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[mkldnn-v1.0] Skip flaky test for unidirectional rnn_relu #16545

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Oct 24, 2019
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24 changes: 11 additions & 13 deletions tests/python/unittest/test_operator.py
Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@
def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2, atol=1e-4):
if default_context().device_type == 'cpu':
# NOTE(zixuanweeei): Currently, we don't add `add` requests support on fused mkl-dnn rnn operator.
# We tracked this issue by https://github.com/apache/incubator-mxnet/issues/16578
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Currently, we don't add add requests support on fused mkl-dnn rnn operator.

Is this a document to mention this limitation?

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Yes.

if isinstance(grad_req, dict) and 'add' in grad_req.values():
print("Skip the test when requiring `add` operation against gradients on CPU context.")
return
Expand Down Expand Up @@ -257,20 +258,17 @@ def test_rnntanh_bidirectional():
@with_seed()
@assert_raises_cudnn_not_satisfied(min_version='5.1.10')
def test_rnnrelu_sym():
Ts = [1, 5]
Ns = [1, 32]
Is = [32, 128, 512]
Hs = [32, 128, 512]
for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='rnn_relu', get_next_state=True, prefix='')
stack = mx.rnn.SequentialRNNCell()
stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l0_'))
stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l1_'))
stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l2_'))
T, N, I, H = 5, 32, 200, 200

check_rnn_consistency(fused, stack, T, N, I, H, 'write')
check_rnn_consistency(fused, stack, T, N, I, H, 'add')
check_rnn_consistency(fused, stack, T, N, I, H, 'null')
fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='rnn_relu', get_next_state=True, prefix='')
stack = mx.rnn.SequentialRNNCell()
stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l0_'))
stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l1_'))
stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l2_'))

check_rnn_consistency(fused, stack, T, N, I, H, 'write')
check_rnn_consistency(fused, stack, T, N, I, H, 'add')
check_rnn_consistency(fused, stack, T, N, I, H, 'null')

@with_seed()
@assert_raises_cudnn_not_satisfied(min_version='5.1.10')
Expand Down