wkcn commented on a change in pull request #18644:
URL: https://github.com/apache/incubator-mxnet/pull/18644#discussion_r447742251
##########
File path: tests/python/unittest/test_gluon.py
##########
@@ -665,6 +665,34 @@ def transpose(shape):
assert (layer(x).shape==ceil_out_shape)
+@with_seed()
+@pytest.mark.parametrize('cudnn_off', [True, False])
+@pytest.mark.parametrize('variable', ['running_var', 'running_mean'])
+def test_batchnorm_backward_synchronization(cudnn_off, variable):
+ """
+ Tests if synchronization of BatchNorm running variables is done correctly.
+ If not, the test sometimes fails - depending on the timing.
+ """
+ ctx = mx.cpu() if cudnn_off else mx.gpu()
+ read_op = 'layer.' + variable + '.data().asnumpy()'
+
+ for _ in range(20):
+ layer = nn.BatchNorm()
+ layer.initialize(ctx=ctx)
+ for _ in range(3):
+ data = mx.nd.random.normal(loc=10, scale=2, shape=(1, 3, 10, 10),
ctx=ctx)
+ with mx.autograd.record():
+ out = layer(data)
+ out.backward()
+
+ # check if each read give the same value
+ var1 = eval(read_op)
Review comment:
Thank you for the contribution!
For safety, I suggest to use `var1 = getattr(layer,
variable).data().asnumpy()`.
##########
File path: tests/python/unittest/test_gluon.py
##########
@@ -665,6 +665,34 @@ def transpose(shape):
assert (layer(x).shape==ceil_out_shape)
+@with_seed()
+@pytest.mark.parametrize('cudnn_off', [True, False])
+@pytest.mark.parametrize('variable', ['running_var', 'running_mean'])
+def test_batchnorm_backward_synchronization(cudnn_off, variable):
+ """
+ Tests if synchronization of BatchNorm running variables is done correctly.
+ If not, the test sometimes fails - depending on the timing.
+ """
+ ctx = mx.cpu() if cudnn_off else mx.gpu()
+ read_op = 'layer.' + variable + '.data().asnumpy()'
+
+ for _ in range(20):
+ layer = nn.BatchNorm()
+ layer.initialize(ctx=ctx)
+ for _ in range(3):
+ data = mx.nd.random.normal(loc=10, scale=2, shape=(1, 3, 10, 10),
ctx=ctx)
+ with mx.autograd.record():
+ out = layer(data)
+ out.backward()
+
+ # check if each read give the same value
+ var1 = eval(read_op)
+ for _ in range(10):
+ var2 = eval(read_op)
Review comment:
`var2 = getattr(layer, variable).data().asnumpy()`.
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