[1.3.3] - 2024-09-04

• Bug fixes
  • Fix an aliasing issue with zero-copy array initialization from NumPy introduced in Warp 1.3.0.
  • Fix wp.Volume.load_from_numpy() behavior when bg_value is a sequence of values.

[1.3.2] - 2024-08-30

• Bug fixes
  • Fix accuracy of 3x3 SVD wp.svd3 with fp64 numbers (GH-281).
  • Fix module hashing when a kernel argument contained a struct array (GH-287).
  • Fix a bug in wp.bvh_query_ray() where the direction instead of the reciprocal direction was used (GH-288).
  • Fix errors when launching a CUDA graph after a module is reloaded. Modules that were used during graph capture will no longer be unloaded before the graph is released.
  • Fix a bug in wp.sim.collide.triangle_closest_point_barycentric() where the returned barycentric coordinates may be incorrect when the closest point lies on an edge.
  • Fix 32-bit overflow when array shape is specified using np.int32.
  • Fix handling of integer indices in the input_output_mask argument to autograd.jacobian and autograd.jacobian_fd (GH-289).
  • Fix ModelBuilder.collapse_fixed_joints() to correctly update the body centers of mass and the ModelBuilder.articulation_start array.
  • Fix precedence of closure constants over global constants.
  • Fix quadrature point indexing in wp.fem.ExplicitQuadrature (regression from 1.3.0).
• Documentation improvements
  • Add missing return types for built-in functions.
  • Clarify that atomic operations also return the previous value.
  • Clarify that wp.bvh_query_aabb() returns parts that overlap the bounding volume.

[1.3.1] - 2024-07-27

• Remove wp.synchronize() from PyTorch autograd function example
• Tape.check_kernel_array_access() and Tape.reset_array_read_flags() are now private methods.
• Fix reporting unmatched argument types

[1.3.0] - 2024-07-25

• Warp Core improvements

  • Update to CUDA 12.x by default (requires NVIDIA driver 525 or newer), please see README.md for commands to install CUDA 11.x binaries for older drivers
  • Add information to the module load print outs to indicate whether a module was compiled (compiled), loaded from the cache (cached), or was unable to be loaded (error).
  • wp.config.verbose = True now also prints out a message upon the entry to a wp.ScopedTimer.
  • Add wp.clear_kernel_cache() to the public API. This is equivalent to wp.build.clear_kernel_cache().
  • Add code-completion support for wp.config variables.
  • Remove usage of a static task (thread) index for CPU kernels to address multithreading concerns (GH-224)
  • Improve error messages for unsupported Python operations such as sequence construction in kernels
  • Update wp.matmul() CPU fallback to use dtype explicitly in np.matmul() call
  • Add support for PEP 563's from __future__ import annotations (GH-256).
  • Allow passing external arrays/tensors to wp.launch() directly via __cuda_array_interface__ and __array_interface__, up to 2.5x faster conversion from PyTorch
  • Add faster Torch interop path using return_ctype argument to wp.from_torch()
  • Handle incompatible CUDA driver versions gracefully
  • Add wp.abs() and wp.sign() for vector types
  • Expose scalar arithmetic operators to Python's runtime (e.g.: wp.float16(1.23) * wp.float16(2.34))
  • Add support for creating volumes with anisotropic transforms
  • Allow users to pass function arguments by keyword in a kernel using standard Python calling semantics
  • Add additional documentation and examples demonstrating wp.copy(), wp.clone(), and array.assign() differentiability
  • Add __new__() methods for all class __del__() methods to handle when a class instance is created but not instantiated before garbage collection
  • Implement the assignment operator for wp.quat
  • Make the geometry-related built-ins available only from within kernels
  • Rename the API-facing query types to remove their _t suffix: wp.BVHQuery, wp.HashGridQuery, wp.MeshQueryAABB, wp.MeshQueryPoint, and wp.MeshQueryRay
  • Add wp.array(ptr=...) to allow initializing arrays from pointer addresses inside of kernels (GH-206)

• warp.autograd improvements:

  • New warp.autograd module with utility functions gradcheck(), jacobian(), and jacobian_fd() for debugging kernel Jacobians (docs)
  • Add array overwrite detection, if wp.config.verify_autograd_array_access is true in-place operations on arrays on the Tape that could break gradient computation will be detected (docs)
  • Fix bug where modification of @wp.func_replay functions and native snippets would not trigger module recompilation
  • Add documentation for dynamic loop autograd limitations

• warp.sim improvements:

  • Improve memory usage and performance for rigid body contact handling when self.rigid_mesh_contact_max is zero (default behavior).
  • The mask argument to wp.sim.eval_fk() now accepts both integer and boolean arrays to mask articulations.
  • Fix handling of ModelBuilder.joint_act in ModelBuilder.collapse_fixed_joints() (affected floating-base systems)
  • Fix and improve implementation of ModelBuilder.plot_articulation() to visualize the articulation tree of a rigid-body mechanism
  • Fix ShapeInstancer __new__() method (missing instance return and *args parameter)
  • Fix handling of upaxis variable in ModelBuilder and the rendering thereof in OpenGLRenderer

• warp.sparse improvements:

  • Sparse matrix allocations (from bsr_from_triplets(), bsr_axpy(), etc.) can now be captured in CUDA graphs; exact number of non-zeros can be optionally requested asynchronously.
  • bsr_assign() now supports changing block shape (including CSR/BSR conversions)
  • Add Python operator overloads for common sparse matrix operations, e.g A += 0.5 * B, y = x @ C

• warp.fem new features and fixes:

  • Support for variable number of nodes per element
  • Global wp.fem.lookup() operator now supports wp.fem.Tetmesh and wp.fem.Trimesh2D geometries
  • Simplified defining custom subdomains (wp.fem.Subdomain), free-slip boundary conditions
  • New field types: wp.fem.UniformField, wp.fem.ImplicitField and wp.fem.NonconformingField
  • New streamlines, magnetostatics and nonconforming_contact examples, updated mixed_elasticity to use a nonlinear model
  • Function spaces can now export VTK-compatible cells for visualization
  • Fixed edge cases with NanoVDB function spaces
  • Fixed differentiability of wp.fem.PicQuadrature w.r.t. positions and measures