v1.4.1

Changelog

[1.4.1] - 2024-10-15

Fixed

• Fix iter_reverse() not working as expected for ranges with steps other than 1 (GH-311).
• Fix potential out-of-bounds memory access when a wp.sparse.BsrMatrix object is reused for storing matrices of different shapes.
• Fix robustness to very low desired tolerance in wp.fem.utils.symmetric_eigenvalues_qr.
• Fix invalid code generation error messages when nesting dynamic and static for-loops.
• Fix caching of kernels with static expressions.
• Fix ModelBuilder.add_builder(builder) to correctly update articulation_start and thereby articulation_count when builder contains more than one articulation.
• Re-introduced the wp.rand*(), wp.sample*(), and wp.poisson() onto the Python scope to revert a breaking change.

v.1.4.0

CHANGELOG

[1.4.0] - 2024-10-01

Added

• Support for a new wp.static(expr) function that allows arbitrary Python expressions to be evaluated at the time of
  function/kernel definition (docs).
• Support for stream priorities to hint to the device that it should process pending work
  in high-priority streams over pending work in low-priority streams when possible
  (docs).
• Adaptive sparse grid geometry to warp.fem (docs).
• Support for defining wp.kernel and wp.func objects from within closures.
• Support for defining multiple versions of kernels, functions, and structs without manually assigning unique keys.
• Support for default argument values for user functions decorated with wp.func.
• Allow passing custom launch dimensions to jax_kernel() (GH-310).
• JAX interoperability examples for sharding and matrix multiplication (docs).
• Interoperability support for the PaddlePaddle ML framework (GH-318).
• Support wp.mod() for vector types (GH-282).
• Expose the modulo operator % to Python's runtime scalar and vector types.
• Support for fp64 atomic_add, atomic_max, and atomic_min (GH-284).
• Support for quaternion indexing (e.g. q.w).
• Support shadowing builtin functions (GH-308).
• Support for redefining function overloads.
• Add an ocean sample to the omni.warp extension.
• warp.sim.VBDIntegrator now supports body-particle collision.
• Add a contributing guide to the Sphinx docs .
• Add documentation for dynamic code generation (docs).

Changed

• wp.sim.Model.edge_indices now includes boundary edges.
• Unexposed wp.rand*(), wp.sample*(), and wp.poisson() from the Python scope.
• Skip unused functions in module code generation, improving performance.
• Avoid reloading modules if their content does not change, improving performance.
• wp.Mesh.points is now a property instead of a raw data member, its reference can be changed after the mesh is initialized.
• Improve error message when invalid objects are referenced in a Warp kernel.
• if/else/elif statements with constant conditions are resolved at compile time with no branches being inserted in the generated code.
• Include all non-hidden builtins in the stub file.
• Improve accuracy of symmetric eigenvalues routine in warp.fem.

Fixed

• Fix for wp.func erroring out when defining a Tuple as a return type hint (GH-302).
• Fix array in-place op (+=, -=) adjoints to compute gradients correctly in the backwards pass
• Fix vector, matrix in-place assignment adjoints to compute gradients correctly in the backwards pass, e.g.: v[1] = x
• Fix a bug in which Python docstrings would be created as local function variables in generated code.
• Fix a bug with autograd array access validation in functions from different modules.
• Fix a rare crash during error reporting on some systems due to glibc mismatches.
• Handle --num_tiles 1 in example_render_opengl.py (GH-306).
• Fix the computation of body contact forces in FeatherstoneIntegrator when bodies and particles collide.
• Fix bug in FeatherstoneIntegrator where eval_rigid_jacobian could give incorrect results or reach an infinite
  loop when the body and joint indices were not in the same order. Added Model.joint_ancestor to fix the indexing
  from a joint to its parent joint in the articulation.
• Fix wrong vertex index passed to add_edges() called from ModelBuilder.add_cloth_mesh() (GH-319).
• Add a workaround for uninitialized memory read warning in the compute-sanitizer initcheck tool when using wp.Mesh.
• Fix name clashes when Warp functions and structs are returned from Python functions multiple times.
• Fix name clashes between Warp functions and structs defined in different modules.
• Fix code generation errors when overloading generic kernels defined in a Python function.
• Fix issues with unrelated functions being treated as overloads (e.g., closures).
• Fix handling of stream argument in array.__dlpack__().
• Fix a bug related to reloading CPU modules.
• Fix a crash when kernel functions are not found in CPU modules.
• Fix conditions not being evaluated as expected in while statements.
• Fix printing Boolean and 8-bit integer values.
• Fix array interface type strings used for Boolean and 8-bit integer values.
• Fix initialization error when setting struct members.
• Fix Warp not being initialized upon entering a wp.Tape context.
• Use kDLBool instead of kDLUInt for DLPack interop of Booleans.

v1.3.3

[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

v1.3.2

[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)

v1.3.1

[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

v1.3.0

[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

v1.2.2

[1.2.2] - 2024-07-04

• Support for NumPy >= 2.0

[1.2.1] - 2024-06-14

• Fix generic function caching
• Fix Warp not being initialized when constructing arrays with wp.array()
• Fix wp.is_mempool_access_supported() not resolving the provided device arguments to wp.context.Device

[1.2.0] - 2024-06-06

• Add a not-a-number floating-point constant that can be used as wp.NAN or wp.nan.
• Add wp.isnan(), wp.isinf(), and wp.isfinite() for scalars, vectors, matrices, etc.
• Improve kernel cache reuse by hashing just the local module constants. Previously, a
  module's hash was affected by all wp.constant() variables declared in a Warp program.
• Revised module compilation process to allow multiple processes to use the same kernel cache directory.
  Cached kernels will now be stored in hash-specific subdirectory.
• Add runtime checks for wp.MarchingCubes on field dimensions and size
• Fix memory leak in wp.Mesh BVH (GH-225)
• Use C++17 when building the Warp library and user kernels
• Increase PTX target architecture up to sm_75 (from sm_70), enabling Turing ISA features
• Extended NanoVDB support (see warp.Volume):
  • Add support for data-agnostic index grids, allocation at voxel granularity
  • New wp.volume_lookup_index(), wp.volume_sample_index() and generic wp.volume_sample()/wp.volume_lookup()/wp.volume_store() kernel-level functions
  • Zero-copy aliasing of in-memory grids, support for multi-grid buffers
  • Grid introspection and blind data access capabilities
  • warp.fem can now work directly on NanoVDB grids using warp.fem.Nanogrid
  • Fixed wp.volume_sample_v() and wp.volume_store_*() adjoints
  • Prevent wp.volume_store() from overwriting grid background values
• Improve validation of user-provided fields and values in warp.fem
• Support headless rendering of wp.render.OpenGLRenderer via pyglet.options["headless"] = True
• wp.render.RegisteredGLBuffer can fall back to CPU-bound copying if CUDA/OpenGL interop is not available
• Clarify terms for external contributions, please see CONTRIBUTING.md for details
• Improve performance of wp.sparse.bsr_mm() by ~5x on benchmark problems
• Fix for XPBD incorrectly indexing into of joint actuations joint_act arrays
• Fix for mass matrix gradients computation in wp.sim.FeatherstoneIntegrator()
• Fix for handling of --msvc_path in build scripts
• Fix for wp.copy() params to record dest and src offset parameters on wp.Tape()
• Fix for wp.randn() to ensure return values are finite
• Fix for slicing of arrays with gradients in kernels
• Fix for function overload caching, ensure module is rebuilt if any function overloads are modified
• Fix for handling of bool types in generic kernels
• Publish CUDA 12.5 binaries for Hopper support, see https://github.com/nvidia/warp?tab=readme-ov-file#installing for details

v1.2.1

[1.2.1] - 2024-06-14

• Fix generic function caching
• Fix Warp not being initialized when constructing arrays with wp.array()
• Fix wp.is_mempool_access_supported() not resolving the provided device arguments to wp.context.Device

[1.2.0] - 2024-06-06

• Add a not-a-number floating-point constant that can be used as wp.NAN or wp.nan.
• Add wp.isnan(), wp.isinf(), and wp.isfinite() for scalars, vectors, matrices, etc.
• Improve kernel cache reuse by hashing just the local module constants. Previously, a
  module's hash was affected by all wp.constant() variables declared in a Warp program.
• Revised module compilation process to allow multiple processes to use the same kernel cache directory.
  Cached kernels will now be stored in hash-specific subdirectory.
• Add runtime checks for wp.MarchingCubes on field dimensions and size
• Fix memory leak in wp.Mesh BVH (GH-225)
• Use C++17 when building the Warp library and user kernels
• Increase PTX target architecture up to sm_75 (from sm_70), enabling Turing ISA features
• Extended NanoVDB support (see warp.Volume):
  • Add support for data-agnostic index grids, allocation at voxel granularity
  • New wp.volume_lookup_index(), wp.volume_sample_index() and generic wp.volume_sample()/wp.volume_lookup()/wp.volume_store() kernel-level functions
  • Zero-copy aliasing of in-memory grids, support for multi-grid buffers
  • Grid introspection and blind data access capabilities
  • warp.fem can now work directly on NanoVDB grids using warp.fem.Nanogrid
  • Fixed wp.volume_sample_v() and wp.volume_store_*() adjoints
  • Prevent wp.volume_store() from overwriting grid background values
• Improve validation of user-provided fields and values in warp.fem
• Support headless rendering of wp.render.OpenGLRenderer via pyglet.options["headless"] = True
• wp.render.RegisteredGLBuffer can fall back to CPU-bound copying if CUDA/OpenGL interop is not available
• Clarify terms for external contributions, please see CONTRIBUTING.md for details
• Improve performance of wp.sparse.bsr_mm() by ~5x on benchmark problems
• Fix for XPBD incorrectly indexing into of joint actuations joint_act arrays
• Fix for mass matrix gradients computation in wp.sim.FeatherstoneIntegrator()
• Fix for handling of --msvc_path in build scripts
• Fix for wp.copy() params to record dest and src offset parameters on wp.Tape()
• Fix for wp.randn() to ensure return values are finite
• Fix for slicing of arrays with gradients in kernels
• Fix for function overload caching, ensure module is rebuilt if any function overloads are modified
• Fix for handling of bool types in generic kernels
• Publish CUDA 12.5 binaries for Hopper support, see https://github.com/nvidia/warp?tab=readme-ov-file#installing for details

v1.2.0

[1.2.0] - 2024-06-06

• Add a not-a-number floating-point constant that can be used as wp.NAN or wp.nan.
• Add wp.isnan(), wp.isinf(), and wp.isfinite() for scalars, vectors, matrices, etc.
• Improve kernel cache reuse by hashing just the local module constants. Previously, a
  module's hash was affected by all wp.constant() variables declared in a Warp program.
• Revised module compilation process to allow multiple processes to use the same kernel cache directory.
  Cached kernels will now be stored in hash-specific subdirectory.
• Add runtime checks for wp.MarchingCubes on field dimensions and size
• Fix memory leak in wp.Mesh BVH (GH-225)
• Use C++17 when building the Warp library and user kernels
• Increase PTX target architecture up to sm_75 (from sm_70), enabling Turing ISA features
• Extended NanoVDB support (see warp.Volume):
  • Add support for data-agnostic index grids, allocation at voxel granularity
  • New wp.volume_lookup_index(), wp.volume_sample_index() and generic wp.volume_sample()/wp.volume_lookup()/wp.volume_store() kernel-level functions
  • Zero-copy aliasing of in-memory grids, support for multi-grid buffers
  • Grid introspection and blind data access capabilities
  • warp.fem can now work directly on NanoVDB grids using warp.fem.Nanogrid
  • Fixed wp.volume_sample_v() and wp.volume_store_*() adjoints
  • Prevent wp.volume_store() from overwriting grid background values
• Improve validation of user-provided fields and values in warp.fem
• Support headless rendering of wp.render.OpenGLRenderer via pyglet.options["headless"] = True
• wp.render.RegisteredGLBuffer can fall back to CPU-bound copying if CUDA/OpenGL interop is not available
• Clarify terms for external contributions, please see CONTRIBUTING.md for details
• Improve performance of wp.sparse.bsr_mm() by ~5x on benchmark problems
• Fix for XPBD incorrectly indexing into of joint actuations joint_act arrays
• Fix for mass matrix gradients computation in wp.sim.FeatherstoneIntegrator()
• Fix for handling of --msvc_path in build scripts
• Fix for wp.copy() params to record dest and src offset parameters on wp.Tape()
• Fix for wp.randn() to ensure return values are finite
• Fix for slicing of arrays with gradients in kernels
• Fix for function overload caching, ensure module is rebuilt if any function overloads are modified
• Fix for handling of bool types in generic kernels
• Publish CUDA 12.5 binaries for Hopper support, see https://github.com/nvidia/warp?tab=readme-ov-file#installing for details

[1.1.1] - 2024-05-24

• wp.init() is no longer required to be called explicitly and will be performed on first call to the API
• Speed up omni.warp.core's startup time

v1.1.0

[1.1.0] - 2024-05-09

• Support returning a value from @wp.func_native CUDA functions using type hints
• Improved differentiability of the wp.sim.FeatherstoneIntegrator
• Fix gradient propagation for rigid body contacts in wp.sim.collide()
• Added support for event-based timing, see wp.ScopedTimer()
• Added Tape visualization and debugging functions, see wp.Tape.visualize()
• Support constructing Warp arrays from objects that define the __cuda_array_interface__ attribute
• Support copying a struct to another device, use struct.to(device) to migrate struct arrays
• Allow rigid shapes to not have any collisions with other shapes in wp.sim.Model
• Change default test behavior to test redundant GPUs (up to 2x)
• Test each example in an individual subprocess
• Polish and optimize various examples and tests
• Allow non-contiguous point arrays to be passed to wp.HashGrid.build()
• Upgrade LLVM to 18.1.3 for from-source builds and Linux x86-64 builds
• Build DLL source code as C++17 and require GCC 9.4 as a minimum
• Array clone, assign, and copy are now differentiable
• Use Ruff for formatting and linting
• Various documentation improvements (infinity, math constants, etc.)
• Improve URDF importer, handle joint armature
• Allow builtins.bool to be used in Warp data structures
• Use external gradient arrays in backward passes when passed to wp.launch()
• Add Conjugate Residual linear solver, see wp.optim.linear.cr()
• Fix propagation of gradients on aliased copy of variables in kernels
• Facilitate debugging and speed up import warp by eliminating raising any exceptions
• Improve support for nested vec/mat assignments in structs
• Recommend Python 3.9 or higher, which is required for JAX and soon PyTorch.
• Support gradient propagation for indexing sliced multi-dimensional arrays, i.e. a[i][j] vs. a[i, j]
• Provide an informative message if setting DLL C-types failed, instructing to try rebuilding the library

[1.0.3] - 2024-04-17

• Add a support_level entry to the configuration file of the extensions