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Revert "Reland "[pytorch][PR] Support dataclasses in TorchScript" take 2 (#74353)"
This reverts commit 5547741960a01fbd3a97d1ddd5ae9b43d8f1169c. Reverted https://github.com/pytorch/pytorch/pull/74889 on behalf of https://github.com/malfet
This commit is contained in:
Nikita Shulga
@ -1,165 +0,0 @@
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# Owner(s): ["oncall: jit"]
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# flake8: noqa
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from dataclasses import dataclass, field, InitVar
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from hypothesis import given, settings, strategies as st
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from torch.testing._internal.jit_utils import JitTestCase
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from typing import List, Optional
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import sys
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import torch
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import unittest
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from enum import Enum
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# Example jittable dataclass
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@torch.jit.script
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@dataclass(order=True)
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class Point:
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x: float
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y: float
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norm: Optional[torch.Tensor] = None
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def __post_init__(self):
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self.norm = (torch.tensor(self.x) ** 2 + torch.tensor(self.y) ** 2) ** 0.5
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class MixupScheme(Enum):
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INPUT = ["input"]
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MANIFOLD = [
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"input",
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"before_fusion_projection",
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"after_fusion_projection",
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"after_classifier_projection",
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]
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@dataclass
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class MixupParams:
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def __init__(self, alpha: float = 0.125, scheme: MixupScheme = MixupScheme.INPUT):
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self.alpha = alpha
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self.scheme = scheme
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class MixupScheme2(Enum):
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A = 1
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B = 2
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@dataclass
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class MixupParams2:
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def __init__(self, alpha: float = 0.125, scheme: MixupScheme2 = MixupScheme2.A):
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self.alpha = alpha
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self.scheme = scheme
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@dataclass
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class MixupParams3:
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def __init__(self, alpha: float = 0.125, scheme: MixupScheme2 = MixupScheme2.A):
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self.alpha = alpha
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self.scheme = scheme
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# Make sure the Meta internal tooling doesn't raise an overflow error
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NonHugeFloats = st.floats(min_value=-1e4, max_value=1e4, allow_nan=False)
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class TestDataclasses(JitTestCase):
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@classmethod
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def tearDownClass(cls):
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torch._C._jit_clear_class_registry()
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# We only support InitVar in JIT dataclasses for Python 3.8+ because it would be very hard
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# to support without the `type` attribute on InitVar (see comment in _dataclass_impls.py).
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@unittest.skipIf(sys.version_info < (3, 8), "InitVar not supported in Python < 3.8")
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def test_init_vars(self):
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@torch.jit.script
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@dataclass(order=True)
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class Point2:
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x: float
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y: float
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norm_p: InitVar[int] = 2
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norm: Optional[torch.Tensor] = None
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def __post_init__(self, norm_p: int):
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self.norm = (torch.tensor(self.x) ** norm_p + torch.tensor(self.y) ** norm_p) ** (1 / norm_p)
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def fn(x: float, y: float, p: int):
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pt = Point2(x, y, p)
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return pt.norm
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self.checkScript(fn, (1.0, 2.0, 3))
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# Sort of tests both __post_init__ and optional fields
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@settings(deadline=None)
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@given(NonHugeFloats, NonHugeFloats)
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def test__post_init__(self, x, y):
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def fn(x: float, y: float):
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pt = Point(x, y)
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return pt.norm
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self.checkScript(fn, [x, y])
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@settings(deadline=None)
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@given(st.tuples(NonHugeFloats, NonHugeFloats), st.tuples(NonHugeFloats, NonHugeFloats))
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def test_comparators(self, pt1, pt2):
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x1, y1 = pt1
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x2, y2 = pt2
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def compare(x1: float, y1: float, x2: float, y2: float):
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pt1 = Point(x1, y1)
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pt2 = Point(x2, y2)
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return (
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pt1 == pt2,
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# pt1 != pt2, # TODO: Modify interpreter to auto-resolve (a != b) to not (a == b) when there's no __ne__
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pt1 < pt2,
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pt1 <= pt2,
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pt1 > pt2,
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pt1 >= pt2,
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)
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self.checkScript(compare, [x1, y1, x2, y2])
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def test_default_factories(self):
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@dataclass
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class Foo(object):
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x: List[int] = field(default_factory=list)
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with self.assertRaises(NotImplementedError):
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torch.jit.script(Foo)
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def fn():
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foo = Foo()
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return foo.x
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torch.jit.script(fn)()
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# The user should be able to write their own __eq__ implementation
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# without us overriding it.
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def test_custom__eq__(self):
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@torch.jit.script
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@dataclass
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class CustomEq:
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a: int
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b: int
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def __eq__(self, other: 'CustomEq') -> bool:
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return self.a == other.a # ignore the b field
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def fn(a: int, b1: int, b2: int):
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pt1 = CustomEq(a, b1)
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pt2 = CustomEq(a, b2)
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return pt1 == pt2
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self.checkScript(fn, [1, 2, 3])
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def test_no_source(self):
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with self.assertRaises(RuntimeError):
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# uses list in Enum is not supported
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torch.jit.script(MixupParams)
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torch.jit.script(MixupParams2) # don't throw
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def test_use_unregistered_dataclass_raises(self):
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def f(a: MixupParams3):
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return 0
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with self.assertRaises(OSError):
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torch.jit.script(f)
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@ -6,6 +6,7 @@ from torch.testing._internal.jit_utils import JitTestCase, make_global
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from torch.testing import FileCheck
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from torch import jit
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from jit.test_module_interface import TestModuleInterface # noqa: F401
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import unittest
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import os
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import sys
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import torch
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@ -46,6 +47,24 @@ class TestMisc(JitTestCase):
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self.assertEqual(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, "could not get source code"):
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torch.jit.script(fn)
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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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@ -78,7 +78,6 @@ from jit.test_device_analysis import TestDeviceAnalysis # noqa: F401
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from jit.test_dce import TestDCE # noqa: F401
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from jit.test_sparse import TestSparse # noqa: F401
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from jit.test_tensor_methods import TestTensorMethods # noqa: F401
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from jit.test_dataclasses import TestDataclasses # noqa: F401
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# Torch
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from torch import Tensor
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@ -47,24 +47,6 @@ except ImportError:
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boolean_dispatched: 'weakref.WeakKeyDictionary[Callable, Dict[str, Callable]]' = weakref.WeakKeyDictionary() # noqa: T484
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FAKE_FILENAME_PREFIX = '__torch_jit_dataclass'
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class SourceLoader:
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def __init__(self):
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self.content = {}
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def cache(self, fn, source):
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self.content[fn] = source
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def get_source(self, fn):
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return self.content.get(fn)
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loader = SourceLoader()
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def createResolutionCallbackFromEnv(lookup_base):
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"""
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Creates a resolution callback that will look up qualified names in an
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@ -341,14 +323,6 @@ def get_type_hint_captures(fn):
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A Dict[str, Any] containing a mapping from the literal annotations used on
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fn to the Python objects they refer to.
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"""
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# First, try to get the source of the function. We'll need to parse it to find the actual string names
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# that were used to annotate the types, since inspect.signature() will only return the class object that
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# the annotation refers to, not the string name. If we can't get the source, simply return an empty dict.
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# This may happen in cases where the function is synthesized dynamically at runtime.
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src = loader.get_source(fn)
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if src is None:
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src = inspect.getsource(fn)
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# Gather a dictionary of parameter name -> type, skipping any parameters whose annotated
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# types are strings. These are only understood by TorchScript in the context of a type annotation
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# that refers to a class in its own definition, but trying to include a mapping for this in the result
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@ -364,6 +338,8 @@ def get_type_hint_captures(fn):
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# Then, get the literal type annotations from the function declaration
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# by source inspection. This accounts for the case in which aliases are used
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# to annotate the arguments (e.g device_t = torch.device, and then d: device_t).
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src = inspect.getsource(fn)
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# frontend.py cannot be used here because it includes _jit_internal, so use ast instead.
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a = ast.parse(dedent(src))
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if len(a.body) != 1 or not isinstance(a.body[0], ast.FunctionDef):
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@ -929,7 +905,7 @@ def is_optional(ann):
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def is_union_as_optional(ann):
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ann_args = ann.__args__
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return len(ann_args) == 2 and (None in ann_args or type(None) in ann_args)
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return len(ann_args) == 2 and None in ann_args
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return is_optional_as_optional(ann) or (is_union(ann) and is_union_as_optional(ann))
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@ -48,20 +48,13 @@ def normalize_source_lines(sourcelines: List[str]) -> List[str]:
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return text[text.startswith(prefix) and len(prefix):]
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# Find the line and line number containing the function definition
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idx = None
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for i, l in enumerate(sourcelines):
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if l.lstrip().startswith("def"):
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idx = i
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break
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# This will happen when the function is a lambda- we won't find "def" anywhere in the source
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# lines in that case. Currently trying to JIT compile a lambda will throw an error up in
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# `parse_def()`, but we might want to handle this case in the future.
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if idx is None:
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return sourcelines
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fn_def = sourcelines[idx]
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# Get a string representing the amount of leading whitespace
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fn_def = sourcelines[idx]
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whitespace = fn_def.split("def")[0]
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# Add this leading whitespace to all lines before and after the `def`
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@ -227,11 +227,10 @@ struct TORCH_API CompilationUnit {
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// Tombstone the method in the compilation unit.
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// Don't erase because the dict_
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auto it = dict_.find(method->qualname());
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if (it != dict_.end()) {
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functions_[it->second] = nullptr;
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// Erase in our big lookup table
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dict_.erase(it);
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}
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TORCH_INTERNAL_ASSERT(it != dict_.end());
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functions_[it->second] = nullptr;
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// Erase in our big lookup table
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dict_.erase(it);
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}
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// Classes can have multiple pointers to the same hook,
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// need to make sure to not delete it twice
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@ -1,152 +0,0 @@
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# Functions for synthesizing magic methods for JIT-compiled dataclasses
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import os
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from functools import partial
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from torch._jit_internal import is_optional, FAKE_FILENAME_PREFIX
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from torch._sources import ParsedDef, SourceContext
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from typing import Callable, Dict, List
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import ast
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import dataclasses
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import inspect
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import sys
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def _get_fake_filename(cls, method_name):
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return os.path.join(FAKE_FILENAME_PREFIX, cls.__name__, method_name)
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def compose_fn(cls, name: str, body_lines: List[str], signature: str) -> ParsedDef:
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body = '\n'.join(f' {b}' for b in body_lines)
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decl = f'def {name}{signature}:\n{body}'
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# Parse the function declaration
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try:
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py_ast = ast.parse(decl)
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except SyntaxError:
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# This should only happen if there's some unforeseeable change
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# in the dataclasses module that makes our synthesized code fail
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raise RuntimeError(
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f"TorchScript failed to synthesize dataclass method '{name}' for class '{cls.__name__}'. "
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"Please file a bug report at <https://github.com/pytorch/pytorch/issues>"
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)
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fake_filename = _get_fake_filename(cls, name)
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# Parse the function
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return ParsedDef(
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py_ast,
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ctx=SourceContext(
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source=decl,
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filename=fake_filename,
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file_lineno=0,
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leading_whitespace_len=0
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),
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source=decl,
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filename=fake_filename,
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file_lineno=0
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)
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def synthesize__init__(cls) -> ParsedDef:
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# Supporting default factories in the way that people expect would sort of require us to
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# allow compiling lambda functions, which is not currently supported.
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if any(field.default_factory is not dataclasses.MISSING for field in dataclasses.fields(cls)):
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raise NotImplementedError("Default factory initializers are not supported in TorchScript dataclasses")
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# Simply read off the generated __init__ signature from CPython's implementation. It'll be
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# almost correct except for InitVar annotations, which we need to handle specially.
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signature = inspect.signature(cls.__init__)
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# Handle InitVars if needed (only works on Python 3.8+, when a `type` attribute was added to InitVar);
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# see CPython commit here https://github.com/python/cpython/commit/01ee12ba35a333e8a6a25c4153c4a21838e9585c
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init_vars: List[str] = []
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if sys.version_info >= (3, 8):
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params = []
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for name, param in signature.parameters.items():
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ann = param.annotation
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if isinstance(ann, dataclasses.InitVar):
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# The TorchScript interpreter can't handle InitVar annotations, so we unwrap the underlying type here
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init_vars.append(name)
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params.append(param.replace(annotation=ann.type)) # type: ignore[attr-defined]
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else:
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params.append(param)
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signature = signature.replace(parameters=params)
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body = [
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# Assign all attributes to self
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f'self.{field.name} = {field.name}'
|
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for field in dataclasses.fields(cls)
|
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if field.init and field.name not in init_vars
|
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]
|
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# Call user's impl of __post_init__ if it exists
|
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if hasattr(cls, '__post_init__'):
|
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body.append('self.__post_init__(' + ', '.join(init_vars) + ')')
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|
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return compose_fn(cls, '__init__', body or ['pass'], signature=str(signature))
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|
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# This is a placeholder at the moment since the TorchScript interpreter doesn't call __repr__
|
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def synthesize__repr__(cls) -> ParsedDef:
|
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return compose_fn(
|
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cls, '__repr__',
|
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[f"return '{cls.__name__}(" + ", ".join([
|
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f"{field.name}=self.{field.name}"
|
||||
for field in dataclasses.fields(cls) if field.repr
|
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]) + ")'"],
|
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signature='(self) -> str'
|
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)
|
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|
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def synthesize__hash__(cls) -> ParsedDef:
|
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return compose_fn(
|
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cls, '__hash__',
|
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[
|
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# This is just a placeholder to prevent compilation from failing; this won't even get called at
|
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# all right now because the TorchScript interpreter doesn't call custom __hash__ implementations
|
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"raise NotImplementedError('__hash__ is not supported for dataclasses in TorchScript')"
|
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],
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signature='(self) -> int'
|
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)
|
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|
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# Implementation for __eq__ and __ne__
|
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def synthesize_equality(cls, name: str, converse: str) -> ParsedDef:
|
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return synthesize_comparison(cls, name, allow_eq=True, raise_on_none=False, inner=[
|
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f"if val1 {converse} val2: return False"
|
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])
|
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|
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def synthesize_inequality(cls, name: str, op: str, allow_eq: bool) -> ParsedDef:
|
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return synthesize_comparison(cls, name, allow_eq, raise_on_none=True, inner=[
|
||||
f"if val1 {op} val2: return True",
|
||||
f"elif val2 {op} val1: return False",
|
||||
])
|
||||
|
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def synthesize_comparison(cls, name: str, allow_eq: bool, raise_on_none: bool, inner: List[str]) -> ParsedDef:
|
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body = []
|
||||
for field in dataclasses.fields(cls):
|
||||
if not field.compare:
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continue
|
||||
|
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body.extend([
|
||||
f"val1 = self.{field.name}",
|
||||
f"val2 = other.{field.name}",
|
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])
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body.extend(
|
||||
inner if not is_optional(field.type) else [
|
||||
# Type refinement for optional fields; we need this to avoid type errors from the interpreter
|
||||
"if val1 is not None and val2 is not None:",
|
||||
*[' ' + line for line in inner],
|
||||
"elif (val1 is None) != (val2 is None):",
|
||||
f" raise TypeError('Cannot compare {cls.__name__} with None')" if raise_on_none else " return False"
|
||||
]
|
||||
)
|
||||
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||||
body.append(f"return {allow_eq}")
|
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return compose_fn(cls, name, body, signature=f'(self, other: {cls.__name__}) -> bool')
|
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|
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DATACLASS_MAGIC_METHODS: Dict[str, Callable] = {
|
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"__init__": synthesize__init__,
|
||||
"__repr__": synthesize__repr__,
|
||||
"__hash__": synthesize__hash__,
|
||||
"__eq__": partial(synthesize_equality, name="__eq__", converse="!="),
|
||||
"__ne__": partial(synthesize_equality, name="__ne__", converse="=="),
|
||||
"__lt__": partial(synthesize_inequality, name="__lt__", op="<", allow_eq=False),
|
||||
"__le__": partial(synthesize_inequality, name="__le__", op="<", allow_eq=True),
|
||||
"__gt__": partial(synthesize_inequality, name="__gt__", op=">", allow_eq=False),
|
||||
"__ge__": partial(synthesize_inequality, name="__ge__", op=">", allow_eq=True),
|
||||
}
|
||||
@ -284,10 +284,7 @@ def get_enum_value_type(e: Type[enum.Enum], loc):
|
||||
# Even though Python supports this case, we chose to not implement it to
|
||||
# avoid overcomplicate logic here for a rare use case. Please report a
|
||||
# feature request if you find it necessary.
|
||||
res = torch._C.unify_type_list(ir_types)
|
||||
if not res:
|
||||
return AnyType.get()
|
||||
return res
|
||||
return torch._C.unify_type_list(ir_types)
|
||||
|
||||
def is_tensor(ann):
|
||||
if issubclass(ann, torch.Tensor):
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
import torch
|
||||
import sys
|
||||
import ast
|
||||
import dataclasses
|
||||
import inspect
|
||||
import string
|
||||
from collections import namedtuple
|
||||
@ -18,11 +17,9 @@ from torch._C._jit_tree_views import (
|
||||
SliceExpr, Subscript, TernaryIf, With, WithItem, Property,
|
||||
DictComp,
|
||||
)
|
||||
from torch._sources import get_source_lines_and_file, ParsedDef, parse_def, make_source_context
|
||||
from torch.jit._dataclass_impls import DATACLASS_MAGIC_METHODS
|
||||
from torch._sources import get_source_lines_and_file, parse_def, make_source_context
|
||||
from torch.jit._monkeytype_config import monkeytype_trace, get_qualified_name
|
||||
from torch._jit_internal import should_drop, is_static_fn, FunctionModifiers # noqa: F401
|
||||
from torch import _jit_internal
|
||||
import torch.jit.annotations
|
||||
|
||||
_IS_ASTUNPARSE_INSTALLED = False
|
||||
@ -198,48 +195,24 @@ def get_jit_class_def(cls, self_name):
|
||||
def is_classmethod(fn):
|
||||
return inspect.ismethod(fn) and getattr(fn, "__self__", None) == cls
|
||||
|
||||
# Get and parse the source code for this class
|
||||
sourcelines, file_lineno, filename = get_source_lines_and_file(cls, torch._C.ErrorReport.call_stack())
|
||||
source = ''.join(sourcelines)
|
||||
methods = [get_jit_def(obj,
|
||||
name,
|
||||
self_name=self_name,
|
||||
is_classmethod=is_classmethod(obj)) for (name, obj) in methods]
|
||||
|
||||
dedent_src = dedent(source)
|
||||
py_ast = ast.parse(dedent_src)
|
||||
|
||||
class_ast = py_ast.body[0]
|
||||
assert isinstance(class_ast, ast.ClassDef)
|
||||
|
||||
# Special case for dataclasses. In general we need access to the source code for
|
||||
# an object in order to JIT compile it. But the dataclasses module dynamically synthesizes
|
||||
# magic methods for classes, and we can't get the source code for these methods. As a
|
||||
# workaround, we synthesize TorchScript-friendly implementations ourselves.
|
||||
if dataclasses.is_dataclass(cls):
|
||||
# Detect whether the user manually implemented any of the magic methods. If they did,
|
||||
# we don't want to synthesize/override them.
|
||||
overrides = {
|
||||
method.name
|
||||
for method in class_ast.body
|
||||
if isinstance(method, ast.FunctionDef) and method.name in DATACLASS_MAGIC_METHODS
|
||||
}
|
||||
for i, (name, _) in enumerate(methods):
|
||||
# Is this a magic method we can synthesize?
|
||||
synthesizer_fn = DATACLASS_MAGIC_METHODS.get(name)
|
||||
if synthesizer_fn and name not in overrides:
|
||||
parsed_def = synthesizer_fn(cls)
|
||||
methods[i] = name, parsed_def
|
||||
func = getattr(cls, name)
|
||||
_jit_internal.loader.cache(func, parsed_def.source)
|
||||
|
||||
method_defs = [
|
||||
get_jit_def(obj, name, self_name=self_name, is_classmethod=is_classmethod(obj))
|
||||
for (name, obj) in methods
|
||||
]
|
||||
properties = get_class_properties(cls, self_name)
|
||||
|
||||
sourcelines, file_lineno, filename = get_source_lines_and_file(cls, torch._C.ErrorReport.call_stack())
|
||||
source = ''.join(sourcelines)
|
||||
dedent_src = dedent(source)
|
||||
py_ast = ast.parse(dedent_src)
|
||||
leading_whitespace_len = len(source.split('\n', 1)[0]) - len(dedent_src.split('\n', 1)[0])
|
||||
ctx = make_source_context(source, filename, file_lineno, leading_whitespace_len, False)
|
||||
class_ast = py_ast.body[0]
|
||||
assert isinstance(class_ast, ast.ClassDef)
|
||||
assigns = get_class_assigns(ctx, class_ast)
|
||||
|
||||
return build_class_def(ctx, class_ast, method_defs, properties, self_name, assigns)
|
||||
return build_class_def(ctx, class_ast, methods, properties, self_name, assigns)
|
||||
|
||||
|
||||
def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
|
||||
@ -247,7 +220,7 @@ def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
|
||||
Build a JIT AST (TreeView) from the given function.
|
||||
|
||||
Args:
|
||||
fn: A function object to compile or a pre-parsed ParsedDef object
|
||||
fn: A function object to compile
|
||||
def_name: The name to give to the resulting AST object. This is not
|
||||
always the same as `fn.__name__`, for example:
|
||||
def _forward(self):
|
||||
@ -257,7 +230,7 @@ def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
|
||||
but we want the result AST to have the name "forward".
|
||||
self_name: If this function is a method, what the type name of `self` is.
|
||||
"""
|
||||
parsed_def = parse_def(fn) if not isinstance(fn, ParsedDef) else fn
|
||||
parsed_def = parse_def(fn)
|
||||
type_line = torch.jit.annotations.get_type_line(parsed_def.source)
|
||||
fn_def = parsed_def.ast.body[0]
|
||||
|
||||
@ -284,7 +257,7 @@ def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
|
||||
# for the arguments from type_trace_db
|
||||
type_trace_db = torch.jit._script._get_type_trace_db()
|
||||
pdt_arg_types = None
|
||||
if monkeytype_trace and not isinstance(fn, ParsedDef):
|
||||
if monkeytype_trace:
|
||||
qualname = get_qualified_name(fn)
|
||||
pdt_arg_types = type_trace_db.get_args_types(qualname)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user