pytorch

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# Owner(s): ["oncall: jit"]
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import torch
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import torch._lazy
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import torch._lazy.ts_backend
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from torch import float16, float32
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from torch.testing._internal.common_utils import TestCase
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torch._lazy.ts_backend.init()
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class TestMetaKernel(TestCase):
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    def test_addmm_invalid_dtype(self):
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        """Tests that the addmm meta kernel returns the correct output type"""
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        input = torch.ones(2, 2, dtype=torch.float16).to("lazy")
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        self.assertTrue(input.dtype == torch.float16)
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        fc_nobias = torch.nn.Linear(2, 2, bias=False, dtype=float32).to("lazy")
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        with self.assertRaises(Exception):
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            out_nobias = fc_nobias(input)
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    def test_addmm(self):
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        """Tests that the addmm meta kernel returns the correct output type"""
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        input = torch.ones(2, 2, dtype=torch.float16).to("lazy")
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        self.assertEqual(input.dtype, torch.float16)
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        fc_nobias = torch.nn.Linear(2, 2, bias=False, dtype=float16).to("lazy")
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        out_nobias = fc_nobias(input)
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        self.assertEqual(out_nobias.dtype, torch.float16)
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        fc_bias = torch.nn.Linear(2, 2, bias=True, dtype=float16).to("lazy")
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        out_bias = fc_bias(input)
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        self.assertEqual(out_bias.dtype, torch.float16)
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    def test_add_invalid_device(self):
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        with self.assertRaisesRegex(RuntimeError, ".*not a lazy tensor.*"):
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            _ = torch.tensor([1], device="cpu") + torch.tensor([1], device="lazy")
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