[GitHub] sandeep-krishnamurthy closed pull request #12099: Fix precision issue of test case test_rnnrelu_bidirectional

[GitBox <gi...@apache.org>]

sandeep-krishnamurthy closed pull request #12099: Fix precision issue of test case test_rnnrelu_bidirectional
URL: https://github.com/apache/incubator-mxnet/pull/12099
 
 
   

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diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 90e85d123d5..a33b882edfa 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -30,7 +30,7 @@
 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')
 
-
-@unittest.skip("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():


 

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