[Bug] relay pytorch frontend not support logical_or op

[taomiao]

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Expected behavior

work well

Actual behavior

self = <tvm.relay.frontend.pytorch.PyTorchOpConverter object at 0x7f090d88aa40>
op_names = {'aten::logical_or'}

    def report_missing_conversion(self, op_names):
        """Check if all ops in an input graph are supported by TVM"""
        known_ops = [
            "prim::Constant",
            "prim::GetAttr",
            "prim::ListConstruct",
            "prim::ListUnpack",
            "prim::TupleConstruct",
            "prim::TupleUnpack",
            "prim::RaiseException",
            "prim::If",
            "prim::Loop",
        ]
        known_ops += list(self.convert_map.keys())
        known_ops += list(qnn_torch.convert_map.keys())
    
        missing = []
    
        for op_name in op_names:
            # Also take care of in-place variant ops like aten::relu_
            if op_name not in known_ops and not (
                op_name.endswith("_") and op_name[:-1] in known_ops
            ):
                missing.append(op_name)
    
        if missing:
            msg = f"The following operators are not implemented: {missing}"
>           raise NotImplementedError(msg)
E           NotImplementedError: The following operators are not implemented: ['aten::logical_or']

../../../../python/tvm/relay/frontend/pytorch.py:4330: NotImplementedError

Environment

os: ubuntu
python: 3.9
pytorch: 2.0
tvm: main branch

Steps to reproduce

def test_logical_or():
    """test_logical_or"""

    def test_fn(x, y):
        return torch.logical_or(x, y)

    a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
    b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
    verify_model(test_fn, [a, b])

    a = torch.tensor([True, False, True])
    b = torch.tensor([True, False, False])
    verify_model(test_fn, [a, b])

Triage

Please refer to the list of label tags here to find the relevant tags and add them below in a bullet format (example below).

• needs-triage
• frontend:pytorch