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bf7e290854
This PR is part of a series attempting to re-submit https://github.com/pytorch/pytorch/pull/134592 as smaller PRs. In jit tests: - Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run. - Raise a RuntimeError on tests which have been disabled (not run) Pull Request resolved: https://github.com/pytorch/pytorch/pull/154725 Approved by: https://github.com/clee2000
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
# Owner(s): ["oncall: jit"]
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import os
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import sys
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from typing import List, Tuple
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import torch
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# Make the helper files in test/ importable
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pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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sys.path.append(pytorch_test_dir)
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from torch.testing._internal.common_utils import raise_on_run_directly
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from torch.testing._internal.jit_utils import JitTestCase
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class TestHash(JitTestCase):
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def test_hash_tuple(self):
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def fn(t1: Tuple[int, int], t2: Tuple[int, int]) -> bool:
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return hash(t1) == hash(t2)
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self.checkScript(fn, ((1, 2), (1, 2)))
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self.checkScript(fn, ((1, 2), (3, 4)))
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self.checkScript(fn, ((1, 2), (2, 1)))
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def test_hash_tuple_nested_unhashable_type(self):
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# Tuples may contain unhashable types like `list`, check that we error
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# properly in that case.
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@torch.jit.script
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def fn_unhashable(t1: Tuple[int, List[int]]):
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return hash(t1)
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with self.assertRaisesRegexWithHighlight(RuntimeError, "unhashable", "hash"):
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fn_unhashable((1, [1]))
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def test_hash_tensor(self):
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"""Tensors should hash by identity"""
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def fn(t1, t2):
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return hash(t1) == hash(t2)
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tensor1 = torch.tensor(1)
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tensor1_clone = torch.tensor(1)
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tensor2 = torch.tensor(2)
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self.checkScript(fn, (tensor1, tensor1))
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self.checkScript(fn, (tensor1, tensor1_clone))
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self.checkScript(fn, (tensor1, tensor2))
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def test_hash_none(self):
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def fn():
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n1 = None
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n2 = None
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return hash(n1) == hash(n2)
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self.checkScript(fn, ())
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def test_hash_bool(self):
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def fn(b1: bool, b2: bool):
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return hash(b1) == hash(b2)
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self.checkScript(fn, (True, False))
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self.checkScript(fn, (True, True))
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self.checkScript(fn, (False, True))
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self.checkScript(fn, (False, False))
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def test_hash_float(self):
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def fn(f1: float, f2: float):
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return hash(f1) == hash(f2)
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self.checkScript(fn, (1.2345, 1.2345))
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self.checkScript(fn, (1.2345, 6.789))
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self.checkScript(fn, (1.2345, float("inf")))
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self.checkScript(fn, (float("inf"), float("inf")))
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self.checkScript(fn, (1.2345, float("nan")))
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if sys.version_info < (3, 10):
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# Hash of two nans are not guaranteed to be equal. From https://docs.python.org/3/whatsnew/3.10.html :
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# Hashes of NaN values of both float type and decimal.Decimal type now depend on object identity.
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self.checkScript(fn, (float("nan"), float("nan")))
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self.checkScript(fn, (float("nan"), float("inf")))
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def test_hash_int(self):
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def fn(i1: int, i2: int):
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return hash(i1) == hash(i2)
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self.checkScript(fn, (123, 456))
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self.checkScript(fn, (123, 123))
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self.checkScript(fn, (123, -123))
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self.checkScript(fn, (-123, -123))
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self.checkScript(fn, (123, 0))
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def test_hash_string(self):
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def fn(s1: str, s2: str):
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return hash(s1) == hash(s2)
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self.checkScript(fn, ("foo", "foo"))
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self.checkScript(fn, ("foo", "bar"))
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self.checkScript(fn, ("foo", ""))
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def test_hash_device(self):
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def fn(d1: torch.device, d2: torch.device):
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return hash(d1) == hash(d2)
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gpu0 = torch.device("cuda:0")
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gpu1 = torch.device("cuda:1")
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cpu = torch.device("cpu")
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self.checkScript(fn, (gpu0, gpu0))
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self.checkScript(fn, (gpu0, gpu1))
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self.checkScript(fn, (gpu0, cpu))
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self.checkScript(fn, (cpu, cpu))
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if __name__ == "__main__":
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raise_on_run_directly("test/test_jit.py")
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