ChangeLog.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

• L=12 spherical harmonics

Fixed

• TensorProduct.visualize now works even if the TP is on the GPU.
• Github actions only trigger a push to coveralls if the corresponding token is set in github secrets.

[0.5.0] - 2022-04-13

Added

• Sparse Voxel Convolution
• Clebsch-Gordan coefficients are computed via a change of basis from the complex to real basis.
• o3, nn and io are accessible through e3nn. For instance e3nn.o3.rand_axis_angle.

Changed

• Since now the code is no more tested against torch==1.8.0, only tested against torch>=1.10.0

Fixed

• wigner_3j now always returns a contiguous copy regardless of dtype or device

[0.4.4] - 2021-12-15

Fixed

• Remove CartesianTensor._rtp. Instead recompute the ReducedTensorProduct everytime. The user can save the ReducedTensorProduct to avoid creating it each time.
• *equivariance_error no longer keeps around unneeded autograd graphs
• CartesianTensor builds ReducedTensorProduct with correct device/dtype when called without one

Added

• Created module for reflected imports allowing for nice syntax for creating irreps, e.g. from e3nn.o3.irreps import l3o # same as Irreps("o3")
• Add uvu<v mode for TensorProduct. Compute only the upper triangular part of the uv terms.
• (beta) TensorSquare. computes x \otimes x and decompose it.
• *equivariance_error now tell you which arguments had which error

Changed

• Give up the support of python 3.6, set python_requires='>=3.7' in setup
• Optimize a little bit ReducedTensorProduct: solve linear system only once per irrep instead of 2L+1 times.
• Do not scale line width by path_weight in TensorProduct.visualize
• *equivariance_error now transforms its inputs in float64 by default, regardless of the dtype used for the calculation itself

[0.4.3] - 2021-11-18

Fixed

• ReducedTensorProduct: replace QR decomposition by orthonormalize the projector X.T @ X. This keeps ReducedTensorProduct deterministic because the projectors and orthonormalize are both deterministic. The output of orthonormalize apears also to be highly sparse (luckily).

[0.4.2] - 2021-11-08

Added

• irrep_normalization and path_normalization for TensorProduct
• compile_right flag to TensorProduct
• Add new global flag jit_script_fx to optionally turn off torch.jit.script of fx code

[0.4.1] - 2021-10-29

Added

• Add to_cartesian() to CartesianTensor

Fixed

• make it work with pytorch 1.10.0

[0.4.0] - 2021-10-05

Changed

• Breaking change. normalization constants for TensorProduct and Linear. Now Linear(2x0e + 7x0e, 0e) is equivalent to Linear(9x0e, 0e). Models with inhomogeneous multiplicities will be affected by this change!

Fixed

• remove profiler.record_function calls that caused troubles with torchscript
• the home made implementation of radius_graph was ignoring the argument r_max

[0.3.5] - 2021-08-27

Fixed

• Extract uses CodeGenMixin to avoid strange recursion errors during training
• Add missing call to normalize in axis_angle_to_quaternion

[0.3.4] - 2021-08-20

Fixed

• ReducedTensorProducts: normalization and filter_ir_mid where not properly propagated through the recusive calls, this bug has no effects if the default values where used
• Use torch.linalg.eigh instead of the deprecated torch.symeig

Added

• (dev only) Pre-commit hooks that run pylint and flake8. These catch some common mistakes/style issues.
• classes to do SO(3) Grid transform (not fast) and Activation function using it
• Add f_in and f_out to o3.Linear
• PBC guide in the doc

[0.3.3] - 2021-06-21

Changed

• FullyConnectedNet is now a torch.nn.Sequential

Fixed

• BatchNorm was not equivariant for pseudo-scalars

Added

• biases argument to o3.Linear
• nn.models.v2106: MessagePassing takes a sequence of irreps
• nn.models.v2106: Convolution inpired from Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks

[0.3.2] - 2021-06-10

Added

• opt_einsum_fx as a dependency
• p=-1 option for Irreps.spherical_harmonics(lmax, p)

Removed

• Removed group/_linalg (has_rep_in_rep and intertwiners) (should use equivariant-MLP instead)

[0.3.1] - 2021-05-26

Added

• preprocess function in e3nn.nn.models.v2103.gate_points_networks.SimpleNetwork
• Specialized code for mode="uuw"
• instance argument to nn.BatchNorm

[0.3.0] - 2021-05-10

Added

• pool_nodes argument (default True) to networks in e3nn.nn.models.v2103.gate_points_networks
• Instruction support for o3.Linear
• o3.Linear.weight_views and o3.Linear.weight_view_for_instruction
• nn.Dropout

Changed

• o3.Linear and o3.FullyConnectedTensorProduct no longer automatically simplifies its irreps_in or irreps_out. If you want this behaviour, simplify your irreps explicitly!

Fixed

• TensorProduct can now gracefully handle multiplicities of zero
• weight_views/weight_view_for_instruction methods now support shared_weights=False

[0.2.9] - 2021-05-04

Added

• Normalization testing with assert_normalized
• Optional logging for equivariance and normalization tests
• Public e3nn.util.test.format_equivariance_error method for printing equivariance test results
• Module o3.SphericalHarmonicsAlphaBeta

Changed

• Generated code (modules like TensorProduct, Linear, Extract) now pickled using TorchScript IR, rather than Python source code.
• e3nn now only requires PyTorch >= 1.8.0 rather than 1.8.1
• Changed o3.legendre into a module o3.Legendre

Removed

• Removed e3nn.util.codegen.eval_code in favor of torch.fx

[0.2.8] - 2021-04-21

Added

• squared option to o3.Norm
• e3nn.nn.models.v2104.voxel_convolution.Convolution made to be resolution agnostic
• TensorProduct.visualize keyword argument aspect_ratio

Changed

• ReducedTensorProducts is a (scriptable) torch.nn.Module
• e3nn now requires the latest stable PyTorch, >=1.8.1
• TensorProduct.visualize: color of paths based on w.pow(2).mean() instead of w.sum().sign() * w.abs().sum()

Fixed

• No more NaN gradients of o3.Norm/nn.NormActivation at zero when using epsilon
• Modules with @compile_mode('trace') can now be compiled when their dtype and the current default dtype are different
• Fix errors in ReducedTensorProducts and add new tests

[0.2.7] - 2021-04-14

Added

• uuu connection mode in o3.TensorProduct now has specialized code

Fixed

• Fixed an issue with Activation (used by Gate). It was only applying the first activation function provided. Activation('0e+0e', [act1, act2]) was equivalent to Activation('2x0e', [act1]). Solved by removing the .simplify() applied to self.irreps_in.
• Gate will not accept non-scalar irreps_gates or irreps_scalars

[0.2.6] - 2021-04-12

Added

• e3nn.util.test.random_irreps convinience function for writing tests

Changed

• o3.Linear now has more efficient specialized code

Fixed

• Fixed a problem with temporary files on windows

[0.2.5] - 2021-04-07

Added

• Added e3nn.set_optimization_defaults() and e3nn.get_optimization_defaults()
• Constructors for empty Irreps: Irreps() and Irreps("")
• Additional tests, docs, and refactoring for Irrep and Irreps.
• Added TensorProduct.weight_views() and TensorProduct.weight_view_for_instruction()
• Fix Docs for ExtractIr

Changed

• Renamed o3.TensorProduct arguments in irreps_in1, irreps_in2 and irreps_out
• Renamed o3.spherical_harmonics arguement xyz into x
• Renamed math.soft_one_hot_linspace argument endpoint into cutoff, cutoff = not endpoint
• Variances are now provided to o3.TensorProduct through explicit in1_var, in2_var, out_var parameters
• Submodules define __all__; documentation uses shorter module names for the classes/methods.

Fixed

• Enabling/disabling einsum optimization no longer affects PyTorch RNG state.

Removed

• Variances can no longer be provided to o3.TensorProduct in the list-of-tuple format for irreps_in1, etc.

[0.2.4] - 2021-03-23

Added

• basis='smooth_finite' option to math.soft_one_hot_linspace
• math.soft_unit_step function
• nn.model.v2103 generic message passing model + examples of networks using it.
• o3.TensorProduct: is jit scriptable
• o3.TensorProduct: also broadcast the weight argument
• simple e3nn models can be saved/loaded with torch.save()/torch.load()
• JITable o3.SphericalHarmonics module version of o3.spherical_harmonics
• in_place option for e3nn.util.jit compilation functions
• New @compile_mode("unsupported") for modules that do not support TorchScript
• flake8 settings have been added to setup.cfg for improved code style
• TensorProduct.visualize() can now plot weights
• basis='bessel' option to math.soft_one_hot_linspace
• Optional optimization of TensorProduct if opt_einsum_fx is installed

Changed

• o3.TensorProduct now uses torch.fx to generate it's code
• e3nn now requires the latest stable PyTorch, >=1.8.0
• in soft_one_hot_linspace the argument base is renamed into basis
• Irreps.slices(), do zip(irreps.slices(), irreps) to retrieve the old behavior
• math.soft_one_hot_linspace very small change in the normalization of fourier basis
• normalize2mom is now a torch.nn.Module
• rename arguments set_ir_... into filter_ir_...
• Renamed e3nn.nn.Gate argument irreps_nonscalars to irreps_gated
• Renamed e3nn.o3.TensorProduct arguments x1, x2 to x, y

Fixed

• nn.Gate was crashing when the number of scalars or gates was zero
• device edge cases for Gate and SphericalHarmonics

[0.2.3] - 2021-02-23

Added

• Add argument basis into math.soft_one_hot_linspace that can take values gaussian, cosine and fourier
• io.SphericalTensor.sum_of_diracs
• Optional arguments function(..., device=None, dtype=None) for many functions
• e3nn.nn.models.gate_points_2102 using node attributes along the length embedding to feed the radial network
• Irreps.slices()
• Module Extract (and ExtractIr) to extract subsets of irreps tensors
• Recursive TorchScript compiler e3nn.util.jit
• TorchScript support for TensorProduct and subclasses, NormActivation, Gate, FullyConnectedNet, and gate_points_2101.Network

Changed

• in o3.TensorProduct.instructions: renamed weight_shape in path_shape and is now set even if has_weight is False
• o3.TensorProduct weights are now flattened tensors
• rename io.SphericalTensor.from_geometry_adjusted into io.SphericalTensor.with_peaks_at
• in ReducedTensorProducts, ElementwiseTensorProduct and FullTensorProduct: rename irreps_out argument into set_ir_out to not confuse it with o3.Irreps

Removed

• io.SphericalTensor.from_geometry_global_rescale
• e3nn.math.reduce.reduce_tensor in favor of e3nn.o3.ReducedTensorProducts
• swish, use torch.nn.functional.silu instead
• "cartesian_vectors" for equivariance testing — since the 0.2.2 Euler angle convention change, L=1 irreps are equivalent

Fixed

• io.SphericalTensor.from_samples_on_s2 manage batch dimension
• Modules that generate code now clean up their temporary files
• NormActivation now works on GPU

[0.2.2] - 2021-02-09

Changed

• Euler angle convention from ZYZ to YXY
• TensorProduct.weight_shapes content put into TensorProduct.instructions

Added

• Better TorchScript support