[Fix core lib warning] Remove LooseVersion (#5026)

* replace LooseVersion with packaging.version

* import packaging from setuptools

* import packaging.version in dgl.utils

* format

* revert changes to apps/kg

* revert changes to _dataloading

python/dgl/backend/mxnet/tensor.py

@@ -3,7 +3,6 @@ from __future__ import absolute_import
 import builtins
 import numbers
 import os
-from distutils.version import LooseVersion
 
 import mxnet as mx
 import mxnet.ndarray as nd
@@ -12,8 +11,9 @@ import numpy as np
 from ... import ndarray as dglnd
 from ..._deprecate import kernel as K
 from ...function.base import TargetCode
+from ...utils import version
 
-if LooseVersion(mx.__version__) < LooseVersion("1.6.0"):
+if version.parse(mx.__version__) < version.parse("1.6.0"):
     raise RuntimeError("DGL requires MXNet >= 1.6")
 
 # After MXNet 1.5, empty tensors aren't supprted by default.

python/dgl/backend/pytorch/tensor.py

@@ -2,7 +2,6 @@ from __future__ import absolute_import
 
 import builtins
 import numbers
-from distutils.version import LooseVersion
 
 import numpy as np
 import scipy  # Weird bug in new pytorch when import scipy after import torch
@@ -12,8 +11,9 @@ from torch.utils import dlpack
 from ... import ndarray as nd
 from ..._deprecate import kernel as K
 from ...function.base import TargetCode
+from ...utils import version
 
-if LooseVersion(th.__version__) < LooseVersion("1.9.0"):
+if version.parse(th.__version__) < version.parse("1.9.0"):
     raise RuntimeError("DGL requires PyTorch >= 1.9.0")
 
 
@@ -425,7 +425,7 @@ def zerocopy_from_numpy(np_array):
     return th.as_tensor(np_array)
 
 
-if LooseVersion(th.__version__) >= LooseVersion("1.10.0"):
+if version.parse(th.__version__) >= version.parse("1.10.0"):
 
     def zerocopy_to_dgl_ndarray(data):
         if data.dtype == th.bool:

python/dgl/backend/tensorflow/tensor.py

@@ -3,7 +3,6 @@ from __future__ import absolute_import
 
 import builtins
 import numbers
-from distutils.version import LooseVersion
 
 import numpy as np
 import tensorflow as tf
@@ -11,8 +10,9 @@ import tensorflow as tf
 from ... import ndarray as nd
 from ..._deprecate import kernel as K
 from ...function.base import TargetCode
+from ...utils import version
 
-if LooseVersion(tf.__version__) < LooseVersion("2.3.0"):
+if version.parse(tf.__version__) < version.parse("2.3.0"):
     raise RuntimeError(
         "DGL requires TensorFlow>=2.3.0 for the official DLPack support."
     )

python/dgl/dataloading/dataloader.py

@@ -3,7 +3,6 @@ from collections.abc import Mapping, Sequence
 from queue import Queue, Empty, Full
 import itertools
 import threading
-from distutils.version import LooseVersion
 import math
 import inspect
 import re
@@ -23,7 +22,7 @@ from .._ffi.base import is_tensor_adaptor_enabled
 from ..heterograph import DGLGraph
 from ..utils import (
     recursive_apply, ExceptionWrapper, recursive_apply_pair, set_num_threads, get_num_threads,
-    get_numa_nodes_cores, context_of, dtype_of)
+    get_numa_nodes_cores, context_of, dtype_of, version)
 from ..frame import LazyFeature
 from ..storages import wrap_storage
 from .base import BlockSampler, as_edge_prediction_sampler
@@ -31,7 +30,7 @@ from .. import backend as F
 from ..distributed import DistGraph
 from ..multiprocessing import call_once_and_share
 
-PYTORCH_VER = LooseVersion(torch.__version__)
+PYTORCH_VER = version.parse(torch.__version__)
 PYTHON_EXIT_STATUS = False
 def _set_python_exit_flag():
     global PYTHON_EXIT_STATUS
@@ -76,7 +75,7 @@ class _TensorizedDatasetIter(object):
         # convert the type-ID pairs to dictionary
         type_ids = batch[:, 0]
         indices = batch[:, 1]
-        if PYTORCH_VER >= LooseVersion("1.10.0"):
+        if PYTORCH_VER >= version.parse("1.10.0"):
             _, type_ids_sortidx = torch.sort(type_ids, stable=True)
         else:
             if not self.shuffle:

python/dgl/utils/__init__.py

@@ -6,3 +6,9 @@ from .filter import *
 from .internal import *
 from .pin_memory import *
 from .shared_mem import *
+
+try:
+    from packaging import version
+except ImportError:
+    # If packaging isn't installed, try and use the vendored copy in setuptools
+    from setuptools.extern.packaging import version

tests/compute/test_sparse.py

-from distutils.version import LooseVersion
 import random
 import unittest
 
@@ -11,6 +10,7 @@ from test_utils.graph_cases import get_cases
 
 import dgl
 from dgl.ops import edge_softmax, gsddmm, gspmm, segment_reduce
+from dgl.utils import version
 
 random.seed(42)
 np.random.seed(42)
@@ -189,7 +189,7 @@ def test_spmm(idtype, g, shp, msg, reducer):
     [(torch.float16, 1e-3, 0.5), (torch.bfloat16, 4e-3, 2.)]
 )
 def test_half_spmm(idtype, dtype, rtol, atol):
-    if LooseVersion(torch.version.cuda) < LooseVersion("11.0") \
+    if version.parse(torch.version.cuda) < version.parse("11.0") \
         and dtype == torch.bfloat16:
         pytest.skip("BF16 requires CUDA >= 11.0.")
 
@@ -373,7 +373,7 @@ def test_segment_mm(idtype, feat_size, dtype, tol):
             "Only support float32 and float64 on CPU."
         )
     if F._default_context_str == "gpu" \
-        and LooseVersion(torch.version.cuda) < LooseVersion("11.0") \
+        and version.parse(torch.version.cuda) < version.parse("11.0") \
         and dtype == torch.bfloat16:
         pytest.skip(
             "BF16 requires CUDA >= 11.0."
@@ -426,7 +426,7 @@ def test_gather_mm_idx_b(feat_size, dtype, tol):
     if F._default_context_str == "cpu" and dtype in (torch.float16, torch.bfloat16):
         pytest.skip("Only support float32 and float64 on CPU.")
     if F._default_context_str == "gpu" \
-        and LooseVersion(torch.version.cuda) < LooseVersion("11.0") \
+        and version.parse(torch.version.cuda) < version.parse("11.0") \
         and dtype == torch.bfloat16:
         pytest.skip("BF16 requires CUDA >= 11.0.")