Revert "[Submodule] Upgrade to oneDNN v1.6.3"

[leezu]

Tests if #19153 introduced the gcc8 incompatibility

[2020-09-17T17:48:04.979Z] ______________________ test_dc_hybridblock_deferred_init _______________________
[2020-09-17T17:48:04.979Z] [gw0] linux -- Python 3.6.9 /opt/rh/rh-python36/root/usr/bin/python3
[2020-09-17T17:48:04.979Z] 
[2020-09-17T17:48:04.979Z]     def test_dc_hybridblock_deferred_init():
[2020-09-17T17:48:04.979Z]         class MyBlock(mx.gluon.HybridBlock):
[2020-09-17T17:48:04.979Z]             def __init__(self):
[2020-09-17T17:48:04.979Z]                 super().__init__()
[2020-09-17T17:48:04.979Z]                 self.dense = mx.gluon.nn.Dense(units=10)
[2020-09-17T17:48:04.979Z]                 self.weight = mx.gluon.Parameter('weight', allow_deferred_init=True)
[2020-09-17T17:48:04.979Z]     
[2020-09-17T17:48:04.979Z]             def infer_shape(self, x):
[2020-09-17T17:48:04.979Z]                 self.weight.shape = (x.shape[1], )
[2020-09-17T17:48:04.979Z]     
[2020-09-17T17:48:04.979Z]             def forward(self, x):
[2020-09-17T17:48:04.979Z]                 return self.dense(x) + self.weight.data(x.context)
[2020-09-17T17:48:04.979Z]     
[2020-09-17T17:48:04.979Z]         net = MyBlock()
[2020-09-17T17:48:04.979Z]         net.initialize()
[2020-09-17T17:48:04.979Z] >       _assert_dc_gluon(_dc_gluon_simple_setup, net, numpy=False)
[2020-09-17T17:48:04.979Z] 
[2020-09-17T17:48:04.979Z] tests/python/unittest/test_deferred_compute.py:504: 
[2020-09-17T17:48:04.979Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
[2020-09-17T17:48:04.979Z] tests/python/unittest/test_deferred_compute.py:421: in _assert_dc_gluon
[2020-09-17T17:48:04.979Z]     _all_same(ys_np, ys_hybrid_np)
[2020-09-17T17:48:04.979Z] _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
[2020-09-17T17:48:04.979Z] 
[2020-09-17T17:48:04.979Z] arrays1 = [array([        nan,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z]         0.07460972, -0.08127148, -0.32424796,...33878, -0.10624887,
[2020-09-17T17:48:04.979Z]         0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z]       dtype=float32), ...]
[2020-09-17T17:48:04.979Z] arrays2 = [array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z]         0.07460972, -0.08127148, -0.32424796,...33878, -0.10624887,
[2020-09-17T17:48:04.979Z]         0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z]       dtype=float32), ...]
[2020-09-17T17:48:04.979Z] message = ''
[2020-09-17T17:48:04.979Z] 
[2020-09-17T17:48:04.979Z]     def _all_same(arrays1, arrays2, message=''):
[2020-09-17T17:48:04.979Z]         same = all(np.array_equal(a1, a2) for a1, a2 in zip(arrays1, arrays2))
[2020-09-17T17:48:04.979Z]         if not same:
[2020-09-17T17:48:04.979Z] >           raise AssertionError('Arrays not equal ({}):\n{}\n\n{}'.format(message, arrays1, arrays2))
[2020-09-17T17:48:04.979Z] E           AssertionError: Arrays not equal ():
[2020-09-17T17:48:04.979Z] E           [array([        nan,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([        nan,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([        nan,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32)]
[2020-09-17T17:48:04.979Z] E           
[2020-09-17T17:48:04.979Z] E           [array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32), array([ 0.01286458,  0.2107217 , -0.06851891,  0.16233878, -0.10624887,
[2020-09-17T17:48:04.979Z] E                   0.07460972, -0.08127148, -0.32424796, -0.0124862 , -0.1862593 ],
[2020-09-17T17:48:04.979Z] E                 dtype=float32)]

[mxnet-bot]

Hey @leezu , Thanks for submitting the PR
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[leezu]

@mxnet-bot run ci [windows-gpu]

[mxnet-bot]

Jenkins CI successfully triggered : [windows-gpu]

[leezu]

Here is a screenshot that shows that the build passes when OneDNN update is reverted: