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#!/usr/bin/env python3
# Owner(s): ["module: internals"]
import unittest
import torch
from torch.testing._internal.common_utils import run_tests, TestCase
class TestComparisonUtils(TestCase):
def test_all_equal_no_assert(self):
t = torch.tensor([0.5])
torch._assert_tensor_metadata(t, [1], [1], torch.float)
def test_all_equal_no_assert_nones(self):
t = torch.tensor([0.5])
torch._assert_tensor_metadata(t, None, None, None)
def test_assert_dtype(self):
t = torch.tensor([0.5])
with self.assertRaises(RuntimeError):
torch._assert_tensor_metadata(t, None, None, torch.int32)
def test_assert_strides(self):
t = torch.tensor([0.5])
with self.assertRaises(RuntimeError):
torch._assert_tensor_metadata(t, None, [3], torch.float)
def test_assert_sizes(self):
t = torch.tensor([0.5])
with self.assertRaises(RuntimeError):
torch._assert_tensor_metadata(t, [3], [1], torch.float)
@unittest.skipIf(not torch.cuda.is_available(), "Requires cuda")
def test_assert_device(self):
t = torch.tensor([0.5], device="cpu")
with self.assertRaises(RuntimeError):
torch._assert_tensor_metadata(t, device="cuda")
def test_assert_layout(self):
t = torch.tensor([0.5])
with self.assertRaises(RuntimeError):
torch._assert_tensor_metadata(t, layout=torch.sparse_coo)
if __name__ == "__main__":
run_tests()
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