v1.2.0

PhysicsNeMo General Release v1.2.0

Added

• Diffusion Transformer (DiT) model. The DiT model can be accessed in
  physicsnemo.experimental.models.dit.DiT. ⚠️Warning: - Experimental feature
  subject to future API changes.
• Improved documentation for diffusion models and diffusion utils.
• Safe API to override __init__'s arguments saved in checkpoint file with
  Module.from_checkpoint("chkpt.mdlus", override_args=set(...)).
• PyTorch Geometric MeshGraphNet backend.
• Functionality in DoMINO to take arbitrary number of scalar or vector
  global parameters and encode them using class ParameterModel
• TopoDiff model and example.
• Added ability for DoMINO model to return volume neighbors.
• Added functionality in DoMINO recipe to introduce physics residual losses.
• Diffusion models, metrics, and utils: implementation of Student-t
  distribution for EDM-based diffusion models (t-EDM). This feature is adapted
  from the paper Heavy-Tailed Diffusion Models, Pandey et al..
  This includes a new EDM preconditioner (tEDMPrecondSuperRes), a loss
  function (tEDMResidualLoss), and a new option in corrdiff diffusion_step.
  ⚠️ This is an experimental feature that can be accessed through the
  physicsnemo.experimental module; it might also be subjected to API changes
  without notice.
• Bumped Ruff version from 0.0.290 to 0.12.5. Replaced Black with ruff-format.
• Domino improvements with Unet attention module and user configs
• Hybrid MeshGraphNet for modeling structural deformation
• Enabled TransformerEngine backend in the transolver model.
• Inference code for x-meshgraphnet example for external aerodynamics.
• Added a new example for external_aerodynamics: training transolver on
  irregular mesh data for DrivaerML surface data.
• Added a new example for external aerodynamics for finetuning pretrained models.

Changed

• Diffusion utils: physicsnemo.utils.generative renamed into physicsnemo.utils.diffusion
• Diffusion models: in CorrDiff model wrappers (EDMPrecondSuperResolution and
  UNet), the arguments profile_mode and amp_mode cannot be overriden by
  from_checkpoint. They are now properties that can be dynamically changed
  after the model instantiation with, for example, model.amp_mode = True
  and model.profile_mode = False.
• Updated healpix data module to use correct DistributedSampler target for
  test data loader
• Existing DGL-based vortex shedding example has been renamed to vortex_shedding_mgn_dgl.
  Added new vortex_shedding_mgn example that uses PyTorch Geometric instead.
• HEALPixLayer can now use earth2grid HEALPix padding ops, if desired
• Migrated Vortex Shedding Reduced Mesh example to PyTorch Geometric.
• CorrDiff example: fixed bugs when training regression UNet.
• Diffusion models: fixed bugs related to gradient checkpointing on non-square
  images.
• Diffusion models: created a separate class Attention for clarity and
  modularity. Updated UNetBlock accordingly to use the Attention class
  instead of custom attention logic. This will update the model architecture
  for SongUNet-based diffusion models. Changes are not BC-breaking and are
  transparent to the user.
• ⚠️ BC-breaking: refactored the automatic mixed precision
  (AMP) API in layers and models defined in physicsnemo/models/diffusion/ for
  improved usability. Note: it is now, not only possible, but required to
  explicitly set model.amp_mode = True in order to use the model in a
  torch.autocast clause. This applies to all SongUNet-based models.
• Diffusion models: fixed and improved API to enable fp16 forward pass in
  UNet and EDMPrecondSuperResolution model wrappers; fp16 forward pass can
  now be toggled/untoggled by setting model.use_fp16 = True.
• Diffusion models: improved API for Apex group norm. SongUNet-based models
  will automatically perform conversion of the input tensors to
  torch.channels_last memory format when model.use_apex_gn is True. New
  warnings are raised when attempting to use Apex group norm on CPU.
• Diffusion utils: systematic compilation of patching operations in stochastic_sampler
  for improved performance.
• CorrDiff example: added option for Student-t EDM (t-EDM) in train.py and
  generate.py. When training a CorrDiff diffusion model, this feature can be
  enabled with the hydra overrides ++training.hp.distribution=student_t and
  ++training.hp.nu_student_t=<nu_value>. For generation, this feature can be
  enabled with similar overrides: ++generation.distribution=student_t and
  ++generation.nu_student_t=<nu_value>.
• CorrDiff example: the parameters P_mean and P_std (used to compute the
  noise level sigma) are now configurable. They can be set with the hydra
  overrides ++training.hp.P_mean=<P_mean_value> and
  ++training.hp.P_std=<P_std_value> for training (and similar ones with
  training.hp replaced by generation for generation).
• Diffusion utils: patch-based inference and lead time support with
  deterministic sampler.
• Existing DGL-based XAeroNet example has been renamed to xaeronet_dgl.
  Added new xaeronet example that uses PyTorch Geometric instead.
• Updated the deforming plate example to use the Hybrid MeshGraphNet model.
• ⚠️ BC-breaking: Refactored the transolver model to improve
  readability and performance, and extend to more use cases.
• Diffusion models: improved lead time support for SongUNetPosLtEmbd and
  EDMLoss. Lead-time embeddings can now be used with/without positional
  embeddings.
• Diffusion models: consolidate ApexGroupNorm and GroupNorm in
  models/diffusion/layers.py with a factory get_group_norm that can
  be used to instantiate either one of them. get_group_norm is now the
  recommended way to instantiate a GroupNorm layer in SongUNet-based and
  other diffusion models.
• Physicsnemo models: improved checkpoint loading API in
  Module.from_checkpoint that now exposes a strict parameter to raise error
  on missing/unexpected keys, similar to that used in
  torch.nn.Module.load_state_dict.
• Migrated Hybrid MGN and deforming plate example to PyTorch Geometric.

Fixed

• Bug fixes in DoMINO model in sphere sampling and tensor reshaping
• Bug fixes in DoMINO utils random sampling and test.py
• Optimized DoMINO config params based on DrivAer ML

v1.1.1

PhysicsNeMo (Core) General Release v1.1.1 (patch to v1.1.0)

Fixed

• Fixed an inadvertent change to the deterministic sampler 2nd order correction

v1.1.0

PhysicsNeMo (Core) General Release v1.1.0

Added

• Added ReGen score-based data assimilation example
• General purpose patching API for patch-based diffusion
• New positional embedding selection strategy for CorrDiff SongUNet models
• Added Multi-Storage Client to allow checkpointing to/from Object Storage

Changed

• Simplified CorrDiff config files, updated default values
• Refactored CorrDiff losses and samplers to use the patching API
• Support for non-square images and patches in patch-based diffusion
• ERA5 download example updated to use current file format convention and
  restricts global statistics computation to the training set
• Support for training custom StormCast models and various other improvements for StormCast
• Updated CorrDiff training code to support multiple patch iterations to amortize
  regression cost and usage of torch.compile
• Refactored physicsnemo/models/diffusion/layers.py to optimize data type
  casting workflow, avoiding unnecessary casting under autocast mode
• Refactored Conv2d to enable fusion of conv2d with bias addition
• Refactored GroupNorm, UNetBlock, SongUNet, SongUNetPosEmbd to support usage of
  Apex GroupNorm, fusion of activation with GroupNorm, and AMP workflow.
• Updated SongUNetPosEmbd to avoid unnecessary HtoD Memcpy of pos_embd
• Updated from_checkpoint to accommodate conversion between Apex optimized ckp
  and non-optimized ckp
• Refactored CorrDiff NVTX annotation workflow to be configurable
• Refactored ResidualLoss to support patch-accumlating training for
  amortizing regression costs
• Explicit handling of Warp device for ball query and sdf
• Merged SongUNetPosLtEmb with SongUNetPosEmb, add support for batch>1
• Add lead time embedding support for positional_embedding_selector. Enable
  arbitrary positioning of probabilistic variables
• Enable lead time aware regression without CE loss
• Bumped minimum PyTorch version from 2.0.0 to 2.4.0, to minimize
  support surface for physicsnemo.distributed functionality.

Dependencies

• Made nvidia.dali an optional dependency

v1.0.1

PhysicsNeMo (Core) General Release v1.0.1

Added

• Added version checks to ensure compatibility with older PyTorch for distributed utilities and ShardTensor

Fixed

• EntryPoint error that occured during physicsnemo checkpoint loading

v1.0.0

PhysicsNeMo (Core) General Release v1.0.0

Added

• DoMINO model architecture, datapipe and training recipe
• Added matrix decomposition scheme to improve graph partitioning
• DrivAerML dataset support in FIGConvNet example.
• Retraining recipe for DoMINO from a pretrained model checkpoint
• Prototype support for domain parallelism of using ShardTensor (new).
• Enable DeviceMesh initialization via DistributedManager.
• Added Datacenter CFD use case.
• Add leave-in profiling utilities to physicsnemo, to easily enable torch/python/nsight
  profiling in all aspects of the codebase.

Changed

• Refactored StormCast training example
• Enhancements and bug fixes to DoMINO model and training example
• Enhancement to parameterize DoMINO model with inlet velocity
• Moved non-dimensionaliztion out of domino datapipe to datapipe in domino example
• Updated utils in physicsnemo.launch.logging to avoid unnecessary wandb and mlflow
  imports
• Moved to experiment-based Hydra config in Lagrangian-MGN example
• Make data caching optional in MeshDatapipe
• The use of older importlib_metadata library is removed

Deprecated

• ProcessGroupConfig is tagged for future deprecation in favor of DeviceMesh.

Fixed

• Update pytests to skip when the required dependencies are not present
• Bug in data processing script in domino training example
• Fixed NCCL_ASYNC_ERROR_HANDLING deprecation warning

Dependencies

• Remove the numpy dependency upper bound
• Moved pytz and nvtx to optional
• Update the base image for the Dockerfile
• Introduce Multi-Storage Client (MSC) as an optional dependency.
• Introduce wrapt as an optional dependency, needed when using
  ShardTensor's automatic domain parallelism

v0.9.0

Modulus (core) general release v0.9.0

Added

• FIGConvUNet model and example.
• The Transolver model.
• The XAeroNet model.
• Incoporated CorrDiff-GEFS-HRRR model into CorrDiff, with lead-time aware SongUNet and
  cross entropy loss.

Changed

• Refactored EDMPrecondSRV2 preconditioner and fixed the bug related to the metadata
• Extended the checkpointing utility to store metadata.
• Corrected missing export of loggin function used by transolver model

v0.8.0

Modulus (core) general release v0.8.0

Added

• Graph Transformer processor for GraphCast/GenCast.
• Utility to generate STL from Signed Distance Field.
• Metrics for CAE and CFD domain such as integrals, drag, and turbulence invariances and
  spectrum.
• Added gradient clipping to StaticCapture utilities.
• Bistride Multiscale MeshGraphNet example.

Changed

• Refactored CorrDiff training recipe for improved usability
• Fixed timezone calculation in datapipe cosine zenith utility.

v0.7.0

Modulus (core) general release v0.7.0

Added

• Code logging for CorrDiff via Wandb.
• Augmentation pipeline for CorrDiff.
• Regression output as additional conditioning for CorrDiff.
• Learnable positional embedding for CorrDiff.
• Support for patch-based CorrDiff training and generation (stochastic sampling only)
• Enable CorrDiff multi-gpu generation
• Diffusion model for fluid data super-resolution (CMU contribution).
• The Virtual Foundry GraphNet.
• A synthetic dataloader for global weather prediction models, demonstrated on GraphCast.
• Sorted Empirical CDF CRPS algorithm
• Support for history, cos zenith, and downscaling/upscaling in the ERA5 HDF5 dataloader.
• An example showing how to train a "tensor-parallel" version of GraphCast on a
  Shallow-Water-Equation example.
• 3D UNet
• AeroGraphNet example of training of MeshGraphNet on Ahmed body and DrivAerNet datasets.
• Warp SDF routine
• DLWP HEALPix model
• Pangu Weather model
• Fengwu model
• SwinRNN model
• Modulated AFNO model

Changed

• Raise ModulusUndefinedGroupError when querying undefined process groups
• Changed Indexing error in examples/cfd/swe_nonlinear_pino for modulus loss function
• Safeguarding against uninitialized usage of DistributedManager

Removed

• Remove mlflow from deployment image

Fixed

• Fixed bug in the partitioning logic for distributing graph structures
  intended for distributed message-passing.
• Fixed bugs for corrdiff diffusion training of EDMv1 and EDMv2

Dependencies

• Update DALI to CUDA 12 compatible version.
• Update minimum python version to 3.10

v0.6.0

Modulus (core) general release v0.6.0

Added

• Added citation file
• Link to the CWA dataset
• ClimateDatapipe: an improved datapipe for HDF5/NetCDF4 formatted climate data
• Performance optimizations to CorrDiff
• Physics-Informed Nonlinear Shallow Water Equations example
• Warp neighbor search routine with a minimal example
• Strict option for loading Modulus checkpoints
• Regression only or diffusion only inference for CorrDiff
• Support for organization level model files on NGC file system
• Physics-Informed Magnetohydrodynamics example

Changed

• Updated Ahmed Body and Vortex Shedding examples to use Hydra config
• Added more config options to FCN AFNO example
• Moved posiitonal embedding in CorrDiff from the dataloader to network architecture

Deprecated

• modulus.models.diffusion.preconditioning.EDMPrecondSR. Use EDMPecondSRV2 instead

Removed

• Pickle dependency for CorrDiff

Fixed

• Consistent handling of single GPU runs in DistributedManager
• Output location of objects downloaded with NGC file system
• Bug in scaling the conditional input in CorrDiff deterministic sampler

Dependencies

• Updated DGL build in Dockerfile
• Updated default base image
• Moved Onnx from optional to required dependencies
• Optional Makani dependency required for SFNO model

v0.5.0

Modulus (core) general release v0.5.0

Added

• Distributed process group configuration mechanism.
• DistributedManager utility to instantiate process groups based on a process group config.
• Helper functions to facilitate distributed training with shared parameters.
• Brain anomaly detection example.
• Updated Frechet Inception Distance to use Wasserstein 2-norm with improved stability.
• Molecular Dynamics example.
• Improved usage of GraphPartition, added more flexible ways of defining a partitioned graph.
• Physics-Informed Stokes Flow example.

Changed

• MLFLow logging such that only proc 0 logs to MLFlow.
• FNO given separate methods for constructing lift and spectral encoder layers.

Removed

• The experimental SFNO

Dependencies

• Removed experimental SFNO dependencies
• Added CorrDiff dependencies (cftime, einops, pyspng)
• Made tqdm a required dependency