pytorch/test/jit/test_dataclasses.py

# Owner(s): ["oncall: jit"]

from dataclasses import dataclass, field, InitVar
from hypothesis import given, settings, strategies as st
from torch.testing._internal.jit_utils import JitTestCase
from typing import List, Optional
import sys
import torch
import unittest

if __name__ == '__main__':
    raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
                       "\tpython test/test_jit.py TESTNAME\n\n"
                       "instead.")

# Example jittable dataclass
@dataclass(order=True)
class Point:
    x: float
    y: float
    norm: Optional[torch.Tensor] = None

    def __post_init__(self):
        self.norm = (torch.tensor(self.x) ** 2 + torch.tensor(self.y) ** 2) ** 0.5


# Make sure the Meta internal tooling doesn't raise an overflow error
NonHugeFloats = st.floats(min_value=-1e4, max_value=1e4, allow_nan=False)

class TestDataclasses(JitTestCase):
    # We only support InitVar in JIT dataclasses for Python 3.8+ because it would be very hard
    # to support without the `type` attribute on InitVar (see comment in _dataclass_impls.py).
    @unittest.skipIf(sys.version_info < (3, 8), "InitVar not supported in Python < 3.8")
    def test_init_vars(self):
        @dataclass(order=True)
        class Point2:
            x: float
            y: float
            norm_p: InitVar[int] = 2
            norm: Optional[torch.Tensor] = None

            def __post_init__(self, norm_p: int):
                self.norm = (torch.tensor(self.x) ** norm_p + torch.tensor(self.y) ** norm_p) ** (1 / norm_p)

        def fn(x: float, y: float, p: int):
            pt = Point2(x, y, p)
            return pt.norm

        self.checkScript(fn, (1.0, 2.0, 3))

    # Sort of tests both __post_init__ and optional fields
    @settings(deadline=None)
    @given(NonHugeFloats, NonHugeFloats)
    def test__post_init__(self, x, y):
        def fn(x: float, y: float):
            pt = Point(x, y)
            return pt.norm

        self.checkScript(fn, [x, y])

    @settings(deadline=None)
    @given(st.tuples(NonHugeFloats, NonHugeFloats), st.tuples(NonHugeFloats, NonHugeFloats))
    def test_comparators(self, pt1, pt2):
        x1, y1 = pt1
        x2, y2 = pt2

        def compare(x1: float, y1: float, x2: float, y2: float):
            pt1 = Point(x1, y1)
            pt2 = Point(x2, y2)
            return (
                pt1 == pt2,
                # pt1 != pt2,   # TODO: Modify interpreter to auto-resolve (a != b) to not (a == b) when there's no __ne__
                pt1 < pt2,
                pt1 <= pt2,
                pt1 > pt2,
                pt1 >= pt2,
            )

        self.checkScript(compare, [x1, y1, x2, y2])

    def test_default_factories(self):
        @dataclass
        class Foo(object):
            x: List[int] = field(default_factory=list)

        with self.assertRaises(NotImplementedError):
            def fn():
                foo = Foo()
                return foo.x

            torch.jit.script(fn)()

    # The user should be able to write their own __eq__ implementation
    # without us overriding it.
    def test_custom__eq__(self):
        @dataclass
        class CustomEq:
            a: int
            b: int

            def __eq__(self, other: 'CustomEq') -> bool:
                return self.a == other.a  # ignore the b field

        def fn(a: int, b1: int, b2: int):
            pt1 = CustomEq(a, b1)
            pt2 = CustomEq(a, b2)
            return pt1 == pt2

        self.checkScript(fn, [1, 2, 3])