27 tests fail

[yurivict]

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Versions

torch-geometric-2.6.0
pytorch-2.4.0
Python-3.11
FreeBSD 14.1

[yurivict]

2.6.1 has: 17 failed, 5452 passed, 986 skipped, 12 warnings in 330.89s (0:05:30)

_____________________________________________________________________________ test_batching_with_new_dimension ______________________________________________________________________________

    def test_batching_with_new_dimension():
        torch_geometric.set_debug(True)
    
        class MyData(Data):
            def __cat_dim__(self, key, value, *args, **kwargs):
                if key == 'foo':
                    return None
                else:
                    return super().__cat_dim__(key, value, *args, **kwargs)
    
        x1 = torch.tensor([1, 2, 3], dtype=torch.float)
        foo1 = torch.randn(4)
        y1 = torch.tensor(1)
    
        x2 = torch.tensor([1, 2], dtype=torch.float)
        foo2 = torch.randn(4)
        y2 = torch.tensor(2)
    
        batch = Batch.from_data_list(
            [MyData(x=x1, foo=foo1, y=y1),
             MyData(x=x2, foo=foo2, y=y2)])
    
        assert str(batch) == ('MyDataBatch(x=[5], y=[2], foo=[2, 4], batch=[5], '
                              'ptr=[3])')
        assert batch.num_graphs == len(batch) == 2
        assert batch.x.tolist() == [1, 2, 3, 1, 2]
        assert batch.foo.size() == (2, 4)
        assert batch.foo[0].tolist() == foo1.tolist()
        assert batch.foo[1].tolist() == foo2.tolist()
        assert batch.y.tolist() == [1, 2]
        assert batch.batch.tolist() == [0, 0, 0, 1, 1]
        assert batch.ptr.tolist() == [0, 3, 5]
        assert batch.num_graphs == 2
    
        data = batch[0]
>       assert str(data) == ('MyData(x=[3], y=[1], foo=[4])')
E       AssertionError: assert 'MyData(x=[3]..., foo=[1, 4])' == 'MyData(x=[3]...[1], foo=[4])'
E         
E         - MyData(x=[3], y=[1], foo=[4])
E         + MyData(x=[3], y=[1], foo=[1, 4])
E         ?                           +++

test/data/test_batch.py:314: AssertionError
===================================================================================== warnings summary ======================================================================================

_____________________________________________________________________________ test_batching_with_new_dimension ______________________________________________________________________________

    def test_batching_with_new_dimension():
        torch_geometric.set_debug(True)
    
        class MyData(Data):
            def __cat_dim__(self, key, value, *args, **kwargs):
                if key == 'foo':
                    return None
                else:
                    return super().__cat_dim__(key, value, *args, **kwargs)
    
        x1 = torch.tensor([1, 2, 3], dtype=torch.float)
        foo1 = torch.randn(4)
        y1 = torch.tensor(1)
    
        x2 = torch.tensor([1, 2], dtype=torch.float)
        foo2 = torch.randn(4)
        y2 = torch.tensor(2)
    
        batch = Batch.from_data_list(
            [MyData(x=x1, foo=foo1, y=y1),
             MyData(x=x2, foo=foo2, y=y2)])
    
        assert str(batch) == ('MyDataBatch(x=[5], y=[2], foo=[2, 4], batch=[5], '
                              'ptr=[3])')
        assert batch.num_graphs == len(batch) == 2
        assert batch.x.tolist() == [1, 2, 3, 1, 2]
        assert batch.foo.size() == (2, 4)
        assert batch.foo[0].tolist() == foo1.tolist()
        assert batch.foo[1].tolist() == foo2.tolist()
        assert batch.y.tolist() == [1, 2]
        assert batch.batch.tolist() == [0, 0, 0, 1, 1]
        assert batch.ptr.tolist() == [0, 3, 5]
        assert batch.num_graphs == 2
    
        data = batch[0]
>       assert str(data) == ('MyData(x=[3], y=[1], foo=[4])')
E       AssertionError: assert 'MyData(x=[3]..., foo=[1, 4])' == 'MyData(x=[3]...[1], foo=[4])'
E         
E         - MyData(x=[3], y=[1], foo=[4])
E         + MyData(x=[3], y=[1], foo=[1, 4])
E         ?                           +++

test/data/test_batch.py:314: AssertionError
===================================================================================== warnings summary ======================================================================================


_________________________________________________________________________ test_traceable_my_conv_with_self_loops[0] _________________________________________________________________________

num_nodes = 0

    @pytest.mark.parametrize('num_nodes', [4, 8, 2, 0])
    def test_traceable_my_conv_with_self_loops(num_nodes):
        # `torch.jit.trace` a `MessagePassing` layer that adds self loops and test
        # it across different input sizes.
        x = torch.randn(4, 16)
        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])
    
        conv = MyConvWithSelfLoops()
>       traced_conv = torch.jit.trace(conv, ((x, edge_index)))

test/nn/conv/test_message_passing.py:713: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/local/lib/python3.11/site-packages/torch/jit/_trace.py:1000: in trace
    traced_func = _trace_impl(
/usr/local/lib/python3.11/site-packages/torch/jit/_trace.py:695: in _trace_impl
    return trace_module(
/usr/local/lib/python3.11/site-packages/torch/jit/_trace.py:1303: in trace_module
    _check_trace(
/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py:116: in decorate_context
    return func(*args, **kwargs)
/usr/local/lib/python3.11/site-packages/torch/jit/_trace.py:592: in _check_trace
    diag_info = graph_diagnostic_info()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

    def graph_diagnostic_info():
        mod_canonicalized = torch._C._jit_pass_canonicalize(traced_func.graph)
        torch._C._jit_pass_inline(mod_canonicalized)
        torch._C._jit_pass_erase_shape_information(mod_canonicalized)
        mod_str = str(mod_canonicalized)
        mod_str = re.sub(r"___torch_mangle_[0-9]+\.", "", mod_str)
        check_canonicalized = torch._C._jit_pass_canonicalize(check_mod_func.graph)
        torch._C._jit_pass_inline(check_canonicalized)
        torch._C._jit_pass_erase_shape_information(check_canonicalized)
        check_str = str(check_canonicalized)
        check_str = re.sub(r"___torch_mangle_[0-9]+\.", "", check_str)
    
        graph_diff_errors = None
        if mod_str != check_str:
            import difflib
    
            graph_diff = difflib.ndiff(
                mod_str.splitlines(True), check_str.splitlines(True)
            )
            graph_diff_errors = "Graph diff:\n" + indent("".join(graph_diff)) + "\n"
    
            for n_mod, n_check in zip(
                mod_canonicalized.nodes(), check_canonicalized.nodes()
            ):
                if str(n_mod) != str(n_check):
                    graph_diff_errors += "First diverging operator:\n"
                    node_diff = difflib.ndiff(
                        str(n_mod).splitlines(True), str(n_check).splitlines(True)
                    )
                    source_printout = (
                        "Node diff:\n" + indent("".join(node_diff)) + "\n"
                    )
                    mod_stack = n_mod.sourceRange()
                    if mod_stack:
                        source_printout += (
                            "Trace source location:\n" + indent(mod_stack) + "\n"
                        )
                    check_stack = n_check.sourceRange()
                    if check_stack:
                        source_printout += (
                            "Check source location:\n" + indent(check_stack) + "\n"
                        )
                    graph_diff_errors += source_printout
    
                    break  # For now, only print out the first pair of nodes that diverges
    
        tensor_compare_errors = None
        # Check Tensor-valued constant nodes
        for n_mod, n_check in zip(
            mod_canonicalized.nodes(), check_canonicalized.nodes()
        ):
            if n_mod.kind() != n_check.kind():
                break  # Graphs have already diverged
    
            if n_mod.kind() == "prim::Constant" and not (
                n_mod.mustBeNone() or n_check.mustBeNone()
            ):
                if not n_mod.hasAttribute("value"):
                    continue
                if n_mod.kindOf("value") != "t" or n_check.kindOf("value") != "t":
                    continue
    
>               mod_tensor_val = n_mod.t("value")
E               RuntimeError: required keyword attribute 'value' has the wrong type

/usr/local/lib/python3.11/site-packages/torch/jit/_trace.py:447: RuntimeError