access2rohit commented on a change in pull request #15048: [MXNET-1408] Adding
test to verify Large Tensor Support for ravel and unravel
URL: https://github.com/apache/incubator-mxnet/pull/15048#discussion_r288205769
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File path: tests/nightly/test_large_array.py
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@@ -279,6 +279,19 @@ def test_diag():
assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+def test_ravel_and_unravel():
+ idxs_2d = [[LARGE_X-1,LARGE_X-100,6],[SMALL_Y-1,SMALL_Y-10,1]]
Review comment:
@marcoabreu : We have to test cases where we have to allocate large tensors
on actual physical memory. That is the whole purpose of adding these tests.
Without which we cannot support or even guarantee large dense tensor support on
MXNet.
The purpose of these tests are not at all to test only Sparse. Currently we
are not addressing performance issues with large dense arrays but trying to
support them. From your reply it seems like you are under the impression that
everything should work fine if tensor size >2^32 elements(Please correct me if
I am wrong). But that's not the case and the operator simply segfaults or gives
unpredictable behavior. That is why is necessary to test them with such large
arrays/tensors.
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