test/test_jit_py3.py

from collections import namedtuple
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.jit_utils import JitTestCase
from torch.testing import FileCheck
from torch import jit
from typing import NamedTuple, List, Optional, Dict, Tuple, Any
from jit.test_module_interface import TestModuleInterface  # noqa: F401
import unittest
import sys
import torch
import torch.testing._internal.jit_utils
import torch.nn as nn
import types

class TestScriptPy3(JitTestCase):
    def test_joined_str(self):
        def func(x):
            hello, test = "Hello", "test"
            print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
            print(f"format blank") # noqa F541
            hi = 'hi'
            print(f"stuff before {hi}")
            print(f"{hi} stuff after")
            return x + 1

        x = torch.arange(4., requires_grad=True)
        # TODO: Add support for f-strings in string parser frontend
        # self.checkScript(func, [x], optimize=True, capture_output=True)

        with self.capture_stdout() as captured:
            out = func(x)

        scripted = torch.jit.script(func)
        with self.capture_stdout() as captured_script:
            out_script = func(x)

        self.assertEqual(out, out_script)
        self.assertEqual(captured, captured_script)

    @unittest.skipIf(sys.version_info[:2] < (3, 7), "`dataclasses` module not present on < 3.7")
    def test_dataclass_error(self):
        from dataclasses import dataclass

        @dataclass
        class NormalizationInfo(object):
            mean: float = 0.0

            def compute(self, total_rows):
                return self.mean

        def fn():
            return NormalizationInfo(1, 2, 3, 4, 5)

        with self.assertRaisesRegex(OSError, "NormalizationInfo"):
            torch.jit.script(fn)

    def test_optional_dict_construct(self):
        class M(torch.nn.Module):
            def use(self, buffer: Dict[str, Optional[torch.Tensor]]):
                return buffer["prev_key"]

            def forward(self, x):
                prev_key = torch.rand(2, 3)
                next_key = torch.rand(2, 3)
                saved_state: Dict[str, Optional[torch.Tensor]] = {
                    "prev_key": prev_key,
                    "next_key": next_key,
                }

                return self.use(saved_state)

        self.checkModule(M(), (torch.rand(2, 2),))

    def test_kwarg_support(self):
        with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, "variable number of arguments"):
            class M(torch.nn.Module):
                def forward(self, *, n_tokens: int, device_name: str = 2):
                    pass
            torch.jit.script(M())

        class M(torch.nn.Module):
            def forward(self, *, n_tokens: int, device_name: str):
                return n_tokens, device_name

        sm = torch.jit.script(M())

        with self.assertRaisesRegex(RuntimeError, "missing value for argument 'n_tokens'"):
            sm()

        input = (3, 'hello')
        self.assertEqual(sm(*input), input)

    def test_named_tuple(self):
        class FeatureVector(NamedTuple):
            float_features: float
            sequence_features: List[float]
            time_since_first: float

        @torch.jit.script
        def foo(x) -> float:
            fv = FeatureVector(3.0, [3.0], 3.0)  # noqa
            rv = fv.float_features
            for val in fv.sequence_features:
                rv += val
            rv *= fv.time_since_first
            return rv

        self.assertEqual(foo(torch.rand(3, 4)), 18.0)

    def test_named_tuple_constant(self):
        class Tup(NamedTuple):
            a: int
            b: int

        @torch.jit.script
        def foo():
            return Tup(1, 2)

        self.assertEqual(foo(), Tup(1, 2))

    def test_dict_preserves_order(self):
        def dict_ordering():
            a : Dict[int, int] = {}
            for i in range(1000):
                a[i] = i + 1
            return a

        self.checkScript(dict_ordering, ())
        di = torch.jit.script(dict_ordering)()
        res = list(di.items())
        for i in range(1000):
            key, value = res[i]
            self.assertTrue(key == i and value == i + 1)

    def test_list_unification_hint(self):
        with self.assertRaisesRegex(RuntimeError, "Expected a List type hint"):
            @torch.jit.script
            def x():
                b : int = [2, 3]
                return b

    def test_return_named_tuple(self):
        class FeatureVector(NamedTuple):
            float_features: float
            sequence_features: List[float]
            time_since_first: float

        @torch.jit.script
        def foo(x):
            fv = FeatureVector(3.0, [3.0], 3.0)
            return fv

        out = foo(torch.rand(3, 4))
        out = foo(torch.rand(3, 4))
        self.assertEqual(out.float_features, 3.0)
        self.assertEqual(out.sequence_features, [3.0])
        self.assertEqual(out.time_since_first, 3.0)

    def test_named_tuple_as_attr(self):
        class Config(NamedTuple):
            size: int

        class MyMod(nn.Module):
            configs: Dict[int, Config]

            def __init__(self, configs):
                super().__init__()
                self.configs = configs

            def forward(self, x):
                for _id, config in self.configs.items():
                    x += config.size
                return x

        s = torch.jit.script(MyMod({0: Config(size=16)}))

    def test_types_as_values(self):
        def fn(m: torch.Tensor) -> torch.device:
            return m.device

        self.checkScript(fn, [torch.randn(2, 2)])

        GG = namedtuple('GG', ['f', 'g'])

        class Foo(torch.nn.Module):
            def __init__(self):
                super().__init__()

            @torch.jit.ignore
            def foo(self, x, z):
                # type: (Tensor, Tensor) -> Tuple[GG, GG]
                return GG(x, z), GG(x, z)

            def forward(self, x, z):
                return self.foo(x, z)

        foo = torch.jit.script(Foo())
        y = foo(torch.randn(2, 2), torch.randn(2, 2))

        class Foo(torch.nn.Module):
            def __init__(self):
                super().__init__()

            @torch.jit.ignore
            def foo(self, x, z) -> Tuple[GG, GG]:
                return GG(x, z)

            def forward(self, x, z):
                return self.foo(x, z)

        foo = torch.jit.script(Foo())
        y = foo(torch.randn(2, 2), torch.randn(2, 2))


    def test_named_tuple_resolution(self):
        class TheType(NamedTuple):
            t: int

        class MyModule(types.ModuleType):
            def __init__(self):
                super(MyModule, self).__init__('MyModule')

            def __getattr__(self, attr):
                return TheType

        some_module = MyModule()

        def fn() -> some_module.Type:
            return some_module.Type(1)

        self.checkScript(fn, [])

    def test_ignore_with_types(self):
        @torch.jit.ignore
        def fn(x: Dict[str, Optional[torch.Tensor]]):
            return x + 10

        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()

            def forward(self, in_batch: Dict[str, Optional[torch.Tensor]]) -> torch.Tensor:
                self.dropout_modality(in_batch)
                fn(in_batch)
                return torch.tensor(1)

            @torch.jit.ignore
            def dropout_modality(self, in_batch: Dict[str, Optional[torch.Tensor]]) -> Dict[str, Optional[torch.Tensor]]:
                return in_batch

        sm = torch.jit.script(M())
        FileCheck().check("dropout_modality").check("in_batch").run(str(sm.graph))

    def test_python_callable(self):
        class MyPythonClass(object):
            @torch.jit.ignore
            def __call__(self, *args) -> str:
                return str(type(args[0]))

        the_class = MyPythonClass()

        @torch.jit.script
        def fn(x):
            return the_class(x)

        # This doesn't involve the string frontend, so don't use checkScript
        x = torch.ones(2)
        self.assertEqual(fn(x), the_class(x))

    def test_bad_types(self):
        @torch.jit.ignore
        def fn(my_arg):
            return my_arg + 10

        with self.assertRaisesRegex(RuntimeError, "argument 'my_arg'"):
            @torch.jit.script
            def other_fn(x):
                return fn('2')

    def test_named_tuple_slice_unpack(self):
        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int]

        @torch.jit.script
        def foo(a : int, b : float, c : List[int]):
            tup = MyCoolNamedTuple(a, b, c)  # noqa
            my_a, my_b, my_c = tup
            return tup[:1], my_a, my_c

        self.assertEqual(foo(3, 3.5, [6]), ((3,), 3, [6]))

    def test_named_tuple_lower(self):
        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int]

        @torch.jit.script
        def foo(a : int):
            tup = MyCoolNamedTuple(a, 3.14, [9])  # noqa
            return tup

        FileCheck().check('TupleConstruct').run(foo.graph)
        torch._C._jit_pass_lower_all_tuples(foo.graph)
        FileCheck().check_not('TupleConstruct').run(foo.graph)

    def test_named_tuple_type_annotation(self):
        global MyCoolNamedTuple  # see [local resolution in python]

        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int]

        @torch.jit.script
        def foo(x : MyCoolNamedTuple) -> MyCoolNamedTuple:
            return x

        mnt = MyCoolNamedTuple(42, 420.0, [666])
        self.assertEqual(foo(mnt), mnt)

    def test_named_tuple_wrong_types(self):
        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int]

        with self.assertRaisesRegex(RuntimeError, "Expected a value of type 'int' for argument 'a'"
                                                  " but instead found type 'str'"):
            @torch.jit.script
            def foo():
                tup = MyCoolNamedTuple('foo', 'bar', 'baz')  # noqa
                return tup

    def test_named_tuple_kwarg_construct(self):
        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int]

        @torch.jit.script
        def foo():
            tup = MyCoolNamedTuple(c=[1, 2, 3], b=3.5, a=9)  # noqa
            return tup

        tup = foo()
        self.assertEqual(tup.a, 9)
        self.assertEqual(tup.b, 3.5)
        self.assertEqual(tup.c, [1, 2, 3])

    def test_named_tuple_default_error(self):
        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int] = [3, 4, 5]

        with self.assertRaisesRegex(RuntimeError, 'Default values are currently not supported'):
            @torch.jit.script
            def foo():
                tup = MyCoolNamedTuple(c=[1, 2, 3], b=3.5, a=9)  # noqa
                return tup

    @unittest.skipIf(True, "broken while these tests were not in CI")
    def test_named_tuple_serialization(self):
        class MyCoolNamedTuple(NamedTuple):
            a : int
            b : float
            c : List[int]

        class MyMod(torch.jit.ScriptModule):
            @torch.jit.script_method
            def forward(self):
                return MyCoolNamedTuple(3, 3.5, [3, 4, 5])

        mm = MyMod()
        mm.save('foo.zip')
        torch.testing._internal.jit_utils.clear_class_registry()
        loaded = torch.jit.load('foo.zip')

        out = mm()
        out_loaded = loaded()

        for name in ['a', 'b', 'c']:
            self.assertEqual(getattr(out_loaded, name), getattr(out, name))

    def test_type_annotate_py3(self):
        def fn():
            a : List[int] = []
            b : torch.Tensor = torch.ones(2, 2)
            c : Optional[torch.Tensor] = None
            d : Optional[torch.Tensor] = torch.ones(3, 4)
            for _ in range(10):
                a.append(4)
                c = torch.ones(2, 2)
                d = None
            return a, b, c, d

        self.checkScript(fn, ())

        def wrong_type():
            wrong : List[int] = [0.5]
            return wrong

        with self.assertRaisesRegex(RuntimeError, "Lists must contain only a single type"):
            torch.jit.script(wrong_type)

    def test_subexpression_List_Future(self):

        @torch.jit.script
        def fn(x: List[torch.jit.Future[int]]) -> torch.jit.Future[int]:
            return x[0]

        FileCheck().check('Future[int]').check('Future[int]').run(fn.graph)

    def test_subexpression_Future_annotate(self):
        @torch.jit.script
        def fn() -> torch.jit.Future[int]:
            x: List[torch.jit.Future[int]] = []
            return x[0]

        FileCheck().check("Future[int][]").run(fn.graph)

    def test_future_isinstance(self):
        @torch.jit.script
        def fn(x: Any) -> torch.jit.Future[int]:
            assert isinstance(x, jit.Future[int])
            return x

        FileCheck().check("Future[int]").run(fn.graph)

    def test_str_refine_any(self):
        def forward(x: Any) -> str:
            if isinstance(x, str):
                return x
            return "foo"
        forward = torch.jit.script(forward)
        self.assertEqual(forward(1), "foo")
        self.assertEqual(forward("bar"), "bar")

    def test_subexpression_Tuple_int_int_Future(self):

        @torch.jit.script
        def fn(x: Tuple[int, int, torch.jit.Future[int]]) -> Tuple[int, torch.jit.Future[int]]:
            return x[0], x[2]

        FileCheck().check('(int, int, Future[int])').check('(int, Future[int])').run(fn.graph)

    def test_subexpression_Dict_int_Future(self):

        @torch.jit.script
        def fn(x: Dict[int, torch.jit.Future[int]], y: int) -> torch.jit.Future[int]:
            return x[y]

        FileCheck().check('Dict(int, Future(int))').check('Future[int]').run(fn.graph)

    def test_subexpression_Optional(self):

        @torch.jit.script
        def fn(x: Optional[Dict[int, torch.jit.Future[int]]]) -> Optional[torch.jit.Future[int]]:
            if x is not None:
                return x[0]
            else:
                return None

        FileCheck().check('Dict(int, Future(int))?').run(fn.graph)

    def test_unimported_type_resolution(self):
        # verify fallback from the python resolver to the c++ resolver

        @ torch.jit.script
        def fn(x):
            # type: (number) -> number
            return x + 1

        FileCheck().check('Scalar').run(fn.graph)

    def test_parser_bug(self):
        def parser_bug(o: Optional[torch.Tensor]):
            pass

    def test_mismatched_annotation(self):
        with self.assertRaisesRegex(RuntimeError, 'annotated with type'):
            @torch.jit.script
            def foo():
                x : str = 4
                return x

    def test_reannotate(self):
        with self.assertRaisesRegex(RuntimeError, 'declare and annotate'):
            @torch.jit.script
            def foo():
                x = 5
                if True:
                    x : Optional[int] = 7

    def test_module_inplace_construct(self):
        class M(nn.Module):
            def __init__(self, start: int):
                super().__init__()
                self.linear = nn.Linear(3, 3)
                self.attribute = start
                self.parameter = nn.Parameter(torch.tensor(3, dtype=torch.float))

            def method(self) -> int:
                return self.attribute

            @torch.jit.unused
            def unused_method(self):
                return self.attribute + self.attribute

            def forward(self, x):
                return self.linear(self.linear(x))


        class N(nn.Module):
            def __init__(self):
                super().__init__()
                self.linear = nn.Linear(4, 4)

            @torch.jit.ignore
            def ignored_method(self, x):
                return x

            def forward(self, x):
                return self.linear(x)

        m = torch.jit.script(M(3))
        n = torch.jit.script(N())

        n._reconstruct(m._c)

        inp = torch.rand((3))

        # Check that both modules produce the same output.
        with torch.no_grad():
            m_out = m(inp)
            n_out = n(inp)
            self.assertEqual(m_out, n_out)

        # Check that ignored method is still intact.
        self.assertEqual(inp, n.ignored_method(inp))

    def test_if_returning_any(self):
        """
        Check that an if statement can return different
        types early from each branch when the return
        type of the function is Any.
        """
        def if_function(inp: torch.Tensor) -> Any:
            if inp.shape[0] == 1:
                return inp * inp
            else:
                return "str"

        self.checkScript(if_function, (torch.randn(5),))

    def test_export_opnames_interface(self):
        global OneTwoModule

        @torch.jit.interface
        class OneTwoModule(nn.Module):
            def one(self, x, y):
                # type: (Tensor, Tensor) -> Tensor
                pass

            def two(self, x):
                # type: (Tensor) -> Tensor
                pass

            def forward(self, x):
                # type: (Tensor) -> Tensor
                pass

        class FooMod(nn.Module):
            def one(self, x, y):
                # type: (Tensor, Tensor) -> Tensor
                return x + y

            def two(self, x):
                # type: (Tensor) -> Tensor
                return 2 * x

            def forward(self, x):
                # type: (Tensor) -> Tensor
                return self.one(self.two(x), x)

        class BarMod(nn.Module):
            def one(self, x, y):
                # type: (Tensor, Tensor) -> Tensor
                return x * y

            def two(self, x):
                # type: (Tensor) -> Tensor
                return 2 / x

            def forward(self, x):
                # type: (Tensor) -> Tensor
                return self.two(self.one(x, x))

        class M(nn.Module):
            sub : OneTwoModule

            def __init__(self):
                super(M, self).__init__()
                self.sub = BarMod()

            def forward(self, x):
                # type: (Tensor) -> Tensor
                return self.sub.forward(x)

        def use_module_interface(mod_list: List[OneTwoModule], x: torch.Tensor):
            return mod_list[0].forward(x) + mod_list[1].forward(x)

        scripted_M_mod = torch.jit.script(M())
        # Temporarily test empty output because lite interpreter does not support interface call
        # Replace it with the issubset call when interface call is supported.
        self.assertTrue(len(torch.jit.export_opnames(scripted_M_mod)) == 0)
        # self.assertTrue(set(['aten::mul.Scalar', 'aten::mul.Tensor', 'aten::reciprocal']).issubset(
        #     set(torch.jit.export_opnames(scripted_M_mod))))

        scripted_M_mod.sub = torch.jit.script(FooMod())
        self.assertTrue(len(torch.jit.export_opnames(scripted_M_mod)) == 0)
        # self.assertTrue(set(['aten::add.Tensor', 'aten::mul.Scalar']).issubset(
        #     set(torch.jit.export_opnames(scripted_M_mod))))


if __name__ == '__main__':
    run_tests()