Improvement to the `test_spot_target_excludes` test. #6652

drivanov · 2023-12-01T00:38:56Z

Description

The main goal of these changes is the elimination of the following 15 warnings:

The main purpose of these changes is to address the following 15 warnings:
  /usr/local/lib/python3.10/dist-packages/dgl/dataloading/dataloader.py:1149: DGLWarning: Dataloader CPU affinity opt is not enabled, consider switching it on (see enable_cpu_affinity() or CPU best practices for DGL [https://docs.dgl.ai/tutorials/cpu/cpu_best_practises.html])
    dgl_warning(

generated by the test_spot_target_excludes test. In addition to this, we have extended the list of variable input parameters with num_workers with values of 1 and 4.

Checklist

Please feel free to remove inapplicable items for your PR.

I've leverage the tools to beautify the python and c++ code.
The PR is complete and small, read the Google eng practice (CL equals to PR) to understand more about small PR. In DGL, we consider PRs with less than 200 lines of core code change are small (example, test and documentation could be exempted).
To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

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frozenbugs · 2024-02-19T02:10:15Z

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frozenbugs · 2024-02-19T02:12:23Z

@drivanov

================================== FAILURES ===================================
______________________ test_spot_target_excludes[1-1-1] _______________________

degree_threshold = 1, batch_size = 1, num_workers = 1

    @pytest.mark.parametrize("degree_threshold", [1, 2, 3, 4, 5])
    @pytest.mark.parametrize("batch_size", [1, 10, 50])
    @pytest.mark.parametrize("num_workers", [1, 4])
    def test_spot_target_excludes(degree_threshold, batch_size, num_workers):
        g, reverse_eids, seed_edges = _create_homogeneous()
        sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
        low_degree_excluder = dgl.dataloading.SpotTarget(
            g,
            exclude="reverse_id",
            degree_threshold=degree_threshold,
            reverse_eids=reverse_eids,
        )
        sampler = dgl.dataloading.as_edge_prediction_sampler(
            sampler,
            exclude=low_degree_excluder,
            negative_sampler=dgl.dataloading.negative_sampler.Uniform(1),
        )
        dataloader = dgl.dataloading.DataLoader(
            g,
            seed_edges,
            sampler,
            batch_size=batch_size,
            num_workers=num_workers,
        )
    
        with dataloader.enable_cpu_affinity():
            for i, (input_nodes, pair_graph, neg_pair_graph, blocks) in enumerate(
&gt;               dataloader
            ):

tests\python\pytorch\dataloading\test_spot_target.py:58: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
python\dgl\dataloading\dataloader.py:1160: in __iter__
    self, super().__iter__(), num_threads=num_threads
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py:435: in __iter__
    return self._get_iterator()
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py:381: in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\utils\data\dataloader.py:1034: in __init__
    w.start()
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\process.py:112: in start
    self._popen = self._Popen(self)
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py:223: in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\context.py:322: in _Popen
    return Popen(process_obj)
C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\popen_spawn_win32.py:89: in __init__
    reduction.dump(process_obj, to_child)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = &lt;Process(Process-117, initial daemon)&gt;
file = &lt;_io.BufferedWriter name=11&gt;, protocol = None

    def dump(obj, file, protocol=None):
        '''Replacement for pickle.dump() using ForkingPickler.'''
&gt;       ForkingPickler(file, protocol).dump(obj)
E       AttributeError: Can't pickle local object 'DataLoader.enable_cpu_affinity.&lt;locals&gt;.init_fn'

C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\reduction.py:60: AttributeError
---------------------------- Captured stdout call -----------------------------
1 DL workers are assigned to cpus [0], main process will use cpus [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
-------------------------- Captured stderr teardown ---------------------------
Traceback (most recent call last):

  File "&lt;string&gt;", line 1, in &lt;module&gt;

  File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main

    exitcode = _main(fd)

  File "C:\Users\Administrator\AppData\Local\Programs\Python\Python37\lib\multiprocessing\spawn.py", line 115, in _main

    self = reduction.pickle.load(from_parent)

EOFError: Ran out of input

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drivanov · 2024-02-20T19:45:44Z

@frozenbugs : Unfortunately, I am unable to reproduce the problem you mentioned in my Linux environment. This appears to be some kind of multi-threading bug that only appears on Windows. Similar problems were reported for PR#6187 and PR#6194 where I suggested similar fixes. I'll convert this to "Draft".

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Improvement to the test_spot_target_excludes test.

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Fixing lint problems.

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drivanov marked this pull request as draft February 20, 2024 19:47

Improvement to the test_spot_target_excludes test. #6652

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Improvement to the test_spot_target_excludes test. #6652

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drivanov commented Dec 1, 2023

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frozenbugs commented Feb 19, 2024 • edited Loading

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Improvement to the `test_spot_target_excludes` test. #6652

Improvement to the `test_spot_target_excludes` test. #6652

frozenbugs commented Feb 19, 2024 •

edited

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