125 lines
3.2 KiB
Python
125 lines
3.2 KiB
Python
import math
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import numpy as np
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from numba import cuda, float64, int8, int32, void
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from numba.cuda.testing import unittest, CUDATestCase
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def cu_mat_power(A, power, power_A):
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y, x = cuda.grid(2)
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m, n = power_A.shape
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if x >= n or y >= m:
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return
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power_A[y, x] = math.pow(A[y, x], int32(power))
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def cu_mat_power_binop(A, power, power_A):
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y, x = cuda.grid(2)
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m, n = power_A.shape
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if x >= n or y >= m:
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return
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power_A[y, x] = A[y, x] ** power
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def vec_pow(r, x, y):
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i = cuda.grid(1)
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if i < len(r):
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r[i] = pow(x[i], y[i])
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def vec_pow_binop(r, x, y):
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i = cuda.grid(1)
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if i < len(r):
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r[i] = x[i] ** y[i]
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def vec_pow_inplace_binop(r, x):
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i = cuda.grid(1)
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if i < len(r):
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r[i] **= x[i]
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def random_complex(N):
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np.random.seed(123)
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return (np.random.random(1) + np.random.random(1) * 1j)
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class TestCudaPowi(CUDATestCase):
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def test_powi(self):
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dec = cuda.jit(void(float64[:, :], int8, float64[:, :]))
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kernel = dec(cu_mat_power)
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power = 2
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A = np.arange(10, dtype=np.float64).reshape(2, 5)
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Aout = np.empty_like(A)
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kernel[1, A.shape](A, power, Aout)
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self.assertTrue(np.allclose(Aout, A ** power))
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def test_powi_binop(self):
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dec = cuda.jit(void(float64[:, :], int8, float64[:, :]))
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kernel = dec(cu_mat_power_binop)
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power = 2
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A = np.arange(10, dtype=np.float64).reshape(2, 5)
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Aout = np.empty_like(A)
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kernel[1, A.shape](A, power, Aout)
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self.assertTrue(np.allclose(Aout, A ** power))
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# Relative tolerance kwarg is provided because 1.0e-7 (the default for
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# assert_allclose) is a bit tight for single precision.
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def _test_cpow(self, dtype, func, rtol=1.0e-7):
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N = 32
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x = random_complex(N).astype(dtype)
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y = random_complex(N).astype(dtype)
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r = np.zeros_like(x)
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cfunc = cuda.jit(func)
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cfunc[1, N](r, x, y)
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np.testing.assert_allclose(r, x ** y, rtol=rtol)
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# Checks special cases
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x = np.asarray([0.0j, 1.0j], dtype=dtype)
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y = np.asarray([0.0j, 1.0], dtype=dtype)
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r = np.zeros_like(x)
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cfunc[1, 2](r, x, y)
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np.testing.assert_allclose(r, x ** y, rtol=rtol)
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def test_cpow_complex64_pow(self):
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self._test_cpow(np.complex64, vec_pow, rtol=3.0e-7)
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def test_cpow_complex64_binop(self):
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self._test_cpow(np.complex64, vec_pow_binop, rtol=3.0e-7)
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def test_cpow_complex128_pow(self):
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self._test_cpow(np.complex128, vec_pow)
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def test_cpow_complex128_binop(self):
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self._test_cpow(np.complex128, vec_pow_binop)
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def _test_cpow_inplace_binop(self, dtype, rtol=1.0e-7):
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N = 32
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x = random_complex(N).astype(dtype)
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y = random_complex(N).astype(dtype)
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r = x ** y
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cfunc = cuda.jit(vec_pow_inplace_binop)
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cfunc[1, N](x, y)
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np.testing.assert_allclose(x, r, rtol=rtol)
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def test_cpow_complex64_inplace_binop(self):
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self._test_cpow_inplace_binop(np.complex64, rtol=3.0e-7)
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def test_cpow_complex128_inplace_binop(self):
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self._test_cpow_inplace_binop(np.complex128, rtol=3.0e-7)
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if __name__ == '__main__':
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unittest.main()
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