This is an automated email from the ASF dual-hosted git repository.
anirudh2290 pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 2899715 [MXNET-792] Fix for issue #9816 with dropout operator and RNG
(#12091)
2899715 is described below
commit 2899715921612ef4dd147004292b5b5d0f83320b
Author: Sam Skalicky <[email protected]>
AuthorDate: Mon Aug 20 16:21:22 2018 -0700
[MXNET-792] Fix for issue #9816 with dropout operator and RNG (#12091)
* added mshadow op for threshold_eq (theshold currently does <, this will
do <=)
modified dropout operator to use threshold_eq instead of theshold this will
ensure equivalent behavior for the random numbers generated on CPU [0, 1) and
GPU (0, 1]
removed fixed seed for test_dropout
* removed comment about flaky test
---
src/operator/mshadow_op.h | 1 +
src/operator/nn/dropout-inl.h | 3 ++-
tests/python/unittest/test_operator.py | 4 ++--
3 files changed, 5 insertions(+), 3 deletions(-)
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 3397193..06a223d 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -275,6 +275,7 @@ MXNET_UNARY_MATH_OP(square_grad, 2.0f * math::id(a));
/*! \brief used for generate Bernoulli mask */
MXNET_BINARY_MATH_OP_NC(threshold, a < b ? DType(1) : DType(0));
+MXNET_BINARY_MATH_OP_NC(threshold_eq, a <= b ? DType(1) : DType(0));
/*! \brief used for generate element of abs */
MXNET_UNARY_MATH_OP(abs, math::fabs(a)); // NOLINT(*)
diff --git a/src/operator/nn/dropout-inl.h b/src/operator/nn/dropout-inl.h
index 8e4aac6..b7c40fb 100644
--- a/src/operator/nn/dropout-inl.h
+++ b/src/operator/nn/dropout-inl.h
@@ -206,7 +206,7 @@ class DropoutOp {
const real_t pkeep) {
RNG_KERNEL_LOOP(xpu, DType, id, gen, N, step, {
const real_t rand_num = static_cast<real_t>(genImpl.uniform());
- mask_out[i] = mshadow_op::threshold::Map<real_t>(rand_num, pkeep) *
(1.0f / pkeep);
+ mask_out[i] = mshadow_op::threshold_eq::Map<real_t>(rand_num, pkeep) *
(1.0f / pkeep);
dropout_out[i] = input_data[i] * mask_out[i];
});
}
@@ -258,6 +258,7 @@ class DropoutOp {
this->pkeep_);
return;
}
+
// initialize the mask
LaunchRNG<BernoulliKernel, xpu>(s, pgen, mask.Size(),
mask.dptr<DType>(),
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index 125666b..0ff9a10 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -5722,8 +5722,7 @@ def test_stack():
check_numeric_gradient(out, inputs)
-# test fails with seed 990952066: 0 output seen with dropout ratio=0. See
issue #9816
-@with_seed(1234)
+@with_seed()
def test_dropout():
def zero_count(array, ratio):
zeros = 0
@@ -5775,6 +5774,7 @@ def test_dropout():
exe.arg_arrays[0][:] = 1
exe.forward(is_train=True)
+
if not math.isnan(max_value):
assert exe.outputs[0].asnumpy().max() > 0
else:
