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Helpers: to_numpy/cupy
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ax3l committed Aug 7, 2023
1 parent 3689583 commit 8dc8b65
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81 changes: 81 additions & 0 deletions src/amrex/Array4.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,81 @@
"""
This file is part of pyAMReX
Copyright 2022 AMReX community
Authors: Axel Huebl
License: BSD-3-Clause-LBNL
"""

def array4_to_numpy(self, copy=False, order="F"):
"""
Provide a Numpy view into an Array4.
Note on the order of indices:
By default, this is as in AMReX in Fortran contiguous order, indexing as
x,y,z. This has performance implications for use in external libraries such
as cupy.
The order="C" option will index as z,y,x and perform better with cupy.
https://github.com/AMReX-Codes/pyamrex/issues/55#issuecomment-1579610074
Parameters
----------
self : amrex.Array4_*
An Array4 class in pyAMReX
copy : bool, optional
Copy the data if true, otherwise create a view (default).
order : string, optional
F order (default) or C. C is faster with external libraries.
Returns
-------
np.array
A numpy n-dimensional array.
"""
import numpy as np

if order == "F":
return np.array(self, copy=copy).T
elif order == "C":
return np.array(self, copy=copy)
else:
raise ValueError("The order argument must be F or C.")


def array4_to_cupy(self, copy=False, order="F"):
"""
Provide a Cupy view into an Array4.
Note on the order of indices:
By default, this is as in AMReX in Fortran contiguous order, indexing as
x,y,z. This has performance implications for use in external libraries such
as cupy.
The order="C" option will index as z,y,x and perform better with cupy.
https://github.com/AMReX-Codes/pyamrex/issues/55#issuecomment-1579610074
Parameters
----------
self : amrex.Array4_*
An Array4 class in pyAMReX
copy : bool, optional
Copy the data if true, otherwise create a view (default).
order : string, optional
F order (default) or C. C is faster with external libraries.
Returns
-------
cupy.array
A numpy n-dimensional array.
Raises
------
ImportError
Raises an exception if cupy is not installed
"""
import cupy as cp

if order == "F":
return cp.array(self, copy=copy).T
elif order == "C":
return cp.array(self, copy=copy)
else:
raise ValueError("The order argument must be F or C.")
45 changes: 45 additions & 0 deletions src/amrex/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -5,3 +5,48 @@
"Axel Huebl, Ryan Sandberg, Shreyas Ananthan, Remi Lehe, " "Weiqun Zhang, et al."
)
__license__ = "BSD-3-Clause-LBNL"

import numpy as np

from . import amrex_pybind
from .Array4 import array4_to_cupy, array4_to_numpy
from .amrex_pybind import * # noqa

__version__ = amrex_pybind.__version__
__doc__ = amrex_pybind.__doc__
__license__ = amrex_pybind.__license__
__author__ = amrex_pybind.__author__

# at this place we can enhance Python classes with additional methods written
# in pure Python or add some other Python logic
#

# Array4 helper methods
#
Array4_float.to_numpy = array4_to_numpy
Array4_double.to_numpy = array4_to_numpy
Array4_longdouble.to_numpy = array4_to_numpy

Array4_short.to_numpy = array4_to_numpy
Array4_int.to_numpy = array4_to_numpy
Array4_long.to_numpy = array4_to_numpy
Array4_longlong.to_numpy = array4_to_numpy

Array4_ushort.to_numpy = array4_to_numpy
Array4_uint.to_numpy = array4_to_numpy
Array4_ulong.to_numpy = array4_to_numpy
Array4_ulonglong.to_numpy = array4_to_numpy

Array4_float.to_cupy = array4_to_cupy
Array4_double.to_cupy = array4_to_cupy
Array4_longdouble.to_cupy = array4_to_cupy

Array4_short.to_cupy = array4_to_cupy
Array4_int.to_cupy = array4_to_cupy
Array4_long.to_cupy = array4_to_cupy
Array4_longlong.to_cupy = array4_to_cupy

Array4_ushort.to_cupy = array4_to_cupy
Array4_uint.to_cupy = array4_to_cupy
Array4_ulong.to_cupy = array4_to_cupy
Array4_ulonglong.to_cupy = array4_to_cupy
19 changes: 8 additions & 11 deletions tests/test_multifab.py
Original file line number Diff line number Diff line change
Expand Up @@ -46,25 +46,25 @@ def test_mfab_loop(make_mfab):
# numpy representation: non-copying view, including the
# guard/ghost region
# note: in numpy, indices are in C-order!
marr_np = np.array(marr, copy=False)
marr_np = marr.to_numpy()

# check the values at start/end are the same: first component
assert marr_np[0, 0, 0, 0] == marr[bx.small_end]
assert marr_np[0, -1, -1, -1] == marr[bx.big_end]
assert marr_np[-1, -1, -1, 0] == marr[bx.big_end]
# same check, but for all components
for n in range(mfab.num_comp):
small_end_comp = list(bx.small_end) + [n]
big_end_comp = list(bx.big_end) + [n]
assert marr_np[n, 0, 0, 0] == marr[small_end_comp]
assert marr_np[n, -1, -1, -1] == marr[big_end_comp]
assert marr_np[0, 0, 0, n] == marr[small_end_comp]
assert marr_np[-1, -1, -1, n] == marr[big_end_comp]

# now we do some faster assignments, using range based access
# this should fail as out-of-bounds, but does not
# does Numpy not check array access for non-owned views?
# marr_np[24:200, :, :, :] = 42.

# all components and all indices set at once to 42
marr_np[:, :, :, :] = 42.0
marr_np[()] = 42.0

# values in start & end still match?
assert marr_np[0, 0, 0, 0] == marr[bx.small_end]
Expand Down Expand Up @@ -210,8 +210,7 @@ def test_mfab_ops_cuda_cupy(make_mfab_device):
with cupy.profiler.time_range("assign 3 [()]", color_id=0):
for mfi in mfab_device:
bx = mfi.tilebox().grow(ngv)
marr = mfab_device.array(mfi)
marr_cupy = cp.array(marr, copy=False)
marr_cupy = mfab_device.array(mfi).to_cupy(order="C")
# print(marr_cupy.shape) # 1, 32, 32, 32
# print(marr_cupy.dtype) # float64

Expand Down Expand Up @@ -244,8 +243,7 @@ def set_to_five(mm):

for mfi in mfab_device:
bx = mfi.tilebox().grow(ngv)
marr = mfab_device.array(mfi)
marr_cupy = cp.array(marr, copy=False)
marr_cupy = mfab_device.array(mfi).to_cupy(order="C")

# write and read into the marr_cupy
fives_cp = set_to_five(marr_cupy)
Expand All @@ -266,8 +264,7 @@ def set_to_seven(x):

for mfi in mfab_device:
bx = mfi.tilebox().grow(ngv)
marr = mfab_device.array(mfi)
marr_cupy = cp.array(marr, copy=False)
marr_cupy = mfab_device.array(mfi).to_cupy(order="C")

# write and read into the marr_cupy
set_to_seven(marr_cupy)
Expand Down

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