mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 13:44:15 +08:00
bdecedd2d7
Summary: Follow up to https://github.com/pytorch/pytorch/pull/39269, use the python resolver for all type resolutions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/39880 Reviewed By: jamesr66a Differential Revision: D22008110 Pulled By: eellison fbshipit-source-id: 4e62eb0867d79e7d45156f88d628a28fa1578b9e
561 lines
17 KiB
Python
561 lines
17 KiB
Python
from collections import namedtuple
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from torch.testing._internal.common_utils import run_tests
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from torch.testing._internal.jit_utils import JitTestCase
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from torch.testing import FileCheck
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from torch import jit
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from typing import NamedTuple, List, Optional, Dict, Tuple, Any
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from jit.test_module_interface import TestModuleInterface # noqa: F401
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import unittest
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import sys
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import torch
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import torch.testing._internal.jit_utils
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import torch.nn as nn
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import types
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class TestScriptPy3(JitTestCase):
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def test_joined_str(self):
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def func(x):
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hello, test = "Hello", "test"
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print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
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print(f"format blank") # noqa F541
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hi = 'hi'
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print(f"stuff before {hi}")
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print(f"{hi} stuff after")
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return x + 1
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x = torch.arange(4., requires_grad=True)
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# TODO: Add support for f-strings in string parser frontend
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# self.checkScript(func, [x], optimize=True, capture_output=True)
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with self.capture_stdout() as captured:
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out = func(x)
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scripted = torch.jit.script(func)
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with self.capture_stdout() as captured_script:
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out_script = func(x)
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self.assertAlmostEqual(out, out_script)
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self.assertEqual(captured, captured_script)
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@unittest.skipIf(sys.version_info[:2] < (3, 7), "`dataclasses` module not present on < 3.7")
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def test_dataclass_error(self):
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from dataclasses import dataclass
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@dataclass
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class NormalizationInfo(object):
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mean: float = 0.0
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def compute(self, total_rows):
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return self.mean
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def fn():
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return NormalizationInfo(1, 2, 3, 4, 5)
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with self.assertRaisesRegex(OSError, "NormalizationInfo"):
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torch.jit.script(fn)
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def test_optional_dict_construct(self):
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class M(torch.nn.Module):
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def use(self, buffer: Dict[str, Optional[torch.Tensor]]):
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return buffer["prev_key"]
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def forward(self, x):
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prev_key = torch.rand(2, 3)
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next_key = torch.rand(2, 3)
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saved_state: Dict[str, Optional[torch.Tensor]] = {
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"prev_key": prev_key,
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"next_key": next_key,
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}
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return self.use(saved_state)
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self.checkModule(M(), (torch.rand(2, 2),))
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def test_kwarg_support(self):
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with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, "variable number of arguments"):
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class M(torch.nn.Module):
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def forward(self, *, n_tokens: int, device_name: str = 2):
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pass
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torch.jit.script(M())
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class M(torch.nn.Module):
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def forward(self, *, n_tokens: int, device_name: str):
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return n_tokens, device_name
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sm = torch.jit.script(M())
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with self.assertRaisesRegex(RuntimeError, "missing value for argument 'n_tokens'"):
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sm()
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input = (3, 'hello')
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self.assertEqual(sm(*input), input)
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def test_named_tuple(self):
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class FeatureVector(NamedTuple):
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float_features: float
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sequence_features: List[float]
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time_since_first: float
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@torch.jit.script
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def foo(x) -> float:
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fv = FeatureVector(3.0, [3.0], 3.0) # noqa
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rv = fv.float_features
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for val in fv.sequence_features:
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rv += val
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rv *= fv.time_since_first
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return rv
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self.assertEqual(foo(torch.rand(3, 4)), 18.0)
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def test_named_tuple_constant(self):
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class Tup(NamedTuple):
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a: int
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b: int
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@torch.jit.script
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def foo():
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return Tup(1, 2)
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self.assertEqual(foo(), Tup(1, 2))
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def test_dict_preserves_order(self):
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def dict_ordering():
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a : Dict[int, int] = {}
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for i in range(1000):
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a[i] = i + 1
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return a
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self.checkScript(dict_ordering, ())
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di = torch.jit.script(dict_ordering)()
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res = list(di.items())
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for i in range(1000):
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key, value = res[i]
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self.assertTrue(key == i and value == i + 1)
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def test_list_unification_hint(self):
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with self.assertRaisesRegex(RuntimeError, "Expected a List type hint"):
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@torch.jit.script
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def x():
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b : int = [2, 3]
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return b
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def test_return_named_tuple(self):
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class FeatureVector(NamedTuple):
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float_features: float
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sequence_features: List[float]
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time_since_first: float
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@torch.jit.script
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def foo(x):
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fv = FeatureVector(3.0, [3.0], 3.0)
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return fv
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out = foo(torch.rand(3, 4))
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out = foo(torch.rand(3, 4))
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self.assertEqual(out.float_features, 3.0)
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self.assertEqual(out.sequence_features, [3.0])
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self.assertEqual(out.time_since_first, 3.0)
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def test_named_tuple_as_attr(self):
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class Config(NamedTuple):
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size: int
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class MyMod(nn.Module):
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configs: Dict[int, Config]
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def __init__(self, configs):
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super().__init__()
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self.configs = configs
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def forward(self, x):
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for _id, config in self.configs.items():
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x += config.size
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return x
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s = torch.jit.script(MyMod({0: Config(size=16)}))
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def test_types_as_values(self):
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def fn(m: torch.Tensor) -> torch.device:
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return m.device
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self.checkScript(fn, [torch.randn(2, 2)])
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GG = namedtuple('GG', ['f', 'g'])
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@torch.jit.ignore
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def foo(self, x, z):
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# type: (Tensor, Tensor) -> Tuple[GG, GG]
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return GG(x, z), GG(x, z)
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def forward(self, x, z):
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return self.foo(x, z)
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foo = torch.jit.script(Foo())
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y = foo(torch.randn(2, 2), torch.randn(2, 2))
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@torch.jit.ignore
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def foo(self, x, z) -> Tuple[GG, GG]:
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return GG(x, z)
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def forward(self, x, z):
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return self.foo(x, z)
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foo = torch.jit.script(Foo())
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y = foo(torch.randn(2, 2), torch.randn(2, 2))
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def test_named_tuple_resolution(self):
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class TheType(NamedTuple):
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t: int
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class MyModule(types.ModuleType):
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def __init__(self):
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super(MyModule, self).__init__('MyModule')
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def __getattr__(self, attr):
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return TheType
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some_module = MyModule()
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def fn() -> some_module.Type:
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return some_module.Type(1)
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self.checkScript(fn, [])
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def test_ignore_with_types(self):
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@torch.jit.ignore
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def fn(x: Dict[str, Optional[torch.Tensor]]):
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return x + 10
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class M(torch.nn.Module):
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def __init__(self):
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super(M, self).__init__()
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def forward(self, in_batch: Dict[str, Optional[torch.Tensor]]) -> torch.Tensor:
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self.dropout_modality(in_batch)
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fn(in_batch)
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return torch.tensor(1)
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@torch.jit.ignore
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def dropout_modality(self, in_batch: Dict[str, Optional[torch.Tensor]]) -> Dict[str, Optional[torch.Tensor]]:
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return in_batch
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sm = torch.jit.script(M())
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FileCheck().check("dropout_modality").check("in_batch").run(str(sm.graph))
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def test_python_callable(self):
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class MyPythonClass(object):
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@torch.jit.ignore
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def __call__(self, *args) -> str:
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return str(type(args[0]))
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the_class = MyPythonClass()
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@torch.jit.script
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def fn(x):
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return the_class(x)
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# This doesn't involve the string frontend, so don't use checkScript
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x = torch.ones(2)
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self.assertEqual(fn(x), the_class(x))
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def test_bad_types(self):
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@torch.jit.ignore
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def fn(my_arg):
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return my_arg + 10
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with self.assertRaisesRegex(RuntimeError, "argument 'my_arg'"):
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@torch.jit.script
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def other_fn(x):
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return fn('2')
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def test_named_tuple_slice_unpack(self):
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int]
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@torch.jit.script
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def foo(a : int, b : float, c : List[int]):
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tup = MyCoolNamedTuple(a, b, c) # noqa
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my_a, my_b, my_c = tup
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return tup[:1], my_a, my_c
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self.assertEqual(foo(3, 3.5, [6]), ((3,), 3, [6]))
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def test_named_tuple_lower(self):
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int]
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@torch.jit.script
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def foo(a : int):
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tup = MyCoolNamedTuple(a, 3.14, [9]) # noqa
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return tup
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FileCheck().check('TupleConstruct').run(foo.graph)
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torch._C._jit_pass_lower_all_tuples(foo.graph)
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FileCheck().check_not('TupleConstruct').run(foo.graph)
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def test_named_tuple_type_annotation(self):
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global MyCoolNamedTuple # see [local resolution in python]
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int]
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@torch.jit.script
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def foo(x : MyCoolNamedTuple) -> MyCoolNamedTuple:
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return x
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mnt = MyCoolNamedTuple(42, 420.0, [666])
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self.assertEqual(foo(mnt), mnt)
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def test_named_tuple_wrong_types(self):
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int]
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with self.assertRaisesRegex(RuntimeError, "Expected a value of type 'int' for argument 'a'"
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" but instead found type 'str'"):
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@torch.jit.script
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def foo():
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tup = MyCoolNamedTuple('foo', 'bar', 'baz') # noqa
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return tup
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def test_named_tuple_kwarg_construct(self):
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int]
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@torch.jit.script
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def foo():
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tup = MyCoolNamedTuple(c=[1, 2, 3], b=3.5, a=9) # noqa
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return tup
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tup = foo()
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self.assertEqual(tup.a, 9)
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self.assertEqual(tup.b, 3.5)
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self.assertEqual(tup.c, [1, 2, 3])
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def test_named_tuple_default_error(self):
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int] = [3, 4, 5]
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with self.assertRaisesRegex(RuntimeError, 'Default values are currently not supported'):
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@torch.jit.script
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def foo():
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tup = MyCoolNamedTuple(c=[1, 2, 3], b=3.5, a=9) # noqa
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return tup
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@unittest.skipIf(True, "broken while these tests were not in CI")
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def test_named_tuple_serialization(self):
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class MyCoolNamedTuple(NamedTuple):
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a : int
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b : float
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c : List[int]
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class MyMod(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self):
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return MyCoolNamedTuple(3, 3.5, [3, 4, 5])
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mm = MyMod()
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mm.save('foo.zip')
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torch.testing._internal.jit_utils.clear_class_registry()
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loaded = torch.jit.load('foo.zip')
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out = mm()
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out_loaded = loaded()
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for name in ['a', 'b', 'c']:
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self.assertEqual(getattr(out_loaded, name), getattr(out, name))
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def test_type_annotate_py3(self):
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def fn():
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a : List[int] = []
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b : torch.Tensor = torch.ones(2, 2)
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c : Optional[torch.Tensor] = None
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d : Optional[torch.Tensor] = torch.ones(3, 4)
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for _ in range(10):
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a.append(4)
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c = torch.ones(2, 2)
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d = None
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return a, b, c, d
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self.checkScript(fn, ())
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def wrong_type():
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wrong : List[int] = [0.5]
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return wrong
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with self.assertRaisesRegex(RuntimeError, "Lists must contain only a single type"):
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torch.jit.script(wrong_type)
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def test_subexpression_List_Future(self):
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@torch.jit.script
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def fn(x: List[torch.jit.Future[int]]) -> torch.jit.Future[int]:
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return x[0]
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FileCheck().check('Future[int]').check('Future[int]').run(fn.graph)
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def test_subexpression_Future_annotate(self):
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@torch.jit.script
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def fn() -> torch.jit.Future[int]:
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x: List[torch.jit.Future[int]] = []
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return x[0]
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FileCheck().check("Future[int][]").run(fn.graph)
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def test_future_isinstance(self):
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@torch.jit.script
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def fn(x: Any) -> torch.jit.Future[int]:
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assert isinstance(x, jit.Future[int])
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return x
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FileCheck().check("Future[int]").run(fn.graph)
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def test_subexpression_Tuple_int_int_Future(self):
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@torch.jit.script
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def fn(x: Tuple[int, int, torch.jit.Future[int]]) -> Tuple[int, torch.jit.Future[int]]:
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return x[0], x[2]
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FileCheck().check('(int, int, Future[int])').check('(int, Future[int])').run(fn.graph)
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def test_subexpression_Dict_int_Future(self):
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@torch.jit.script
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def fn(x: Dict[int, torch.jit.Future[int]], y: int) -> torch.jit.Future[int]:
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return x[y]
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FileCheck().check('Dict(int, Future(int))').check('Future[int]').run(fn.graph)
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def test_subexpression_Optional(self):
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@torch.jit.script
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def fn(x: Optional[Dict[int, torch.jit.Future[int]]]) -> Optional[torch.jit.Future[int]]:
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if x is not None:
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return x[0]
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else:
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return None
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FileCheck().check('Dict(int, Future(int))?').run(fn.graph)
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def test_unimported_type_resolution(self):
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# verify fallback from the python resolver to the c++ resolver
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@ torch.jit.script
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def fn(x):
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# type: (number) -> number
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return x + 1
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FileCheck().check('Scalar').run(fn.graph)
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def test_parser_bug(self):
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def parser_bug(o: Optional[torch.Tensor]):
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pass
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def test_mismatched_annotation(self):
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with self.assertRaisesRegex(RuntimeError, 'annotated with type'):
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@torch.jit.script
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def foo():
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x : str = 4
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return x
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def test_reannotate(self):
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with self.assertRaisesRegex(RuntimeError, 'declare and annotate'):
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@torch.jit.script
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def foo():
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x = 5
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if True:
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x : Optional[int] = 7
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def test_export_opnames_interface(self):
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global OneTwoModule
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@torch.jit.interface
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class OneTwoModule(nn.Module):
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def one(self, x, y):
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# type: (Tensor, Tensor) -> Tensor
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pass
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def two(self, x):
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# type: (Tensor) -> Tensor
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pass
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def forward(self, x):
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# type: (Tensor) -> Tensor
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pass
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class FooMod(nn.Module):
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def one(self, x, y):
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# type: (Tensor, Tensor) -> Tensor
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return x + y
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def two(self, x):
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# type: (Tensor) -> Tensor
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return 2 * x
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def forward(self, x):
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# type: (Tensor) -> Tensor
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return self.one(self.two(x), x)
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class BarMod(nn.Module):
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def one(self, x, y):
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# type: (Tensor, Tensor) -> Tensor
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return x * y
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def two(self, x):
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# type: (Tensor) -> Tensor
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return 2 / x
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|
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def forward(self, x):
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# type: (Tensor) -> Tensor
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return self.two(self.one(x, x))
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|
|
|
class M(nn.Module):
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sub : OneTwoModule
|
|
|
|
def __init__(self):
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super(M, self).__init__()
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self.sub = BarMod()
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|
|
|
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())
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|
# Temporarily test empty output because lite interpreter does not support interface call
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|
# Replace it with the issubset call when interface call is supported.
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|
self.assertTrue(len(torch.jit.export_opnames(scripted_M_mod)) == 0)
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|
# 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()
|