Fix compatibility with NumPy 2.4: np.trapz and np.in1d removed#739
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NumPy 2.4 removed np.trapz (deprecated in 2.0, replaced by np.trapezoid) and np.in1d (deprecated in 2.0, replaced by np.isin). Changes: - np.trapz overload: Only register when numpy_version < (2, 4) - np.trapezoid overload: Keep registration for numpy_version >= (2, 0) - Add internal _in1d_impl helper function using @register_jitable - np.in1d overload: Only register when numpy_version < (2, 4) - Update np.setdiff1d and np.isin to use _in1d_impl instead of np.in1d This follows the same approach used in numba/numba commits aa7cf35 and c39d72e. Fixes NVIDIA#726
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gmarkall
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Jan 21, 2026
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Greptile SummaryThis PR fixes compatibility with NumPy 2.4 by addressing the removal of deprecated functions
The changes follow the same approach used in upstream numba/numba commits (aa7cf35 and c39d72e) and maintain backward compatibility with older NumPy versions. Confidence Score: 5/5
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bdice
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Jan 21, 2026
Co-authored-by: Bradley Dice <bdice@bradleydice.com>
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January 21, 2026 19:11
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- Add Python 3.14 to the wheel publishing matrix (NVIDIA#750) - feat: swap out internal device array usage with `StridedMemoryView` (NVIDIA#703) - Fix max block size computation in `forall` (NVIDIA#744) - Fix prologue debug line info pointing to decorator instead of def line (NVIDIA#746) - Fix kernel return type in DISubroutineType debug metadata (NVIDIA#745) - Fix missing line info in Jupyter notebooks (NVIDIA#742) - Fix: Pass correct flags to linker when debugging in the presence of LTOIR code (NVIDIA#698) - chore(deps): add cuda-pathfinder to pixi deps (NVIDIA#741) - fix: enable flake8-bugbear lints and fix found problems (NVIDIA#708) - fix: Fix race condition in CUDA Simulator (NVIDIA#690) - ci: run tests in parallel (NVIDIA#740) - feat: users can pass `shared_memory_carveout` to @cuda.jit (NVIDIA#642) - Fix compatibility with NumPy 2.4: np.trapz and np.in1d removed (NVIDIA#739) - Pass the -numba-debug flag to libnvvm (NVIDIA#681) - ci: remove rapids containers from conda ci (NVIDIA#737) - Use `pathfinder` for dynamic libraries (NVIDIA#308) - CI: Add CUDA 13.1 testing support (NVIDIA#705) - Adding `pixi run test` and `pixi run test-par` support (NVIDIA#724) - Disable per-PR nvmath tests + follow same test practice (NVIDIA#723) - chore(deps): regenerate pixi lockfile (NVIDIA#722) - Fix DISubprogram line number to point to function definition line (NVIDIA#695) - revert: chore(dev): build pixi using rattler (NVIDIA#713) (NVIDIA#719) - [feat] Initial version of the Numba CUDA GDB pretty-printer (NVIDIA#692) - chore(dev): build pixi using rattler (NVIDIA#713) - build(deps): bump the actions-monthly group across 1 directory with 8 updates (NVIDIA#704)
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- Add Python 3.14 to the wheel publishing matrix (#750) - feat: swap out internal device array usage with `StridedMemoryView` (#703) - Fix max block size computation in `forall` (#744) - Fix prologue debug line info pointing to decorator instead of def line (#746) - Fix kernel return type in DISubroutineType debug metadata (#745) - Fix missing line info in Jupyter notebooks (#742) - Fix: Pass correct flags to linker when debugging in the presence of LTOIR code (#698) - chore(deps): add cuda-pathfinder to pixi deps (#741) - fix: enable flake8-bugbear lints and fix found problems (#708) - fix: Fix race condition in CUDA Simulator (#690) - ci: run tests in parallel (#740) - feat: users can pass `shared_memory_carveout` to @cuda.jit (#642) - Fix compatibility with NumPy 2.4: np.trapz and np.in1d removed (#739) - Pass the -numba-debug flag to libnvvm (#681) - ci: remove rapids containers from conda ci (#737) - Use `pathfinder` for dynamic libraries (#308) - CI: Add CUDA 13.1 testing support (#705) - Adding `pixi run test` and `pixi run test-par` support (#724) - Disable per-PR nvmath tests + follow same test practice (#723) - chore(deps): regenerate pixi lockfile (#722) - Fix DISubprogram line number to point to function definition line (#695) - revert: chore(dev): build pixi using rattler (#713) (#719) - [feat] Initial version of the Numba CUDA GDB pretty-printer (#692) - chore(dev): build pixi using rattler (#713) - build(deps): bump the actions-monthly group across 1 directory with 8 updates (#704) <!-- Thank you for contributing to numba-cuda :) Here are some guidelines to help the review process go smoothly. 1. Please write a description in this text box of the changes that are being made. 2. Please ensure that you have written units tests for the changes made/features added. 3. If you are closing an issue please use one of the automatic closing words as noted here: https://help.github.com/articles/closing-issues-using-keywords/ 4. If your pull request is not ready for review but you want to make use of the continuous integration testing facilities please label it with `[WIP]`. 5. If your pull request is ready to be reviewed without requiring additional work on top of it, then remove the `[WIP]` label (if present) and replace it with `[REVIEW]`. If assistance is required to complete the functionality, for example when the C/C++ code of a feature is complete but Python bindings are still required, then add the label `[HELP-REQ]` so that others can triage and assist. The additional changes then can be implemented on top of the same PR. If the assistance is done by members of the rapidsAI team, then no additional actions are required by the creator of the original PR for this, otherwise the original author of the PR needs to give permission to the person(s) assisting to commit to their personal fork of the project. If that doesn't happen then a new PR based on the code of the original PR can be opened by the person assisting, which then will be the PR that will be merged. 6. Once all work has been done and review has taken place please do not add features or make changes out of the scope of those requested by the reviewer (doing this just add delays as already reviewed code ends up having to be re-reviewed/it is hard to tell what is new etc!). Further, please do not rebase your branch on main/force push/rewrite history, doing any of these causes the context of any comments made by reviewers to be lost. If conflicts occur against main they should be resolved by merging main into the branch used for making the pull request. Many thanks in advance for your cooperation! -->
maxymnaumchyk
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Mar 3, 2026
…A/numba-cuda#739, which is now needed for using numpy>2.4
ianna
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Mar 13, 2026
…3894) * feat: customly overload the reducers that call cuda-compute kernels * overload reducers only if they are implemented in cuda/reducers.py * style: pre-commit fixes * treat complex values in a separate kernel * style: pre-commit fixes * import AnyType * pass starts.data * add offsets.data as an input to the argmin kernel * style: pre-commit fixes * add argmax reducer * empty commit to rerun the gpu tests * one more empty commit to rerun the gpu tests * delete old calls for cuda-compute(cccl) reducer kernels * style: pre-commit fixes * delete old cuda_kernels for argmin and argmax * add suggestions from Ianna * improvement from Ianna Co-authored-by: Ianna Osborne <ianna.osborne@cern.ch> * style: pre-commit fixes * add suggestions from Ianna * add AxisNoneReducerMax reducer * add AxisNoneReducerArgMax reducer * pass `needs_position: Final` for AxisNone reducers * pass `needs_position: True` for AxisNoneArgMax reducer only * try to apply positional corrections for the result of `AxisNoneReducerArgMax` * pass a superclass directly * try processing complex numbers as a separate case * if the two values are equal, keep the index of the FIRST max value we encounter * add two tests to see if the code path is actually using the overloaded reducers * style: pre-commit fixes * style fix * move `awkward_axis_none_reduce_argmax` to the reducers.py * add `AxisNoneReducerArgMin` * correct the parts with comparing the values * increase numba-cuda version requirement to 0.25. It has this PR NVIDIA/numba-cuda#739, which is now needed for using numpy>2.4 * ignore a `DeprecationWarning` from new `numba-cuda` version for now * for the equal values always return the one with the lowest index * Update layout.py * style: pre-commit fixes * for now call new cuda.compute kernels from `cupy.py`. They should be moved to `_connect/cuda/reducers.py` in the next PR * there is no need to return the result from inside the kernels --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Ianna Osborne <ianna.osborne@cern.ch>
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NumPy 2.4 removed
np.trapz(deprecated in 2.0, replaced bynp.trapezoid) andnp.in1d(deprecated in 2.0, replaced bynp.isin).Changes
np.trapzoverload: Only register whennumpy_version < (2, 4)np.trapezoidoverload: Keep registration fornumpy_version >= (2, 0)_in1d_implhelper function using@register_jitablenp.in1doverload: Only register whennumpy_version < (2, 4)np.setdiff1dandnp.isinto use_in1d_implinstead ofnp.in1dThis follows the same approach used in numba/numba commits aa7cf35 and c39d72e.
Fixes #726