[GitHub] [incubator-mxnet] sxjscience commented on a change in pull request #19033: Numpy RNN operator large dim checks

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

sxjscience commented on a change in pull request #19033:
URL: https://github.com/apache/incubator-mxnet/pull/19033#discussion_r480326711



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File path: tests/nightly/test_np_large_array.py
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@@ -875,34 +875,65 @@ def test_digamma():
                 rtol=1e-3, atol=1e-5)
 
 @use_np
-@pytest.mark.skip(reason='broken on large tensors; also backward errors out')
-def test_rnn():
+def test_rnn_dim_check():
+    L_SEQ, BAT, L_INP, L_STA = 2**31, 4, 2**10, 2
+    data = np.random.uniform(-1, 1, (L_SEQ, BAT, L_INP))

Review comment:
       You can consider to revise it like what I have suggested. You generate 20 random numbers from uniform(-1, 1) and create a small array to store these numbers and cache it in the python script. Then, you cyclically use these numbers to construct the large tensor.




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