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new 9933d7a Fix precision issue of test case test_rnnrelu_bidirectional
(#12099)
9933d7a is described below
commit 9933d7a4db256ab89827f8490d725061144c35d6
Author: Hao Li <[email protected]>
AuthorDate: Mon Aug 13 07:21:30 2018 +0800
Fix precision issue of test case test_rnnrelu_bidirectional (#12099)
* adjust tolerance only for relu for fixing test case bug
* only adjust torence for test_rnnrelu_bidirectional and adjust back on
test_rnnrelu_sym
---
tests/python/unittest/test_operator.py | 17 ++++++++---------
1 file changed, 8 insertions(+), 9 deletions(-)
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index 54eb0fd..c395199 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -30,7 +30,7 @@ from mxnet.base import py_str, MXNetError, _as_list
from common import setup_module, with_seed, teardown,
assert_raises_cudnn_disabled, assertRaises
import unittest
-def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req):
+def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2,
atol=1e-4):
dshape = (N, T, I)
data = mx.sym.Variable('data')
@@ -53,18 +53,18 @@ def check_rnn_consistency(cell1, cell2, T, N, I, H,
grad_req):
# check inference
mod1.forward(batch, is_train=False)
mod2.forward(batch, is_train=False)
- assert_allclose(mod1.get_outputs()[0].asnumpy(),
mod2.get_outputs()[0].asnumpy(), rtol=1e-2, atol=1e-4)
+ assert_allclose(mod1.get_outputs()[0].asnumpy(),
mod2.get_outputs()[0].asnumpy(), rtol=rtol, atol=atol)
# check training
mod1.forward(batch, is_train=True)
mod2.forward(batch, is_train=True)
- assert_allclose(mod1.get_outputs()[0].asnumpy(),
mod2.get_outputs()[0].asnumpy(), rtol=1e-2, atol=1e-4)
+ assert_allclose(mod1.get_outputs()[0].asnumpy(),
mod2.get_outputs()[0].asnumpy(), rtol=rtol, atol=atol)
dy = mx.random.uniform(shape=mod1.get_outputs()[0].shape)
mod1.backward(out_grads=[dy])
mod2.backward(out_grads=[dy])
if grad_req != 'null':
- assert_allclose(mod1.get_input_grads()[0].asnumpy(),
mod2.get_input_grads()[0].asnumpy(), rtol=1e-2, atol=1e-4)
+ assert_allclose(mod1.get_input_grads()[0].asnumpy(),
mod2.get_input_grads()[0].asnumpy(), rtol=rtol, atol=atol)
else:
assert(mod1.get_input_grads()[0] == None)
assert(mod2.get_input_grads()[0] == None)
@@ -195,9 +195,8 @@ def test_rnnrelu_sym():
check_rnn_consistency(fused, stack, T, N, I, H, 'add')
check_rnn_consistency(fused, stack, T, N, I, H, 'null')
-
[email protected]("test fails intermittently. temporarily disabled till it gets
fixed. tracked at https://github.com/apache/incubator-mxnet/issues/11410";)
@with_seed()
+@assert_raises_cudnn_disabled()
def test_rnnrelu_bidirectional():
T, N, I, H = 5, 20, 200, 200
@@ -214,9 +213,9 @@ def test_rnnrelu_bidirectional():
mx.rnn.RNNCell(H, activation='relu', prefix='r1_'),
output_prefix='bi_rnnrelu_1_'))
- 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')
+ check_rnn_consistency(fused, stack, T, N, I, H, 'write', rtol=1e-2,
atol=1e-2)
+ check_rnn_consistency(fused, stack, T, N, I, H, 'add', rtol=1e-2,
atol=1e-2)
+ check_rnn_consistency(fused, stack, T, N, I, H, 'null', rtol=1e-2,
atol=1e-2)
@with_seed()
def test_lstm_dropout():
