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Merged
sklam merged 5 commits into from
Apr 9, 2024
Merged

Add np.log* bindings for CUDA #9417

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605ac85
Add np.log* bindings for CUDA
guilhermeleobas Feb 2, 2024
8af4864
add release notes
guilhermeleobas Feb 9, 2024
bd9737b
address reviewer comments
guilhermeleobas Mar 13, 2024
898d018
Merge remote-tracking branch 'numba/main' into guilhermeleobas/cuda_n…
gmarkall Apr 8, 2024
fdc423f
address reviewer comments
guilhermeleobas Apr 8, 2024
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4 changes: 4 additions & 0 deletions docs/upcoming_changes/9417.new_feature.rst
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Original file line number Diff line number Diff line change
@@ -0,0 +1,4 @@
Support math.log2
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guilhermeleobas marked this conversation as resolved.
Resolved
=================

Support for ``np.log2`` is added to CUDA target.

@stuartarchibald stuartarchibald Mar 12, 2024

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This adds more than just np.log2 support?

@guilhermeleobas guilhermeleobas Mar 13, 2024

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Fixed

5 changes: 5 additions & 0 deletions numba/cuda/cudadecl.py
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Expand Up @@ -5,6 +5,7 @@
register_numpy_ufunc,
trigonometric_functions,
comparison_functions,
math_operations,
bit_twiddling_functions)
from numba.core.typing.templates import (AttributeTemplate, ConcreteTemplate,
AbstractTemplate, CallableTemplate,
Expand Down Expand Up @@ -799,3 +800,7 @@ def resolve_local(self, mod):

for func in bit_twiddling_functions:
register_numpy_ufunc(func, register_global)

for func in math_operations:
if func in ('log2', 'log', 'log10'):

@stuartarchibald stuartarchibald Mar 12, 2024

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Would being specific by using something like the following be preferable to iterating over all the math operations and masking off those supported?

for name in ('log2', 'log', 'log10'):
  func = getattr(math_operations, name, None)
  if func is None:
    raise # something appropriate 
  ... # do registration

@guilhermeleobas guilhermeleobas Mar 13, 2024

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Sounds good. But just a remainder that math_operations is actually a list containing the name of supported math functions, not the math module itself.

(Pdb++) math_operations
['add', 'subtract', 'multiply', 'logaddexp', 'logaddexp2', 'true_divide', 'floor_divide', 'negative', 'positive', 'power', 'float_power', 'remainder', 'fmod', 'absolute', 'rint', 'sign', 'conjugate', 'exp', 'exp2', 'log', 'log2', 'log10', 'expm1', 'log1p', 'sqrt', 'square', 'cbrt', 'reciprocal', 'divide', 'mod', 'divmod', 'abs', 'fabs', 'gcd', 'lcm']

register_numpy_ufunc(func, register_global)
12 changes: 12 additions & 0 deletions numba/cuda/tests/cudapy/test_ufuncs.py
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Expand Up @@ -260,6 +260,18 @@ def test_bitwise_not_ufunc(self):
# when the second argument is a negative. See the comment in
# numba/tests/test_ufuncs.py for more details.

############################################################################
# Mathematical Functions

def test_log_ufunc(self):
self.basic_ufunc_test(np.log, kinds='cf')

def test_log2_ufunc(self):
self.basic_ufunc_test(np.log2, kinds='cf')

def test_log10_ufunc(self):
self.basic_ufunc_test(np.log10, kinds='cf')
Comment on lines +266 to +273

@stuartarchibald stuartarchibald Mar 12, 2024

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I don't think these test functions will check values in the complex domain.

@guilhermeleobas guilhermeleobas Mar 13, 2024

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I print the data that was generated in basic_ufunc_test and could see a few complex arrays there.

@gmarkall gmarkall Apr 8, 2024

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The ufunc tests in numba.tests.test_ufuncs don't check values in the complex domain, but the CUDA ones have extra inputs added:

numba/numba/cuda/tests/cudapy/test_ufuncs.py

Lines 33 to 48 in 03f2722

# The basic ufunc test does not set up complex inputs, so we'll add
# some here for testing with CUDA.
self.inputs.extend([
(np.complex64(-0.5 - 0.5j), types.complex64),
(np.complex64(0.0), types.complex64),
(np.complex64(0.5 + 0.5j), types.complex64),
(np.complex128(-0.5 - 0.5j), types.complex128),
(np.complex128(0.0), types.complex128),
(np.complex128(0.5 + 0.5j), types.complex128),
(np.array([-0.5 - 0.5j, 0.0, 0.5 + 0.5j], dtype='c8'),
types.Array(types.complex64, 1, 'C')),
(np.array([-0.5 - 0.5j, 0.0, 0.5 + 0.5j], dtype='c16'),
types.Array(types.complex128, 1, 'C')),
])



if __name__ == '__main__':
unittest.main()
31 changes: 31 additions & 0 deletions numba/cuda/ufuncs.py
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Expand Up @@ -35,6 +35,15 @@ def np_binary_impl(fn, context, builder, sig, args):
impl = get_binary_impl_for_fn_and_ty(fn, sig.args[0])
return impl(context, builder, sig, args)

def np_real_log_impl(context, builder, sig, args):
return np_unary_impl(math.log, context, builder, sig, args)

def np_real_log2_impl(context, builder, sig, args):
return np_unary_impl(math.log2, context, builder, sig, args)

def np_real_log10_impl(context, builder, sig, args):
return np_unary_impl(math.log10, context, builder, sig, args)

def np_real_sin_impl(context, builder, sig, args):
return np_unary_impl(math.sin, context, builder, sig, args)

Expand Down Expand Up @@ -628,4 +637,26 @@ def np_real_atanh_impl(context, builder, sig, args):
'qq->q': numbers.int_shr_impl,
'QQ->Q': numbers.int_shr_impl,
}

db[np.log] = {
'f->f': np_real_log_impl,
'd->d': np_real_log_impl,
'F->F': npyfuncs.np_complex_log_impl,
'D->D': npyfuncs.np_complex_log_impl,
Comment on lines +644 to +645

@stuartarchibald stuartarchibald Mar 12, 2024

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I'm not sure what will happen with complex data types, I think the calls will resolve in terms of the math and cmath implementations, but am not sure how they will interact with CUDA math.

@guilhermeleobas guilhermeleobas Mar 13, 2024 •
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np.log(complex_array) is implemented using a combination of three libdevice functions: hypotf, logf and atan2f:

...
B0:
  %".25" = call float @"__nv_hypotf"(float %"arg.x", float %"arg.y")
  %".26" = call float @"__nv_logf"(float %".25")
  %".27" = call float @"__nv_atan2f"(float %"arg.y", float %"arg.x")
  store {float, float} zeroinitializer, {float, float}* %".28"
  %".31" = getelementptr inbounds {float, float}, {float, float}* %".28", i32 0, i32 0
  store float %".26", float* %".31"
  %".33" = getelementptr inbounds {float, float}, {float, float}* %".28", i32 0, i32 1
  store float %".27", float* %".33"
  %".35" = load {float, float}, {float, float}* %".28"
  %"extracted.real" = extractvalue {float, float} %".35", 0
  %"extracted.imag" = extractvalue {float, float} %".35", 1
  %".36" = insertvalue {float, float} undef, float %"extracted.real", 0
  %".37" = insertvalue {float, float} %".36", float %"extracted.imag", 1
  store {float, float} %".37", {float, float}* %".ret"
  ret i32 0

@gmarkall gmarkall Apr 8, 2024

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These libdevice functions are used because of the lowerings of math.log, math.hypot, and math.atan2 in mathimpl.py.

}

db[np.log2] = {
'f->f': np_real_log2_impl,
'd->d': np_real_log2_impl,
'F->F': npyfuncs.np_complex_log2_impl,
'D->D': npyfuncs.np_complex_log2_impl,
}

db[np.log10] = {
'f->f': np_real_log10_impl,
'd->d': np_real_log10_impl,
'F->F': npyfuncs.np_complex_log10_impl,
'D->D': npyfuncs.np_complex_log10_impl,
}

return db