Implement all NumPy intrinsics via ASR #2474

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1. faze-geek commented on Feb 4, 2024

   

   faze-geek

   on Feb 4, 2024

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   I was trying to breakdown this project and would appreciate if someone can guide me in the right direction.
   I see some functions like empty(), zeroes() are supported. I also saw issues through the numpy label to implement numpy.clip() and numpy.size().
   Is this project about implementing various routines found at https://numpy.org/doc/stable/reference/routines.html . If yes, we can discuss about which Array creation/manipulation routines we want to prioritize. Also some popular NumPy mathematical modules or decorators for array operations (like @vectorize) can be added.
2. certik commented on Feb 5, 2024

   

   certik

   on Feb 5, 2024

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   Is this project about implementing various routines found at https://numpy.org/doc/stable/reference/routines.html

   Yes.

   We can prioritize those that we already support in ASR.

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3. faze-geek commented on Feb 11, 2024

   

   faze-geek

   on Feb 11, 2024

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   We can prioritize those that we already support in ASR.

   I would like to have some clarity on this.
   Following the numpy label I come across the 2 files supporting numpy functions.

   1. https://github.com/lcompilers/lpython/blob/main/src/runtime/lpython_intrinsic_numpy.py - Containing all mathematical and trigonometric routines (all of them follow the intrinsic architecture in src/libasr/pass/intrinsic_function_registry.h).

   2. https://github.com/lcompilers/lpython/blob/main/src/lpython/semantics/python_ast_to_asr.cpp - Containing numpy array creation routines like empty(), size() etc. (not ported to the intrinsic function files yet).

   Which functions are to be focussed upon and implemented via ASR?
4. faze-geek commented on Feb 12, 2024

   

   faze-geek

   on Feb 12, 2024

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   @rebcabin I observed a lot of your work in the issue tracker uses Numpy. Would be great if you chime in here and mention any numpy intrinsics you would like to be implemented. Thanks !
5. faze-geek commented on Feb 16, 2024

   

   faze-geek

   on Feb 16, 2024

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   I would like to summarize the target of the project for my own understanding, kindly guide me if I am deviating from the goal here.
   Let's say we want to use a trigo functions from numpy like sin(x). For a snippet like -

   from numpy import array, empty, float64,
   
   def f(xi32: f64[:]) -> f64:
       xi32[0] = 0.1
       xi32[1] = 0.2
       xi32[2] = 0.3
       print(sin(xi32))
   
   def d():
       ai32: f64[3] = empty(3, dtype=float64)
       f(ai32)
   
   d()
   

   Right now, this is handled here and due to the@vectorizedecorator we are able to handle array inputs.
   

   lpython/src/runtime/lpython_intrinsic_numpy.py

   Lines 9 to 12 in 6098157

   	@overload
   	@vectorize
   	def sin(x: f64) -> f64:
   	return _lfortran_dsin(x)

   So we already are able to generate the ASR, from lpython_instrinsic_numpy.py file but we want this to be handled by the instantiate_Sin/ create_Sin functions from intrinsic_function_registry.h.
6. certik commented on Feb 16, 2024

   

   certik

   on Feb 16, 2024

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   This should be updated to instead create the Sin intrinsic as IntrinsicElementalFunction in ASR, and it should be done in https://github.com/lcompilers/lpython/blob/main/src/lpython/semantics/python_ast_to_asr.cpp.

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