.fixture + @pytest.fixture(scope="class") + def context_dim(self): + """Standard context dimension for testing.""" + return 128 + + @pytest.fixture(scope="class") + def input_dim(self): + """Standard input dimension for testing.""" + return 256 + + @pytest.fixture(scope="class") + def hidden_dim(self): + """Standard hidden dimension for testing.""" + return 384 + + @pytest.fixture(scope="class") + def num_heads(self): + """Standard number of attention heads for testing.""" + return 4 + + @pytest.fixture(scope="class") + def batch_size(self): + """Standard batch size for testing.""" + return 2 + + @pytest.fixture(scope="class") + def seq_len(self): + """Standard sequence length for testing.""" + return 28 + + @pytest.fixture(scope="class") + def context_seq_len(self): + """Standard context sequence length for testing.""" + return 32 + + @pytest.fixture(scope="class") def mmdit_attention(self, context_dim: int, input_dim: int, hidden_dim: int, num_heads: int): """ Create MMDiTAttention instance for testing. @@ -418,7 +510,42 @@ class TestDiTBlock: test_gradient_flow: Tests gradient propagation through the transformer block. """ - @pytest.fixture + @pytest.fixture(scope="class") + def input_dim(self): + """Standard input dimension for testing.""" + return 256 + + @pytest.fixture(scope="class") + def hidden_dim(self): + """Standard hidden dimension for testing.""" + return 384 + + @pytest.fixture(scope="class") + def num_heads(self): + """Standard number of attention heads for testing.""" + return 4 + + @pytest.fixture(scope="class") + def batch_size(self): + """Standard batch size for testing.""" + return 2 + + @pytest.fixture(scope="class") + def embedding_dim(self): + """Standard embedding dimension for testing.""" + return 64 + + @pytest.fixture(scope="class") + def mlp_ratio(self): + """Standard MLP ratio for testing.""" + return 4 + + @pytest.fixture(scope="class") + def seq_len(self): + """Standard sequence length for testing.""" + return 16 + + @pytest.fixture(scope="class") def dit_block(self, input_dim: int, hidden_dim: int, embedding_dim: int, num_heads: int, mlp_ratio: int): """ Create DiTBlock instance for testing. @@ -442,9 +569,7 @@ def dit_block(self, input_dim: int, hidden_dim: int, embedding_dim: int, num_hea partial_rotary_factor=1.0, ) - def test_init( - self, dit_block: DiTBlock, input_dim: int, hidden_dim: int, embedding_dim: int, num_heads: int, mlp_ratio: int - ): + def test_init(self, dit_block: DiTBlock, input_dim: int, mlp_ratio: int): """ Test DiTBlock initialization. @@ -455,9 +580,6 @@ def test_init( Args: dit_block (DiTBlock): The transformer block to test. input_dim (int): Expected input dimension. - hidden_dim (int): Expected hidden dimension. - embedding_dim (int): Expected embedding dimension. - num_heads (int): Expected number of attention heads. mlp_ratio (int): Expected MLP expansion ratio. Raises: @@ -489,7 +611,7 @@ def test_forward_shape( batch_size (int): Size of the input batch. seq_len (int): Length of the input sequence. input_dim (int): Dimension of input features. - embedding_dim (int): Dimension of conditioning embeddings. + embedding_dim (int): Dimension of the conditioning embeddings. Raises: AssertionError: If output shape or data type doesn't match input. @@ -524,7 +646,7 @@ def test_modulation_calls( batch_size (int): Size of the input batch. seq_len (int): Length of the input sequence. input_dim (int): Dimension of input features. - embedding_dim (int): Dimension of conditioning embeddings. + embedding_dim (int): Dimension of the conditioning embeddings. Raises: AssertionError: If modulate is not called the expected number of times (twice). @@ -554,7 +676,7 @@ def test_gradient_flow( batch_size (int): Size of the input batch. seq_len (int): Length of the input sequence. input_dim (int): Dimension of input features. - embedding_dim (int): Dimension of conditioning embeddings. + embedding_dim (int): Dimension of the conditioning embeddings. Raises: AssertionError: If gradients are not computed for input tensor or embeddings. @@ -588,7 +710,52 @@ class TestMMDiTBlock: test_gradient_flow: Tests gradient propagation through the multi-modal block. """ - @pytest.fixture + @pytest.fixture(scope="class") + def input_dim(self): + """Standard input dimension for testing.""" + return 256 + + @pytest.fixture(scope="class") + def context_dim(self): + """Standard context dimension for testing.""" + return 128 + + @pytest.fixture(scope="class") + def hidden_dim(self): + """Standard hidden dimension for testing.""" + return 384 + + @pytest.fixture(scope="class") + def num_heads(self): + """Standard number of attention heads for testing.""" + return 4 + + @pytest.fixture(scope="class") + def batch_size(self): + """Standard batch size for testing.""" + return 2 + + @pytest.fixture(scope="class") + def embedding_dim(self): + """Standard embedding dimension for testing.""" + return 64 + + @pytest.fixture(scope="class") + def mlp_ratio(self): + """Standard MLP ratio for testing.""" + return 4 + + @pytest.fixture(scope="class") + def seq_len(self): + """Standard sequence length for testing.""" + return 16 + + @pytest.fixture(scope="class") + def context_seq_len(self): + """Standard context sequence length for testing.""" + return 32 + + @pytest.fixture(scope="class") def mmdit_block( self, context_dim: int, input_dim: int, hidden_dim: int, embedding_dim: int, num_heads: int, mlp_ratio: int ): @@ -743,7 +910,37 @@ class TestModulatedLastLayer: test_gradient_flow: Tests gradient propagation through the output layer. """ - @pytest.fixture + @pytest.fixture(scope="class") + def embedding_dim(self): + """Standard embedding dimension for testing.""" + return 64 + + @pytest.fixture(scope="class") + def input_dim(self): + """Standard input dimension for testing.""" + return 128 + + @pytest.fixture(scope="class") + def patch_size(self): + """Standard patch size for testing.""" + return 16 + + @pytest.fixture(scope="class") + def input_channels(self): + """Standard number of output channels for testing.""" + return 3 + + @pytest.fixture(scope="class") + def batch_size(self): + """Standard batch size for testing.""" + return 2 + + @pytest.fixture(scope="class") + def seq_len(self): + """Standard sequence length for testing.""" + return 32 + + @pytest.fixture(scope="class") def last_layer(self, embedding_dim: int, input_dim: int, patch_size: int, input_channels: int): """ Create ModulatedLastLayer instance for testing. @@ -765,7 +962,11 @@ def last_layer(self, embedding_dim: int, input_dim: int, patch_size: int, input_ ) def test_init( - self, last_layer: ModulatedLastLayer, input_dim: int, patch_size: int, input_channels: int, embedding_dim: int + self, + last_layer: ModulatedLastLayer, + input_dim: int, + patch_size: int, + input_channels: int, ): """ Test ModulatedLastLayer initialization. @@ -779,7 +980,6 @@ def test_init( input_dim (int): Expected hidden feature dimension. patch_size (int): Expected patch size. input_channels (int): Expected number of output channels. - embedding_dim (int): Expected embedding dimension. Raises: AssertionError: If any component is not initialized correctly or has wrong dimensions. @@ -898,7 +1098,53 @@ class TestMMDiT: test_weight_initialization: Tests proper weight initialization. """ - @pytest.fixture + # Hyperparameter fixtures (mirroring other test classes for consistency) + @pytest.fixture(scope="class") + def input_channels(self): + return 3 + + @pytest.fixture(scope="class") + def input_dim(self): + return 256 + + @pytest.fixture(scope="class") + def hidden_dim(self): + return 384 + + @pytest.fixture(scope="class") + def embedding_dim(self): + return 64 + + @pytest.fixture(scope="class") + def num_heads(self): + return 4 + + @pytest.fixture(scope="class") + def mlp_ratio(self): + return 4 + + @pytest.fixture(scope="class") + def patch_size(self): + return 16 + + @pytest.fixture(scope="class") + def context_dim(self): + return 128 + + # Sample data fixtures + @pytest.fixture(scope="class") + def sample_image(self, input_channels: int): + return torch.randn(2, input_channels, 64, 64) + + @pytest.fixture(scope="class") + def sample_timesteps(self): + return torch.randn(2) + + @pytest.fixture(scope="class") + def sample_labels(self): + return torch.randint(0, 10, (2,)) + + @pytest.fixture(scope="class") def simple_dit( self, input_channels: int, @@ -938,7 +1184,7 @@ def simple_dit( classifier_free=True, ) - @pytest.fixture + @pytest.fixture(scope="class") def mmdit_with_context( self, input_channels: int, @@ -1141,8 +1387,7 @@ def test_mmdit_forward_with_context( Raises: AssertionError: If output shape or keys are incorrect. """ - initial_context = "test context" # Mock context - output: ModelOutput = mmdit_with_context(sample_image, sample_timesteps, initial_context=initial_context) + output: ModelOutput = mmdit_with_context(sample_image, sample_timesteps, initial_context=sample_image.shape[0]) assert "x" in output assert output["x"].shape == sample_image.shape @@ -1359,7 +1604,7 @@ def test_gradient_flow_mmdit_with_context( sample_image.requires_grad_(True) sample_timesteps.requires_grad_(True) - output: ModelOutput = mmdit_with_context(sample_image, sample_timesteps, initial_context="test") + output: ModelOutput = mmdit_with_context(sample_image, sample_timesteps, initial_context=sample_image.shape[0]) loss = output["x"].sum() loss.backward() # type: ignore[reportUnknownMemberType] @@ -1509,7 +1754,61 @@ class TestMMDiTEdgeCases: test_very_small_dimensions: Tests model with minimal dimension settings. """ - def test_zero_batch_size(self, input_channels: int): + # Fixtures (mirroring style used in other test classes for consistency) + @pytest.fixture(scope="class") + def input_channels(self): + return 3 + + @pytest.fixture(scope="class") + def input_dim(self): + return 64 + + @pytest.fixture(scope="class") + def hidden_dim(self): + return 128 + + @pytest.fixture(scope="class") + def embedding_dim(self): + return 128 + + @pytest.fixture(scope="class") + def num_heads(self): + return 2 # small for tests + + @pytest.fixture(scope="class") + def mlp_ratio(self): + return 4 + + @pytest.fixture(scope="class") + def patch_size(self): + return 16 + + @pytest.fixture(scope="class") + def small_image(self, input_channels: int): + return torch.randn(2, input_channels, 32, 32) + + @pytest.fixture(scope="class") + def empty_image(self, input_channels: int): + return torch.empty((0, input_channels, 64, 64)) + + @pytest.fixture(scope="class") + def sample_timesteps(self): + return torch.randn(2) + + @pytest.fixture(scope="class") + def sample_labels(self): + return torch.randint(0, 10, (2,)) + + def test_zero_batch_size( + self, + input_channels: int, + input_dim: int, + hidden_dim: int, + embedding_dim: int, + num_heads: int, + mlp_ratio: int, + patch_size: int, + ): """ Test behavior with zero batch size. @@ -1518,6 +1817,12 @@ def test_zero_batch_size(self, input_channels: int): Args: input_channels (int): Number of input image channels. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + patch_size (int): Size of image patches. Raises: AssertionError: If the model doesn't handle empty batches correctly or @@ -1526,20 +1831,31 @@ def test_zero_batch_size(self, input_channels: int): model = MMDiT( simple_dit=True, input_channels=input_channels, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + patch_size=patch_size, n_classes=10, depth=1, ) - - # Empty tensor sample_image = torch.empty((0, input_channels, 64, 64)) sample_timesteps = torch.empty((0,)) sample_labels = torch.empty((0,), dtype=torch.long) - - # Should handle empty batch gracefully output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) assert output["x"].shape[0] == 0 - def test_non_square_images(self, input_channels: int): + def test_non_square_images( + self, + input_channels: int, + input_dim: int, + hidden_dim: int, + embedding_dim: int, + num_heads: int, + mlp_ratio: int, + patch_size: int, + ): """ Test model behavior with non-square image inputs. @@ -1549,30 +1865,45 @@ def test_non_square_images(self, input_channels: int): Args: input_channels (int): Number of input image channels. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + num_heads (int): Number of attention heads. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + patch_size (int): Size of image patches. Raises: AssertionError: If the model fails to process non-square images correctly or if output dimensions don't match expected values. ValueError: If the patch size doesn't divide evenly into the image dimensions. """ - patch_size = 16 model = MMDiT( simple_dit=True, input_channels=input_channels, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, patch_size=patch_size, n_classes=10, depth=1, ) - - # Non-square but divisible by patch size - sample_image = torch.randn(2, input_channels, 64, 128) # 64x128 + sample_image = torch.randn(2, input_channels, 64, 128) sample_timesteps = torch.randn(2) sample_labels = torch.randint(0, 10, (2,)) - output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) assert output["x"].shape == sample_image.shape - def test_single_head_attention(self, input_channels: int): + def test_single_head_attention( + self, + input_channels: int, + input_dim: int, + hidden_dim: int, + embedding_dim: int, + mlp_ratio: int, + patch_size: int, + ): """ Test model with single attention head configuration. @@ -1582,6 +1913,11 @@ def test_single_head_attention(self, input_channels: int): Args: input_channels (int): Number of input image channels. + input_dim (int): Dimension of the input features. + hidden_dim (int): Hidden dimension for attention computation. + embedding_dim (int): Dimension of the conditioning embeddings. + mlp_ratio (int): Ratio for MLP hidden dimension expansion. + patch_size (int): Size of image patches. Raises: AssertionError: If the model fails with single head attention or @@ -1590,15 +1926,18 @@ def test_single_head_attention(self, input_channels: int): model = MMDiT( simple_dit=True, input_channels=input_channels, + input_dim=input_dim, + hidden_dim=hidden_dim, + embedding_dim=embedding_dim, num_heads=1, + mlp_ratio=mlp_ratio, + patch_size=patch_size, n_classes=10, depth=1, ) - sample_image = torch.randn(2, input_channels, 64, 64) sample_timesteps = torch.randn(2) sample_labels = torch.randint(0, 10, (2,)) - output: ModelOutput = model(sample_image, sample_timesteps, y=sample_labels) assert output["x"].shape == sample_image.shape diff --git a/tests/unit_tests/networks/denoisers/test_unet.py b/tests/unit_tests/networks/denoisers/test_unet.py new file mode 100644 index 0000000..5e9a0fe --- /dev/null +++ b/tests/unit_tests/networks/denoisers/test_unet.py @@ -0,0 +1,1314 @@ +""" +Unit tests for the UNet-based denoiser components (ResBlock, AttentionBlock, UNetModel). + +Overview: + This module provides comprehensive, behavior-focused test coverage for the UNetModel + architecture and its fundamental building blocks used in diffusion-style denoisers. + Tests emphasize initialization correctness, tensor shape integrity, conditioning + pathways (class and context), classifier-free guidance behavior, attention injection, + architectural configuration flags, gradient propagation, and edge/error handling. + +Components Under Test: + - ResBlock: Residual block supporting optional up/down sampling and FiLM-style + (scale, shift) modulation via timestep / context embeddings. + - AttentionBlock: Multi-head self/cross attention operating over flattened spatial + tokens with optional external context key/value tensor. + - UNetModel: Full hierarchical encoder–decoder with skip connections, channel + multipliers, attention at selectable resolutions, and optional class / context + conditioning plus classifier-free guidance. + +Fixtures Provided: + Reusable pytest fixtures standardize tensor shapes and hyperparameters (e.g., + batch size, spatial dimensions, channels, embedding dimensions, and synthetic + context). Context conditioning uses a lightweight deterministic MockContextEmbedder. + +Test Coverage Summary: + - Initialization: Parameter bookkeeping, submodule presence, configuration flags. + - Forward Pass: Output tensor shapes across conditioning modes and architectural + permutations (channel multipliers, scale-shift norm, resblock up/down routing, + gradient checkpointing, multiple attention resolutions, auxiliary image context). + - Conditioning: Class label vs context embeddings, classifier-free guidance mask + probability, assertion coverage when required inputs are omitted. + - Attention: Self vs cross attention equivalence, multi-resolution insertion counts. + - Gradient Flow: Backpropagation to inputs and embeddings for critical paths. + - Edge / Error Cases: Non-square inputs, mismatched spatial sizes, invalid guidance + probability usage, zero / altered shapes (implicit via assertions). + +Classes: + TestResBlock: Initialization, forward shape, scale-shift variant, up/down sampling, + gradient flow. + TestAttentionBlock: Initialization, self vs cross attention, gradient flow, + equivalence check. + TestUNetModel: Conditioning modes, guidance probability, architectural flags, + attention resolution counts, gradient flow, edge/error assertions. +""" + +from __future__ import annotations + +from typing import Any + +import pytest +import torch +import torch.nn as nn +from torch import Tensor + +from diffulab.networks.denoisers.common import ModelOutput +from diffulab.networks.denoisers.unet import ( + AttentionBlock, + ResBlock, + UNetModel, +) +from diffulab.networks.embedders.common import ContextEmbedder + + +class MockContextEmbedder(ContextEmbedder): + """Mock context embedder used to emulate external sequence conditioning. + + Generates a single context tensor shaped ``(B, C_ctx, L)`` where both the channel + dimension and sequence length are configurable. Implements a simple dropout + mechanism that zeroes entire per-sample context tensors with probability ``p``. + + Args: + context_channels (int, optional): Number of context feature channels. Defaults to 32. + context_length (int, optional): Number of sequential context tokens. Defaults to 16. + + Returns: + tuple[torch.Tensor]: A one-element tuple containing the context tensor of + shape ``(B, context_channels, context_length)``. + + Raises: + None + + Notes: + The forward method accepts either an integer (interpreted as batch size) or a + tensor whose first dimension supplies the batch size. This keeps tests lightweight + and independent from upstream data dependencies. + """ + + def __init__(self, context_channels: int = 32, context_length: int = 16): + super().__init__() # type: ignore + self._channels = context_channels + self._length = context_length + + @property + def n_output(self) -> int: # type: ignore[override] + return 1 + + @property + def output_size(self) -> tuple[int, ...]: # type: ignore[override] + return (self._channels, self._length) + + def drop_conditions(self, context: Any, p: float) -> Any: # type: ignore[override] + if p <= 0: + return context + mask = torch.rand(context.shape[0], device=context.device) < p + context[mask] = 0.0 + return context + + def forward(self, context: Any, p: float): # type: ignore[override] + # context argument unused; we synthesize deterministic tensor for shape tests + B = context if isinstance(context, int) else (context.shape[0] if torch.is_tensor(context) else 2) # fallback + ctx = torch.randn(B, self._channels, self._length) + ctx = self.drop_conditions(ctx, p) + return (ctx,) + + +@pytest.fixture(scope="module") +def context_channels() -> int: + """Standard number of context channels for testing.""" + return 32 + + +@pytest.fixture(scope="module") +def context_length() -> int: + """Standard context sequence length for testing.""" + return 16 + + +@pytest.fixture(scope="module") +def mock_context_embedder(context_channels: int, context_length: int) -> MockContextEmbedder: + return MockContextEmbedder(context_channels=context_channels, context_length=context_length) + + +# --------------------------------------------------------------------------- +# ResBlock Tests +# --------------------------------------------------------------------------- +class TestResBlock: + """Tests for the UNet residual block (ResBlock). + + Validates correct construction and runtime behavior across variants. + + Covered Behaviors: + * Channel / embedding bookkeeping and skip connection adaptation. + * Standard forward pass output shape integrity. + * FiLM scale/shift normalization path activation. + * Spatial size changes under up/down sampling flags. + * Gradient propagation to inputs and embeddings. + + Test Methods: + - test_init + - test_forward_shape + - test_scale_shift_variant + - test_up_and_down + - test_gradient_flow + """ + + @pytest.fixture(scope="class") + def batch_size(self) -> int: + """Standard batch size for ResBlock tests.""" + return 4 + + @pytest.fixture(scope="class") + def channels(self) -> int: + """Standard input channel count for the block.""" + return 32 + + @pytest.fixture(scope="class") + def embedding_dim(self) -> int: + """Standard embedding (timestep/context) dimension used for FiLM style modulation.""" + return 128 + + @pytest.fixture(scope="class") + def dropout(self) -> float: + """Dropout probability applied inside the block MLP path.""" + return 0.1 + + @pytest.fixture(scope="class") + def out_channels(self) -> int: + """Output channel count (tests skip connection adaptation).""" + return 64 + + @pytest.fixture(scope="class") + def hw_image_size(self) -> list[int]: + """Spatial resolution [H, W] for generated feature maps.""" + return [64, 64] + + @pytest.fixture(scope="class") + def resblock(self, channels: int, embedding_dim: int, dropout: float, out_channels: int) -> ResBlock: + """Instantiate a canonical ResBlock under test. + + Args: + channels: Input feature channels. + embedding_dim: Embedding dimension for conditioning. + dropout: Dropout probability. + out_channels: Desired output channel count. + """ + return ResBlock( + channels=channels, + emb_channels=embedding_dim, + dropout=dropout, + out_channels=out_channels, + use_conv=True, + use_scale_shift_norm=False, + ) + + def test_init(self, resblock: ResBlock, channels: int, embedding_dim: int, out_channels: int): + """Validate ResBlock initialization and configured submodules. + + Args: + resblock (ResBlock): Instance under test. + channels (int): Expected input channel count. + embedding_dim (int): Expected embedding (conditioning) dimension. + out_channels (int): Expected output channel count after block. + + Returns: + None + + Raises: + AssertionError: If any expected dimension or module type is incorrect. + """ + assert resblock.channels == channels + assert resblock.emb_channels == embedding_dim + assert resblock.out_channels == out_channels + # Skip connection should be 3x3 conv because use_conv True and channel change + assert isinstance(resblock.skip_connection, nn.Conv2d) + assert resblock.skip_connection.kernel_size in [(3, 3), (1, 1)] + # Emb layers dimension + lin = resblock.emb_layers[1] + assert isinstance(lin, nn.Linear) + assert lin.in_features == embedding_dim + assert lin.out_features == out_channels + + def test_forward_shape( + self, + batch_size: int, + resblock: ResBlock, + channels: int, + embedding_dim: int, + out_channels: int, + hw_image_size: list[int], + ): + """Ensure standard forward pass preserves spatial size and updates channels. + + Args: + batch_size (int): Number of samples in mini-batch. + resblock (ResBlock): Block under test. + channels (int): Input channel count. + embedding_dim (int): Embedding dimension for modulation. + out_channels (int): Expected output channel count. + hw_image_size (list[int]): Spatial dimensions [H, W]. + + Returns: + None + + Raises: + AssertionError: If output tensor shape deviates from expectation. + """ + x = torch.randn(batch_size, channels, *hw_image_size) + emb = torch.randn(batch_size, embedding_dim) + out = resblock(x, emb) + assert out.shape == (batch_size, out_channels, *hw_image_size) + + def test_scale_shift_variant( + self, + channels: int, + embedding_dim: int, + dropout: float, + out_channels: int, + batch_size: int, + hw_image_size: list[int], + ): + """Test variant using scale-shift normalization FiLM modulation path. + + Args: + channels (int): Input channel count. + embedding_dim (int): Embedding dimension. + dropout (float): Dropout probability for internal layers. + out_channels (int): Output channel count. + batch_size (int): Batch size. + hw_image_size (list[int]): Spatial dimensions [H, W]. + + Returns: + None + + Raises: + AssertionError: If output shape is incorrect. + """ + block = ResBlock( + channels=channels, + emb_channels=embedding_dim, + dropout=dropout, + out_channels=out_channels, + use_scale_shift_norm=True, + ) + x = torch.randn(batch_size, channels, *hw_image_size) + emb = torch.randn(batch_size, embedding_dim) + out = block(x, emb) + assert out.shape == (batch_size, out_channels, *hw_image_size) + + def test_up_and_down( + self, + channels: int, + embedding_dim: int, + out_channels: int, + batch_size: int, + hw_image_size: list[int], + dropout: float, + ): + """Validate spatial scaling for up=True and down=True configurations. + + Args: + channels (int): Input channel count. + embedding_dim (int): Embedding dimension. + out_channels (int): Output channel count. + batch_size (int): Batch size. + hw_image_size (list[int]): Input spatial dimensions [H, W]. + dropout (float): Dropout probability. + + Returns: + None + + Raises: + AssertionError: If spatial scaling factors are incorrect. + """ + # Up + up_block = ResBlock(channels, embedding_dim, dropout, out_channels=out_channels, up=True) + x = torch.randn(batch_size, channels, *hw_image_size) + emb = torch.randn(batch_size, embedding_dim) + up_out = up_block(x, emb) + assert up_out.shape[2:] == (hw_image_size[0] * 2, hw_image_size[1] * 2) + # Down + down_block = ResBlock(channels, embedding_dim, dropout, out_channels=out_channels, down=True) + down_out = down_block(x, emb) + assert down_out.shape[2:] == (hw_image_size[0] // 2, hw_image_size[1] // 2) + + def test_gradient_flow( + self, resblock: ResBlock, batch_size: int, channels: int, hw_image_size: list[int], embedding_dim: int + ): + """Confirm gradients propagate to inputs and embeddings during backward pass. + + Args: + resblock (ResBlock): Block under test. + batch_size (int): Batch size. + channels (int): Input channel count. + hw_image_size (list[int]): Spatial dimensions [H, W]. + embedding_dim (int): Embedding dimension. + + Returns: + None + + Raises: + AssertionError: If gradients are missing for inputs or embeddings. + """ + x = torch.randn(batch_size, channels, *hw_image_size, requires_grad=True) + emb = torch.randn(batch_size, embedding_dim, requires_grad=True) + out = resblock(x, emb) + out.sum().backward() # type: ignore + assert x.grad is not None + assert emb.grad is not None + + +# --------------------------------------------------------------------------- +# AttentionBlock Tests +# --------------------------------------------------------------------------- +class TestAttentionBlock: + """Tests for spatial multi-head (self/cross) attention block. + + Covered Behaviors: + * Projection layer initialization & dimensional attributes. + * Self-attention (implicit context) forward shape. + * Cross-attention with external context forward shape. + * Gradient propagation to feature and context tensors. + * Equivalence of self-attention vs cross-attention when context==flattened input. + + Test Methods: + - test_init + - test_forward_self_attention + - test_forward_cross_attention + - test_gradient_flow + - test_attention_self_equivalence + """ + + @pytest.fixture(scope="class") + def channels(self) -> int: + """Canonical feature channel size for attention queries and outputs.""" + return 64 + + @pytest.fixture(scope="class") + def context_channels(self) -> int: + """Channel size for external context key/value inputs.""" + return 32 + + @pytest.fixture(scope="class") + def num_heads(self) -> int: + """Number of attention heads to partition inner channels.""" + return 4 + + @pytest.fixture(scope="class") + def inner_channels(self) -> int: + """Projected MHA internal channel size (must divide by num_heads).""" + return 64 + + @pytest.fixture(scope="class") + def batch_size(self) -> int: + """Mini-batch size for attention tests.""" + return 4 + + @pytest.fixture(scope="class") + def hw_image_size(self) -> list[int]: + """Spatial resolution for feature map tokens (H, W).""" + return [8, 8] + + @pytest.fixture(scope="class") + def context_length(self) -> int: + """Temporal/sequence length of flattened external context tokens.""" + return 32 + + @pytest.fixture(scope="class") + def attn_block(self, channels: int, context_channels: int, num_heads: int, inner_channels: int) -> AttentionBlock: + """Instantiate AttentionBlock with explicit cross-attention capability.""" + return AttentionBlock( + channels=channels, context_channels=context_channels, num_heads=num_heads, inner_channels=inner_channels + ) + + def test_init( + self, attn_block: AttentionBlock, channels: int, context_channels: int, num_heads: int, inner_channels: int + ): + """Check attribute values and projection layer setups after initialization. + + Args: + attn_block (AttentionBlock): Instance under test. + channels (int): Query/input channel dimension. + context_channels (int): Context channel dimension. + num_heads (int): Number of attention heads. + inner_channels (int): Projected internal channel dimension. + + Returns: + None + + Raises: + AssertionError: If any attribute or module setup is invalid. + """ + assert attn_block.channels == channels + assert attn_block.context_channels == context_channels + assert attn_block.inner_channels == inner_channels + assert attn_block.num_heads == num_heads + assert attn_block.dim_head == inner_channels // num_heads + assert isinstance(attn_block.to_q, nn.Conv1d) + assert isinstance(attn_block.to_kv, nn.Conv1d) + assert attn_block.to_q.in_channels == channels + assert attn_block.to_kv.in_channels == context_channels + + def test_forward_self_attention( + self, batch_size: int, channels: int, inner_channels: int, num_heads: int, hw_image_size: list[int] + ): + """Validate self-attention path when context is omitted (defaults to x). + + Args: + batch_size (int): Batch size. + channels (int): Input channel count. + inner_channels (int): Internal projection dim. + num_heads (int): Number of heads. + hw_image_size (list[int]): Spatial dims [H, W]. + + Returns: + None + + Raises: + AssertionError: If output shape mismatches input. + """ + block = AttentionBlock(channels=channels, num_heads=num_heads, inner_channels=inner_channels) + x = torch.randn(batch_size, channels, *hw_image_size) + out = block(x, None) + assert out.shape == x.shape + + def test_forward_cross_attention( + self, + attn_block: AttentionBlock, + batch_size: int, + channels: int, + context_channels: int, + hw_image_size: list[int], + context_length: int, + ): + """Ensure cross-attention consumes external context and preserves output spatial shape. + + Args: + attn_block (AttentionBlock): Instance under test. + batch_size (int): Batch size. + channels (int): Input channel dimension. + context_channels (int): Context feature channel dimension. + hw_image_size (list[int]): Spatial dims [H, W]. + context_length (int): Number of context tokens. + + Returns: + None + + Raises: + AssertionError: If output shape deviates from expected. + """ + x = torch.randn(batch_size, channels, *hw_image_size) + context = torch.randn(batch_size, context_channels, context_length) + out = attn_block(x, context) + assert out.shape == x.shape + + def test_gradient_flow( + self, + attn_block: AttentionBlock, + batch_size: int, + channels: int, + context_channels: int, + hw_image_size: list[int], + context_length: int, + ): + """Check gradients for both input feature map and context tokens. + + Args: + attn_block (AttentionBlock): Instance under test. + batch_size (int): Batch size. + channels (int): Input channels. + context_channels (int): Context channels. + hw_image_size (list[int]): Spatial dims. + context_length (int): Context token count. + + Returns: + None + + Raises: + AssertionError: If gradients are absent for input or context. + """ + x = torch.randn(batch_size, channels, *hw_image_size, requires_grad=True) + context = torch.randn(batch_size, context_channels, context_length, requires_grad=True) + out = attn_block(x, context) + out.mean().backward() # type: ignore + assert x.grad is not None + assert context.grad is not None + + def test_attention_self_equivalence( + self, batch_size: int, hw_image_size: list[int], channels: int, inner_channels: int, num_heads: int + ): + """Self-attention result must equal cross-attention when context == flattened x. + + Args: + batch_size (int): Batch size. + hw_image_size (list[int]): Spatial dims [H, W]. + channels (int): Input channels. + inner_channels (int): Projection channels. + num_heads (int): Number of heads. + + Returns: + None + + Raises: + AssertionError: If self and cross attention outputs differ beyond tolerance. + """ + block = AttentionBlock(channels=channels, inner_channels=inner_channels, num_heads=num_heads) + x = torch.randn(batch_size, channels, *hw_image_size) + out_self = block(x, None) + context = x.view(batch_size, channels, -1) + out_cross = block(x, context) + assert torch.allclose(out_self, out_cross, atol=1e-5) + + +# --------------------------------------------------------------------------- +# UNetModel Tests +# --------------------------------------------------------------------------- +class TestUNetModel: + """Comprehensive tests for the full UNetModel denoiser. + + Focus Areas: + * Initialization under class-conditional and context-conditional modes. + * Forward pass shape correctness across conditioning configurations. + * Classifier-free guidance probability masking semantics. + * Auxiliary image concatenation (x_context) path correctness. + * Non-square and mismatched spatial size handling (assertions). + * Architectural permutations: channel multipliers, scale-shift norm, resblock + up/down, gradient checkpointing, multi-attention resolutions. + * Output head zero-initialization and timestep dtype flexibility (int/float). + * Gradient flow through conditioning pathways. + + Test Methods: + - test_init_class_cond + - test_init_context_cond + - test_forward_class_cond + - test_forward_context_cond + - test_forward_with_x_context + - test_classifier_free_guidance_probability + - test_invalid_missing_labels + - test_invalid_missing_context + - test_invalid_p_without_classifier_free + - test_non_square_image + - test_incorrect_image_size_assert + - test_gradient_flow + - test_variable_channel_mult + - test_use_scale_shift_norm + - test_resblock_updown + - test_use_checkpoint + - test_output_zero_init + - test_integer_and_float_timesteps + - test_multiple_attention_resolutions + """ + + @pytest.fixture(scope="class") + def hw_image_size(self) -> list[int]: + """Canonical spatial size for UNet inputs (H, W).""" + return [64, 64] + + @pytest.fixture(scope="class") + def in_channels(self) -> int: + """Input image channel count (e.g., RGB=3).""" + return 3 + + @pytest.fixture(scope="class") + def model_channels(self) -> int: + """Base channel width for UNet encoder/decoder stages.""" + return 64 + + @pytest.fixture(scope="class") + def out_channels(self) -> int: + """Number of prediction channels (often equals in_channels).""" + return 3 + + @pytest.fixture(scope="class") + def num_res_blocks(self) -> int: + """Residual blocks per resolution level.""" + return 2 + + @pytest.fixture(scope="class") + def attention_resolutions(self) -> list[int]: + """Downsample factors (relative to input) where attention blocks are inserted.""" + return [4] + + @pytest.fixture(scope="class") + def num_classes(self) -> int: + """Number of discrete class labels for class conditioning tests.""" + return 10 + + @pytest.fixture(scope="class") + def channel_mult(self) -> str: + """Comma-separated multipliers expanding channels across resolutions.""" + return "1,2" + + @pytest.fixture(scope="class") + def num_heads(self) -> int: + """Attention heads in spatial attention modules.""" + return 4 + + @pytest.fixture(scope="class") + def sample_images(self, batch_size: int, in_channels: int, hw_image_size: list[int]) -> Tensor: + """Random batch of input images for forward passes.""" + return torch.randn(batch_size, in_channels, *hw_image_size) + + @pytest.fixture(scope="class") + def timesteps(self, batch_size: int) -> Tensor: + """Random float timesteps in [0,1) used for time embedding path.""" + return torch.rand(batch_size) + + @pytest.fixture(scope="class") + def sample_labels(self, batch_size: int, num_classes: int) -> Tensor: + """Random integer class labels for classifier-free guidance tests.""" + return torch.randint(0, num_classes, (batch_size,)) + + @pytest.fixture(scope="class") + def batch_size(self) -> int: + """Standard batch size for UNet tests.""" + return 2 + + @pytest.fixture(scope="class") + def unet_class_cond( + self, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + out_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + num_classes: int, + channel_mult: str, + num_heads: int, + ) -> UNetModel: + """Instantiate a class-conditional UNet with classifier-free guidance enabled.""" + return UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=out_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + n_classes=num_classes, + classifier_free=True, + channel_mult=channel_mult, + num_heads=num_heads, + ) + + @pytest.fixture(scope="class") + def unet_context_cond( + self, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + out_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + mock_context_embedder: MockContextEmbedder, + channel_mult: str, + num_heads: int, + ) -> UNetModel: + """Instantiate a context-conditional UNet using a mock ContextEmbedder.""" + return UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=out_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + context_embedder=mock_context_embedder, + channel_mult=channel_mult, + num_heads=num_heads, + ) + + def test_init_class_cond(self, unet_class_cond: UNetModel, num_classes: int): + """Confirm components for class conditioning (label embedding) are created. + + Args: + unet_class_cond (UNetModel): Class-conditional UNet instance. + num_classes (int): Expected number of classes. + + Returns: + None + + Raises: + AssertionError: If label embedding or class count not configured properly. + """ + assert unet_class_cond.n_classes == num_classes + assert unet_class_cond.label_embed is not None + assert unet_class_cond.context_embedder is None + # Extract first layer of first EmbedSequential block + first_block = unet_class_cond.input_blocks[0] + submodules = [m for m in first_block.modules() if m is not first_block] + first = submodules[0] if submodules else None + assert isinstance(first, nn.Conv2d) + + def test_init_context_cond(self, unet_context_cond: UNetModel, mock_context_embedder: MockContextEmbedder): + """Verify context embedder is wired and middle block contains attention modules. + + Args: + unet_context_cond (UNetModel): Context-conditional UNet instance. + mock_context_embedder (MockContextEmbedder): Provided mock embedder. + + Returns: + None + + Raises: + AssertionError: If embedder not assigned or attention modules missing. + """ + assert unet_context_cond.context_embedder is mock_context_embedder + assert unet_context_cond.label_embed is None + # Middle block has AttentionBlock + assert any(isinstance(m, AttentionBlock) for m in unet_context_cond.middle_block) + + def test_forward_class_cond( + self, + unet_class_cond: UNetModel, + sample_images: Tensor, + sample_labels: Tensor, + timesteps: Tensor, + ): + """Run forward pass with class labels and validate output tensor shape. + + Args: + unet_class_cond (UNetModel): Class-conditional model. + sample_images (Tensor): Input images (B, C, H, W). + sample_labels (Tensor): Class labels (B,). + timesteps (Tensor): Timestep values (B,). + + Returns: + None + + Raises: + AssertionError: If output key or shape is incorrect. + """ + out: ModelOutput = unet_class_cond(sample_images, timesteps, y=sample_labels) + assert "x" in out + assert out["x"].shape == sample_images.shape + + def test_forward_context_cond( + self, + unet_context_cond: UNetModel, + sample_images: Tensor, + timesteps: Tensor, + ): + """Forward with context conditioning; uses batch size as synthetic context input. + + Args: + unet_context_cond (UNetModel): Context-conditioned model. + sample_images (Tensor): Input images (B, C, H, W). + timesteps (Tensor): Timestep values (B,). + + Returns: + None + + Raises: + AssertionError: If output shape mismatches input. + """ + out: ModelOutput = unet_context_cond( + sample_images, timesteps, context=sample_images.shape[0] + ) # pass batch size + assert out["x"].shape == sample_images.shape + + def test_forward_with_x_context( + self, + sample_images: Tensor, + timesteps: Tensor, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + channel_mult: str, + num_heads: int, + ): + """Test auxiliary image concatenation path (x_context) doubles input channels. + + Args: + sample_images (Tensor): Primary input images. + timesteps (Tensor): Timestep values. + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Base input channels. + model_channels (int): Base model channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention insertion scales. + channel_mult (str): Channel multiplier string. + num_heads (int): Attention heads. + + Returns: + None + + Raises: + AssertionError: If output shape differs from original image shape. + """ + unet = UNetModel( + image_size=hw_image_size, + in_channels=in_channels * 2, + model_channels=model_channels, + out_channels=in_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + channel_mult=channel_mult, + num_heads=num_heads, + ) + # Provide additional context image (same spatial size) + x_context = torch.randn_like(sample_images) + out = unet(sample_images, timesteps, x_context=x_context) + assert out["x"].shape == sample_images.shape + + def test_classifier_free_guidance_probability( + self, unet_class_cond: UNetModel, sample_images: Tensor, timesteps: Tensor, sample_labels: Tensor + ): + """Ensure classifier-free guidance probability p executes masked label path without error. + + Args: + unet_class_cond (UNetModel): Class-conditional model with guidance enabled. + sample_images (Tensor): Input images. + timesteps (Tensor): Timestep tensor. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If output shape mismatches expectations. + """ + out = unet_class_cond(sample_images, timesteps, y=sample_labels, p=0.2) + assert out["x"].shape == sample_images.shape + + def test_invalid_missing_labels(self, unet_class_cond: UNetModel, sample_images: Tensor, timesteps: Tensor): + """Expect assertion if class-conditional model is called without labels. + + Args: + unet_class_cond (UNetModel): Class-conditional model. + sample_images (Tensor): Input batch. + timesteps (Tensor): Timesteps. + + Returns: + None + + Raises: + AssertionError: Always (validated via context manager) due to missing labels. + """ + with pytest.raises(AssertionError, match="must specify y"): + unet_class_cond(sample_images, timesteps) + + def test_invalid_missing_context(self, unet_context_cond: UNetModel, sample_images: Tensor, timesteps: Tensor): + """Expect assertion if context-conditional model lacks context input. + + Args: + unet_context_cond (UNetModel): Context-conditional model. + sample_images (Tensor): Input batch. + timesteps (Tensor): Timesteps. + + Returns: + None + + Raises: + AssertionError: Always when context not supplied. + """ + with pytest.raises(AssertionError, match="must specify context"): + unet_context_cond(sample_images, timesteps) + + def test_invalid_p_without_classifier_free( + self, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + out_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + num_classes: int, + channel_mult: str, + sample_images: Tensor, + timesteps: Tensor, + sample_labels: Tensor, + ): + """Calling with p when classifier_free=False should raise an assertion. + + Args: + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Input channels. + model_channels (int): Base model channels. + out_channels (int): Output channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention resolutions. + num_classes (int): Number of classes. + channel_mult (str): Channel mult string. + sample_images (Tensor): Input images. + timesteps (Tensor): Timesteps. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: Always (verified) because p provided while classifier_free disabled. + """ + model = UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=out_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + n_classes=num_classes, + classifier_free=False, + channel_mult=channel_mult, + ) + with pytest.raises( + AssertionError, + match="probability of dropping", + ): + model(sample_images, timesteps, y=sample_labels, p=0.1) + + def test_non_square_image( + self, + in_channels: int, + model_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + channel_mult: str, + num_classes: int, + batch_size: int, + timesteps: Tensor, + sample_labels: Tensor, + ): + """Verify model accepts non-square spatial dimensions and returns matching shape. + + Args: + in_channels (int): Input channels. + model_channels (int): Base model channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention resolutions. + channel_mult (str): Channel multipliers. + num_classes (int): Number of classes. + batch_size (int): Batch size. + timesteps (Tensor): Timesteps. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If output shape mismatches non-square input shape. + """ + img_size = [32, 48] + model = UNetModel( + image_size=img_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=in_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + channel_mult=channel_mult, + n_classes=num_classes, + classifier_free=True, + ) + x = torch.randn(batch_size, in_channels, *img_size) + out = model(x, timesteps, y=sample_labels) + assert out["x"].shape == x.shape + + def test_incorrect_image_size_assert(self, unet_class_cond: UNetModel, in_channels: int): + """Providing mismatched spatial size should trigger assertion guard. + + Args: + unet_class_cond (UNetModel): Configured UNet expecting specific image size. + in_channels (int): Channel count for synthetic mismatch tensor. + + Returns: + None + + Raises: + AssertionError: Always when provided image size differs from configured size. + """ + wrong = torch.randn(2, in_channels, 32, 32) + t = torch.randint(0, 1000, (2,)) + y = torch.randint(0, 10, (2,)) + with pytest.raises(AssertionError, match="Input shape"): + unet_class_cond(wrong, t, y=y) + + def test_gradient_flow( + self, unet_class_cond: UNetModel, sample_images: Tensor, timesteps: Tensor, sample_labels: Tensor + ): + """Ensure gradients flow to image tensor and timestep inputs. + + Args: + unet_class_cond (UNetModel): Model under test. + sample_images (Tensor): Input images (requires_grad set in test). + timesteps (Tensor): Timesteps (requires_grad set in test). + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If gradients are missing after backward pass. + """ + sample_images.requires_grad_(True) + timesteps.requires_grad_(True) + out = unet_class_cond(sample_images, timesteps, y=sample_labels) + out["x"].sum().backward() # type: ignore + assert sample_images.grad is not None + assert timesteps.grad is not None + + @pytest.mark.parametrize("channel_mult", ["1", "1,2", "1,2,4"]) + def test_variable_channel_mult( + self, + channel_mult: str, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + out_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + num_classes: int, + sample_images: Tensor, + timesteps: Tensor, + sample_labels: Tensor, + ): + """Smoke test different channel multiplier layouts for forward shape stability. + + Args: + channel_mult (str): Channel multiplier specification. + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Input channels. + model_channels (int): Base model channels. + out_channels (int): Output channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention resolutions. + num_classes (int): Number of classes. + sample_images (Tensor): Input images. + timesteps (Tensor): Timesteps. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If forward output shape deviates from input shape. + """ + model = UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=out_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + channel_mult=channel_mult, + n_classes=num_classes, + classifier_free=True, + ) + out = model(sample_images, timesteps, y=sample_labels) + assert out["x"].shape == sample_images.shape + + def test_use_scale_shift_norm( + self, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + channel_mult: str, + num_classes: int, + ): + """Verify enabling use_scale_shift_norm applies to all ResBlocks. + + Args: + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Input channels. + model_channels (int): Base model channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention resolutions. + channel_mult (str): Channel multiplier spec. + num_classes (int): Number of classes. + + Returns: + None + + Raises: + AssertionError: If any ResBlock lacks scale-shift norm when enabled. + """ + model = UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=in_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + channel_mult=channel_mult, + n_classes=num_classes, + classifier_free=True, + use_scale_shift_norm=True, + ) + resblocks = [m for m in model.modules() if isinstance(m, ResBlock)] + assert all(rb.use_scale_shift_norm for rb in resblocks) + + def test_resblock_updown( + self, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + channel_mult: str, + num_classes: int, + ): + """Check presence of ResBlocks configured for up/down sampling when resblock_updown=True. + + Args: + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Input channels. + model_channels (int): Base model channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention resolutions. + channel_mult (str): Channel multiplier spec. + num_classes (int): Number of classes. + + Returns: + None + + Raises: + AssertionError: If no up/down sampling ResBlocks are found. + """ + model = UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=in_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + channel_mult=channel_mult, + n_classes=num_classes, + classifier_free=True, + resblock_updown=True, + ) + # Ensure there are ResBlocks with up/down flags + ups = [m for m in model.modules() if isinstance(m, ResBlock) and m.updown] + assert len(ups) > 0 + + def test_use_checkpoint( + self, + hw_image_size: list[int], + in_channels: int, + model_channels: int, + num_res_blocks: int, + attention_resolutions: list[int], + channel_mult: str, + num_classes: int, + sample_images: Tensor, + timesteps: Tensor, + sample_labels: Tensor, + ): + """Smoke test that gradient checkpoint flag does not break forward pass. + + Args: + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Input channels. + model_channels (int): Base model channels. + num_res_blocks (int): Residual blocks per level. + attention_resolutions (list[int]): Attention resolutions. + channel_mult (str): Channel multiplier specification. + num_classes (int): Number of classes. + sample_images (Tensor): Input images. + timesteps (Tensor): Timesteps. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If forward output shape mismatches input shape. + """ + model = UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=in_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=attention_resolutions, + channel_mult=channel_mult, + n_classes=num_classes, + classifier_free=True, + use_checkpoint=True, + ) + out = model(sample_images, timesteps, y=sample_labels) + assert out["x"].shape == sample_images.shape + + def test_output_zero_init( + self, + unet_class_cond: UNetModel, + sample_images: Tensor, + timesteps: Tensor, + sample_labels: Tensor, + ): + """Check that output head is zero-initialized (common for stable training). + + Args: + unet_class_cond (UNetModel): Model instance. + sample_images (Tensor): Input images. + timesteps (Tensor): Timesteps. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If output deviates from near-zero initialization. + """ + out = unet_class_cond(sample_images, timesteps, y=sample_labels)["x"] + assert torch.allclose(out, torch.zeros_like(out), atol=1e-6) + + def test_integer_and_float_timesteps( + self, + unet_class_cond: UNetModel, + sample_images: Tensor, + sample_labels: Tensor, + ): + """Verify model accepts both integer (discrete) and float timesteps equally. + + Args: + unet_class_cond (UNetModel): Model instance. + sample_images (Tensor): Input images. + sample_labels (Tensor): Class labels. + + Returns: + None + + Raises: + AssertionError: If output shapes differ between int and float timestep runs. + """ + t = torch.randint(0, 1000, (sample_images.shape[0],)) + out = unet_class_cond(sample_images, t, y=sample_labels)["x"] + assert out.shape == sample_images.shape + t = torch.rand(sample_images.shape[0]) + out_float = unet_class_cond(sample_images, t, y=sample_labels)["x"] + assert out_float.shape == sample_images.shape + + def test_multiple_attention_resolutions( + self, hw_image_size: list[int], in_channels: int, model_channels: int, num_res_blocks: int, num_classes: int + ): + """Count attention blocks for multiple attention resolutions and deeper channel_mult. + + Args: + hw_image_size (list[int]): Image size [H, W]. + in_channels (int): Input channels. + model_channels (int): Base model channels. + num_res_blocks (int): Residual blocks per level. + num_classes (int): Number of classes. + + Returns: + None + + Raises: + AssertionError: If discovered attention block count differs from expected. + """ + model = UNetModel( + image_size=hw_image_size, + in_channels=in_channels, + model_channels=model_channels, + out_channels=in_channels, + num_res_blocks=num_res_blocks, + attention_resolutions=[1, 2, 4], + channel_mult="1,2,4", + n_classes=num_classes, + classifier_free=True, + ) + num_attn = sum(1 for m in model.modules() if isinstance(m, AttentionBlock)) + assert num_attn == 16