[GitHub] pengzhao-intel commented on a change in pull request #13328: adding test for mkldnn softmax operator for large negative inputs

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

pengzhao-intel commented on a change in pull request #13328: adding test for mkldnn softmax operator for large negative inputs
URL: https://github.com/apache/incubator-mxnet/pull/13328#discussion_r234864191
 
 

 ##########
 File path: tests/python/mkl/test_mkldnn.py
 ##########
 @@ -383,6 +383,17 @@ def check_fullyconnected_training(stype):
     for stype in stypes:
         check_fullyconnected_training(stype)
 
+def test_softmax_with_large_negative_inputs():
+    input_data = mx.nd.array([[[[-1e30,-1e30]]]])
+    data = mx.sym.Variable('data')
+    out1 = data.softmax(axis=1)
+    exec1 = out1.bind(mx.cpu(), args={'data': input_data, 'softmax_label': mx.nd.ones([1]),
+                                      'fc_weight': mx.nd.ones([2,2]), 'fc1_weight': mx.nd.ones([2,2])})
+    exec1.forward()[0].wait_to_read()
+    ndarr = exec1.outputs[0][0][0][0]
+    nparr = ndarr.asnumpy()
+    assert(np.isnan(nparr).any(), False)
 
 Review comment:
   Could you build other corner cases to cover more, such as 1e-30 as input and other possible combinations?

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