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[Flaky test] Skip test_operator_gpu.test_convolution_independent_gradients #15631

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Jul 26, 2019
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[Flaky test] Skip test_operator_gpu.test_convolution_independent_gradients #15631

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3acac91
Skip test_convolution_independent_gradirents
zixuanweeei Jul 23, 2019
9a544d0
Add an issue link
zixuanweeei Jul 23, 2019
72ee05d
Fix inconsistent context of input array and binding op
zixuanweeei Jul 24, 2019
c1216a6
Trigger CI
zixuanweeei Jul 25, 2019
0835392
Merge master and trigger CI
zixuanweeei Jul 25, 2019
2358dd2
Retrigger CI
zixuanweeei Jul 25, 2019
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28 changes: 15 additions & 13 deletions tests/python/unittest/test_operator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2001,10 +2001,12 @@ def test_depthwise_convolution():

@with_seed()
def test_convolution_independent_gradients():
ctx = default_context()
# set a low bar for autotuned cudnn conv
atol = 1.0e-1 if ctx.device_type == "gpu" else 1.0e-3
rtol = 1.0e-2 if ctx.device_type == "gpu" else 1.0e-3
# NOTE(zixuanweeei): Flaky test tracked by https://github.com/apache/incubator-mxnet/issues/15603.
# GPU context will be enabled after figuring out the possible issue tracked at
# https://github.com/apache/incubator-mxnet/issues/15638.
ctx = mx.cpu()
atol = 1.0e-3
rtol = 1.0e-3
reqs = ["null", "write", "add"]
var_names = ["x", "w", "b"]
dims = [1, 2]
Expand Down Expand Up @@ -2034,14 +2036,14 @@ def test_convolution_independent_gradients():
for req_kind in reqs:
# Binding args for conv with possible dependent gradients
base_args = {
'x': mx.nd.random.normal(shape=x_shape),
'w': mx.nd.random.normal(shape=w_shape),
'b': mx.nd.random.normal(shape=(num_filter, )) if not no_bias else None}
'x': mx.nd.random.normal(shape=x_shape, ctx=ctx),
'w': mx.nd.random.normal(shape=w_shape, ctx=ctx),
'b': mx.nd.random.normal(shape=(num_filter, ), ctx=ctx) if not no_bias else None}
args1 = copy.deepcopy(base_args)
grad1 = {
'x': mx.nd.zeros(shape=x_shape),
'w': mx.nd.zeros(shape=w_shape),
'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
'x': mx.nd.zeros(shape=x_shape, ctx=ctx),
'w': mx.nd.zeros(shape=w_shape, ctx=ctx),
'b': mx.nd.zeros(shape=(num_filter, ), ctx=ctx) if not no_bias else None}

grad_req1 = [req_kind] * 3
grad_req1 = dict(zip(var_names, grad_req1))
Expand All @@ -2054,9 +2056,9 @@ def test_convolution_independent_gradients():
# Binding args for conv with independent gradients
args2 = copy.deepcopy(base_args) # Deepcopy the same params of `exe1`
grad2 = {
'x': mx.nd.zeros(shape=x_shape),
'w': mx.nd.zeros(shape=w_shape),
'b': mx.nd.zeros(shape=(num_filter, )) if not no_bias else None}
'x': mx.nd.zeros(shape=x_shape, ctx=ctx),
'w': mx.nd.zeros(shape=w_shape, ctx=ctx),
'b': mx.nd.zeros(shape=(num_filter, ), ctx=ctx) if not no_bias else None}
grad_req2 = {"x": x_req, "w": w_req, "b": b_req}
exe2 = conv.bind(ctx, args2, args_grad=grad2, grad_req=grad_req2)

Expand Down