import functools import numpy as np import unittest from numba import config, cuda, types from numba.tests.support import TestCase from numba.tests.test_ufuncs import BasicUFuncTest def _make_ufunc_usecase(ufunc): ldict = {} arg_str = ",".join(["a{0}".format(i) for i in range(ufunc.nargs)]) func_str = f"def fn({arg_str}):\n np.{ufunc.__name__}({arg_str})" exec(func_str, globals(), ldict) fn = ldict["fn"] fn.__name__ = "{0}_usecase".format(ufunc.__name__) return fn # This test would also be a CUDATestCase, but to avoid a confusing and # potentially dangerous inheritance diamond with setUp methods that modify # global state, we implement the necessary parts of CUDATestCase within this # class instead. These are: # # - Disable parallel testing with _numba_parallel_test_. # - Disabling CUDA performance warnings for the duration of tests. class TestUFuncs(BasicUFuncTest, TestCase): _numba_parallel_test_ = False def setUp(self): BasicUFuncTest.setUp(self) # 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"), ), ] ) # Test with multiple dimensions self.inputs.extend( [ # Basic 2D and 3D arrays ( np.linspace(0, 1).reshape((5, -1)), types.Array(types.float64, 2, "C"), ), ( np.linspace(0, 1).reshape((2, 5, -1)), types.Array(types.float64, 3, "C"), ), # Complex data (i.e. interleaved) ( np.linspace(0, 1 + 1j).reshape(5, -1), types.Array(types.complex128, 2, "C"), ), # F-ordered ( np.asfortranarray(np.linspace(0, 1).reshape((5, -1))), types.Array(types.float64, 2, "F"), ), ] ) # Add tests for other integer types self.inputs.extend( [ (np.uint8(0), types.uint8), (np.uint8(1), types.uint8), (np.int8(-1), types.int8), (np.int8(0), types.int8), (np.uint16(0), types.uint16), (np.uint16(1), types.uint16), (np.int16(-1), types.int16), (np.int16(0), types.int16), (np.ulonglong(0), types.ulonglong), (np.ulonglong(1), types.ulonglong), (np.longlong(-1), types.longlong), (np.longlong(0), types.longlong), ( np.array([0, 1], dtype=np.ulonglong), types.Array(types.ulonglong, 1, "C"), ), ( np.array([0, 1], dtype=np.longlong), types.Array(types.longlong, 1, "C"), ), ] ) self._low_occupancy_warnings = config.CUDA_LOW_OCCUPANCY_WARNINGS self._warn_on_implicit_copy = config.CUDA_WARN_ON_IMPLICIT_COPY # Disable warnings about low gpu utilization in the test suite config.CUDA_LOW_OCCUPANCY_WARNINGS = 0 # Disable warnings about host arrays in the test suite config.CUDA_WARN_ON_IMPLICIT_COPY = 0 def tearDown(self): # Restore original warning settings config.CUDA_LOW_OCCUPANCY_WARNINGS = self._low_occupancy_warnings config.CUDA_WARN_ON_IMPLICIT_COPY = self._warn_on_implicit_copy def _make_ufunc_usecase(self, ufunc): return _make_ufunc_usecase(ufunc) @functools.lru_cache(maxsize=None) def _compile(self, pyfunc, args): # We return an already-configured kernel so that basic_ufunc_test can # call it just like it does for a CPU function return cuda.jit(args)(pyfunc)[1, 1] def basic_int_ufunc_test(self, name=None): skip_inputs = [ types.float32, types.float64, types.Array(types.float32, 1, "C"), types.Array(types.float32, 2, "C"), types.Array(types.float64, 1, "C"), types.Array(types.float64, 2, "C"), types.Array(types.float64, 3, "C"), types.Array(types.float64, 2, "F"), types.complex64, types.complex128, types.Array(types.complex64, 1, "C"), types.Array(types.complex64, 2, "C"), types.Array(types.complex128, 1, "C"), types.Array(types.complex128, 2, "C"), ] self.basic_ufunc_test(name, skip_inputs=skip_inputs) ############################################################################ # Trigonometric Functions def test_sin_ufunc(self): self.basic_ufunc_test(np.sin, kinds="cf") def test_cos_ufunc(self): self.basic_ufunc_test(np.cos, kinds="cf") def test_tan_ufunc(self): self.basic_ufunc_test(np.tan, kinds="cf") def test_arcsin_ufunc(self): self.basic_ufunc_test(np.arcsin, kinds="cf") def test_arccos_ufunc(self): self.basic_ufunc_test(np.arccos, kinds="cf") def test_arctan_ufunc(self): self.basic_ufunc_test(np.arctan, kinds="cf") def test_arctan2_ufunc(self): self.basic_ufunc_test(np.arctan2, kinds="f") def test_hypot_ufunc(self): self.basic_ufunc_test(np.hypot, kinds="f") def test_sinh_ufunc(self): self.basic_ufunc_test(np.sinh, kinds="cf") def test_cosh_ufunc(self): self.basic_ufunc_test(np.cosh, kinds="cf") def test_tanh_ufunc(self): self.basic_ufunc_test(np.tanh, kinds="cf") def test_arcsinh_ufunc(self): self.basic_ufunc_test(np.arcsinh, kinds="cf") def test_arccosh_ufunc(self): self.basic_ufunc_test(np.arccosh, kinds="cf") def test_arctanh_ufunc(self): # arctanh is only valid is only finite in the range ]-1, 1[ # This means that for any of the integer types it will produce # conversion from infinity/-infinity to integer. That's undefined # behavior in C, so the results may vary from implementation to # implementation. This means that the result from the compiler # used to compile NumPy may differ from the result generated by # llvm. Skipping the integer types in this test avoids failed # tests because of this. to_skip = [ types.Array(types.uint32, 1, "C"), types.uint32, types.Array(types.int32, 1, "C"), types.int32, types.Array(types.uint64, 1, "C"), types.uint64, types.Array(types.int64, 1, "C"), types.int64, ] self.basic_ufunc_test(np.arctanh, skip_inputs=to_skip, kinds="cf") def test_deg2rad_ufunc(self): self.basic_ufunc_test(np.deg2rad, kinds="f") def test_rad2deg_ufunc(self): self.basic_ufunc_test(np.rad2deg, kinds="f") def test_degrees_ufunc(self): self.basic_ufunc_test(np.degrees, kinds="f") def test_radians_ufunc(self): self.basic_ufunc_test(np.radians, kinds="f") ############################################################################ # Comparison functions def test_greater_ufunc(self): self.signed_unsigned_cmp_test(np.greater) def test_greater_equal_ufunc(self): self.signed_unsigned_cmp_test(np.greater_equal) def test_less_ufunc(self): self.signed_unsigned_cmp_test(np.less) def test_less_equal_ufunc(self): self.signed_unsigned_cmp_test(np.less_equal) def test_not_equal_ufunc(self): self.signed_unsigned_cmp_test(np.not_equal) def test_equal_ufunc(self): self.signed_unsigned_cmp_test(np.equal) def test_logical_and_ufunc(self): self.basic_ufunc_test(np.logical_and) def test_logical_or_ufunc(self): self.basic_ufunc_test(np.logical_or) def test_logical_xor_ufunc(self): self.basic_ufunc_test(np.logical_xor) def test_logical_not_ufunc(self): self.basic_ufunc_test(np.logical_not) def test_maximum_ufunc(self): self.basic_ufunc_test(np.maximum) def test_minimum_ufunc(self): self.basic_ufunc_test(np.minimum) def test_fmax_ufunc(self): self.basic_ufunc_test(np.fmax) def test_fmin_ufunc(self): self.basic_ufunc_test(np.fmin) def test_bitwise_and_ufunc(self): self.basic_int_ufunc_test(np.bitwise_and) def test_bitwise_or_ufunc(self): self.basic_int_ufunc_test(np.bitwise_or) def test_bitwise_xor_ufunc(self): self.basic_int_ufunc_test(np.bitwise_xor) def test_invert_ufunc(self): self.basic_int_ufunc_test(np.invert) def test_bitwise_not_ufunc(self): self.basic_int_ufunc_test(np.bitwise_not) # Note: there is no entry for np.left_shift and np.right_shift # because their implementations in NumPy have undefined behavior # 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") if __name__ == "__main__": unittest.main()