Numba Meeting: 2024-03-05

Attendees:
FPOC (last week):
FPOC (incoming):

NOTE: All communication is subject to the Numba Code of Conduct.

Please refer to this calendar for the next meeting date.

0. Discussion

  • Numba user survey update.

  • Numba 0.59.1 schedule.

    • Aim for patches in this week, test build over the weekend and release next week.

  • Review NumPy 2.0 community communication note.

  • How to stage NumPy 2.0 work and impact on maintenance.

  • Draft of February's NAN is ready for comments from maintainers.

  • Possible "solutions" to the question last week about how to specialise compilation based on a configuration object, and still preserve literals.

    ​​from numba import jit, types
​​from numba.extending import overload
​​import functools


​​# Some sort of configuration object
​​class Config():
​​​   def __init__(self, a, b):
​​​       self._a = a
​​​       self._b = b

​​​   @property
​​​   def a(self):
​​​       return self._a

​​​   @property
​​​   def b(self):
​​​       return self._b


​​# Perhaps use a cache so that the identical Config instances return the same
​​# jit function? This will prevent recompilation of the entry point for two
​​# identical config instances as the jit function passed as the argument will be
​​# the same.
​​@functools.cache
​​def obj2strkeydict(obj, config_name):

​​​   # unpack object to freevars and close over them
​​​   tmp_a = obj.a
​​​   tmp_b = obj.b
​​​   assert isinstance(config_name, str)
​​​   tmp_force_heterogeneous = config_name

​​​   @jit
​​​   def configurator():
​​​       d = {'a': tmp_a,
​​​            'b': tmp_b,
​​​            'config_name': tmp_force_heterogeneous}
​​​       return d

​​​   # return a configuration function that returns a string-key-dict
​​​   # representation of the configuration object.
​​​   return configurator


​​# Define some "work"
​​def work(pred):
​​​   # ... elided, put python implementation here
​​​   pass


​​@overload(work)
​​def ol_work(pred):
​​​   assert isinstance(pred, types.Literal)
​​​   print('Calling work with type', pred)
​​​   return lambda pred: pred


​​@jit
​​def physics(cfig_func):
​​​   # This is the main entry point to the application, it takes a configuration
​​​   # function as an argument. It will specialise on each configuration
​​​   # function.

​​​   # call the function, config is a string-key-dict
​​​   config = cfig_func()
​​​   # unpack config, these types will be preserved as literals.
​​​   a = config['a']
​​​   b = config['b']
​​​   # call some work to check the types.
​​​   return work(b) + a


​​# demo
​​def demo():

​​​   # Create two different Python based configuration objects with literal
​​​   # entries.
​​​   configuration1 = Config(10, True)
​​​   configuration2 = Config(12.3, False)

​​​   # Create corresponding converted configuration objects...
​​​   jit_config1 = obj2strkeydict(configuration1, 'config1')
​​​   jit_config2 = obj2strkeydict(configuration2, 'config2')
​​​   # create "another" configuration1 instance, memoization prevents
​​​   # duplication.
​​​   jit_config1_again = obj2strkeydict(configuration1, 'config1')

​​​   # Call the `physics` application, it will specialize on config.
​​​   physics(jit_config1)
​​​   physics(jit_config2)
​​​   # should not trigger a 3rd compilation, config is memoized.
​​​   physics(jit_config1_again)
​​​   physics.inspect_types()


​​if __name__ == "__main__":
​​​   demo()

New "Ready for Review" PRs

1. New Issues

  • numba#9471 - CUDA crashes when passed complex record array
  • numba#9472 - [ANN] Numba User Survey 2024
  • numba#9473 - want int64 rather than Optional(int64) when dict.get(key, default_int64) and dict value type is int64
  • numba#9475 - Infer Numba Types from Python Type Hints
  • numba#9476 - [feature request] assign a tuple to one row of recarray
  • llvmlite#1034 - Add numba and llvmlite musl wheels for alpine support

Closed Issues

  • numba#9477 - np.searchsorted no longer work for datetime64[ns] array type

2. New PRs

  • numba#9470 - Add ability to link CUDA functions with in-memory PTX.
  • numba#9474 - (Do Merge) NumPy 2: Removed dead code conflicting with binary builds against NumPy 2

Closed PRs

(last numba: 9477; llvmlite 1034)

3. Short-term Roadmap

2024-gantt: TBD
2023-gantt: https://github.com/numba/numba/issues/8971

Select a repo