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Minutes_2023_04_18
esc edited this page Apr 26, 2023
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Attendees: Siu Kwan Lam, brandon willard, Guilherme, Ianna Osborne, Jim Pivarski, stuart, Todd A. Anderson, Luk, Andre Masella FPOC (last week): Guilherme FPOC (incoming): Andre
NOTE: All communication is subject to the Numba Code of Conduct.
Please refer to this calendar for the next meeting date.
- Issue #8897: Replicate old generated_jit behavior on basis of overload for only-jitted variants of functions
-
generated_jit
was an older less flexible version ofoverload
and the plan was to deprecated and migrate - there was an unhandled case discovered after migration; if a Python function, with an overloaded stub, and you want to call it from Python, you need to provide a Python implementation; however, the Python interpreted version will be run, not the JITted version.
- ultimately, we have deprecated something without a direct analogue
- Options: 1) try to reimplement existing functionality, 2) say not to use it; we'd prefer to try to implement
generated_jit
usingoverload
- One possible prototype was developed by Stuart: https://github.com/numba/numba/issues/8466#issuecomment-1274593340
- caveats include: kwargs and using disable JIT will break in unexpected ways
- it is unlikely to get something that will work the same and the problems are likely not fixable (and the bugs were part of the motivations for migration)
- do we only care about the case where we can call JITed function from CPython; is that sufficient?
- this functionality is useful elsewhere
- what are the motivating use cases?
- having the ability to call a complex math function from CPython and also via the JIT(?)
- a concrete example where multiple dispatch at the CPython level
- some boilerplate likely required
- plan is to figure out examples for boilerplate to make this work for users without bringing back or reimplementing
generated_jit
and see if that satisfied everyone's needs. (Stuart)
-
- xDSL (https://xdsl.dev/)
- lots of potential uses for Numba and lots of potential contributions to xDSL from Numba
- MLIR has a high barrier to entry due to C++ and tablegen; xDSL managed to communicate with C++ and has a much lower barrier
- Umbrella projects (No discussion)
- Numba-Scipy maintainers
-
numba#8891 - Error when calling
nb.generated_jit
from CUDA - numba#8894 - I cannot install numba with pip
- numba#8897 - Replicate old generated_jit behavior on basis of overload for only-jitted variants of functions
- numba#8898 - numba 0.57.0rc1 segfaults: missing ldexpf for np.exp2 on Windows
- numba#8899 - Unable to install shap on python 3.11 doubt to numba cannot install on Python version 3.11.2
- numba#8900 - 0.57.0rc1 UnsupportedError when number of keyword arguments in CALL_FUNCTION_EX is 16 or more
- numba#8901 - IntEnum get item by value
-
numba#8903 - 0.57.0rc1 NumbaDeprecationWarning:
@guvectorize
even withnopython
keyword argument - numba#8904 - IndexError and TypeError when overloading a function with keyword-only arguments
- numba#8893 - Support NVRTC using the ctypes binding
- numba#8895 - CUDA: Enable caching functions that use CG
- numba#8902 - Enable CALL_FUNCTION_EX test for py3.11
-
numba#8905 - FIX: Add missing
**targetoptions
tojit
inGUFuncBuilder
- numba#8906 - Add support for reflected dunder methods in jitclass
- numba#8907 - Add tests for reproducing issue #8898.
- llvmlite#934 - Expose TargetLibraryInfo pass
- llvmlite#935 - Disable zlib for LLVM on Windows
- merged - numba#8892 - Add support for *matmul methods in jitclass
- merged - numba#8896 - Remove codecov install (now deleted from PyPI