258 lines
6.8 KiB
Python
258 lines
6.8 KiB
Python
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from llvmlite import ir, binding as ll
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from numba.core import types, datamodel
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from numba.core.datamodel.testing import test_factory
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from numba.core.datamodel.manager import DataModelManager
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from numba.core.datamodel.models import OpaqueModel
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import unittest
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class TestBool(test_factory()):
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fe_type = types.boolean
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class TestPyObject(test_factory()):
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fe_type = types.pyobject
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class TestInt8(test_factory()):
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fe_type = types.int8
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class TestInt16(test_factory()):
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fe_type = types.int16
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class TestInt32(test_factory()):
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fe_type = types.int32
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class TestInt64(test_factory()):
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fe_type = types.int64
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class TestUInt8(test_factory()):
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fe_type = types.uint8
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class TestUInt16(test_factory()):
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fe_type = types.uint16
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class TestUInt32(test_factory()):
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fe_type = types.uint32
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class TestUInt64(test_factory()):
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fe_type = types.uint64
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class TestFloat(test_factory()):
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fe_type = types.float32
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class TestDouble(test_factory()):
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fe_type = types.float64
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class TestComplex(test_factory()):
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fe_type = types.complex64
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class TestDoubleComplex(test_factory()):
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fe_type = types.complex128
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class TestPointerOfInt32(test_factory()):
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fe_type = types.CPointer(types.int32)
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class TestUniTupleOf2xInt32(test_factory()):
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fe_type = types.UniTuple(types.int32, 2)
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class TestUniTupleEmpty(test_factory()):
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fe_type = types.UniTuple(types.int32, 0)
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class TestTupleInt32Float32(test_factory()):
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fe_type = types.Tuple([types.int32, types.float32])
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class TestTupleEmpty(test_factory()):
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fe_type = types.Tuple([])
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class Test1DArrayOfInt32(test_factory()):
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fe_type = types.Array(types.int32, 1, 'C')
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class Test2DArrayOfComplex128(test_factory()):
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fe_type = types.Array(types.complex128, 2, 'C')
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class Test0DArrayOfInt32(test_factory()):
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fe_type = types.Array(types.int32, 0, 'C')
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class TestArgInfo(unittest.TestCase):
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def _test_as_arguments(self, fe_args):
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"""
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Test round-tripping types *fe_args* through the default data model's
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argument conversion and unpacking logic.
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"""
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dmm = datamodel.default_manager
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fi = datamodel.ArgPacker(dmm, fe_args)
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module = ir.Module()
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fnty = ir.FunctionType(ir.VoidType(), [])
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function = ir.Function(module, fnty, name="test_arguments")
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builder = ir.IRBuilder()
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builder.position_at_end(function.append_basic_block())
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args = [ir.Constant(dmm.lookup(t).get_value_type(), None)
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for t in fe_args]
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# Roundtrip
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values = fi.as_arguments(builder, args)
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asargs = fi.from_arguments(builder, values)
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self.assertEqual(len(asargs), len(fe_args))
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valtys = tuple([v.type for v in values])
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self.assertEqual(valtys, fi.argument_types)
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expect_types = [a.type for a in args]
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got_types = [a.type for a in asargs]
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self.assertEqual(expect_types, got_types)
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# Assign names (check this doesn't raise)
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fi.assign_names(values, ["arg%i" for i in range(len(fe_args))])
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builder.ret_void()
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ll.parse_assembly(str(module))
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def test_int32_array_complex(self):
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fe_args = [types.int32,
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types.Array(types.int32, 1, 'C'),
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types.complex64]
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self._test_as_arguments(fe_args)
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def test_two_arrays(self):
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fe_args = [types.Array(types.int32, 1, 'C')] * 2
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self._test_as_arguments(fe_args)
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def test_two_0d_arrays(self):
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fe_args = [types.Array(types.int32, 0, 'C')] * 2
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self._test_as_arguments(fe_args)
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def test_tuples(self):
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fe_args = [types.UniTuple(types.int32, 2),
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types.UniTuple(types.int32, 3)]
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self._test_as_arguments(fe_args)
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# Tuple of struct-likes
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arrty = types.Array(types.int32, 1, 'C')
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fe_args = [types.UniTuple(arrty, 2),
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types.UniTuple(arrty, 3)]
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self._test_as_arguments(fe_args)
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# Nested tuple
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fe_args = [types.UniTuple(types.UniTuple(types.int32, 2), 3)]
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self._test_as_arguments(fe_args)
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def test_empty_tuples(self):
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# Empty tuple
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fe_args = [types.UniTuple(types.int16, 0),
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types.Tuple(()),
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types.int32]
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self._test_as_arguments(fe_args)
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def test_nested_empty_tuples(self):
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fe_args = [types.int32,
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types.UniTuple(types.Tuple(()), 2),
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types.int64]
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self._test_as_arguments(fe_args)
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class TestMemInfo(unittest.TestCase):
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def setUp(self):
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self.dmm = datamodel.default_manager
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def test_number(self):
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ty = types.int32
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dm = self.dmm[ty]
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self.assertFalse(dm.contains_nrt_meminfo())
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def test_array(self):
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ty = types.int32[:]
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dm = self.dmm[ty]
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self.assertTrue(dm.contains_nrt_meminfo())
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def test_tuple_of_number(self):
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ty = types.UniTuple(dtype=types.int32, count=2)
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dm = self.dmm[ty]
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self.assertFalse(dm.contains_nrt_meminfo())
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def test_tuple_of_array(self):
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ty = types.UniTuple(dtype=types.int32[:], count=2)
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dm = self.dmm[ty]
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self.assertTrue(dm.contains_nrt_meminfo())
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class TestMisc(unittest.TestCase):
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def test_issue2921(self):
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import numpy as np
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from numba import njit
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@njit
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def copy(a, b):
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for i in range(a.shape[0]):
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a[i] = b[i]
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b = np.arange(5, dtype=np.uint8).view(np.bool_)
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a = np.zeros_like(b)
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copy(a, b)
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np.testing.assert_equal(a, np.array((False,) + (True,) * 4))
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class TestDMMChaining(unittest.TestCase):
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def test_basic(self):
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dmm = DataModelManager()
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class int_handler(OpaqueModel):
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pass
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class float_handler(OpaqueModel):
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pass
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dmm.register(types.Integer, int_handler)
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dmm.register(types.Float, float_handler)
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inter_dmm = DataModelManager()
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class new_int_handler(OpaqueModel):
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pass
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inter_dmm.register(types.Integer, new_int_handler)
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chained_dmm = inter_dmm.chain(dmm)
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# Check that the chained DMM has the new handler
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self.assertIsInstance(chained_dmm.lookup(types.intp), new_int_handler)
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# and not the old handler
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self.assertNotIsInstance(chained_dmm.lookup(types.intp), int_handler)
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# Check that the base DMM has the old handler
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self.assertIsInstance(dmm.lookup(types.intp), int_handler)
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# Check that float goes to the float_handler
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self.assertIsInstance(chained_dmm.lookup(types.float32), float_handler)
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self.assertIsInstance(dmm.lookup(types.float32), float_handler)
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# Check the intermediate DMM
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self.assertIsInstance(inter_dmm.lookup(types.intp), new_int_handler)
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with self.assertRaises(KeyError):
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inter_dmm.lookup(types.float32)
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if __name__ == '__main__':
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unittest.main()
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