from math import sqrt
from numba import cuda, float32, void
from numba.cuda import compile_ptx, compile_ptx_for_current_device

from numba.cuda.testing import skip_on_cudasim, unittest


@skip_on_cudasim('Compilation unsupported in the simulator')
class TestCompileToPTX(unittest.TestCase):
    def test_global_kernel(self):
        def f(r, x, y):
            i = cuda.grid(1)
            if i < len(r):
                r[i] = x[i] + y[i]

        args = (float32[:], float32[:], float32[:])
        ptx, resty = compile_ptx(f, args)

        # Kernels should not have a func_retval parameter
        self.assertNotIn('func_retval', ptx)
        # .visible .func is used to denote a device function
        self.assertNotIn('.visible .func', ptx)
        # .visible .entry would denote the presence of a global function
        self.assertIn('.visible .entry', ptx)
        # Return type for kernels should always be void
        self.assertEqual(resty, void)

    def test_device_function(self):
        def add(x, y):
            return x + y

        args = (float32, float32)
        ptx, resty = compile_ptx(add, args, device=True)

        # Device functions take a func_retval parameter for storing the
        # returned value in by reference
        self.assertIn('func_retval', ptx)
        # .visible .func is used to denote a device function
        self.assertIn('.visible .func', ptx)
        # .visible .entry would denote the presence of a global function
        self.assertNotIn('.visible .entry', ptx)
        # Inferred return type as expected?
        self.assertEqual(resty, float32)

    def test_fastmath(self):
        def f(x, y, z, d):
            return sqrt((x * y + z) / d)

        args = (float32, float32, float32, float32)
        ptx, resty = compile_ptx(f, args, device=True)

        # Without fastmath, fma contraction is enabled by default, but ftz and
        # approximate div / sqrt is not.
        self.assertIn('fma.rn.f32', ptx)
        self.assertIn('div.rn.f32', ptx)
        self.assertIn('sqrt.rn.f32', ptx)

        ptx, resty = compile_ptx(f, args, device=True, fastmath=True)

        # With fastmath, ftz and approximate div / sqrt are enabled
        self.assertIn('fma.rn.ftz.f32', ptx)
        # "full" refers to a full-range approximate divide
        self.assertIn('div.full.ftz.f32', ptx)
        self.assertIn('sqrt.approx.ftz.f32', ptx)

    def test_compile_ptx_for_current_device(self):
        def add(x, y):
            return x + y

        args = (float32, float32)
        ptx, resty = compile_ptx_for_current_device(add, args, device=True)

        # Check we target the current device's compute capabilitay
        cc = cuda.get_current_device().compute_capability
        target = f'.target sm_{cc[0]}{cc[1]}'
        self.assertIn(target, ptx)


if __name__ == '__main__':
    unittest.main()

Add tests of compile_ptx and compile_ptx_for_current_device