Description

This PR introduces more test cases to cover the gluon computation on MKLDNN with ndarray related memory operations, such as slice, reshape etc. I am also implementing more test cases covering more gluon computations involving MKLDNN (such as BN, pooling etc.), and will be reflected in standalone PR(s) accordingly.

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage:
• Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
• Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
• Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

All the changes is reflected by tests/python/mkl/test_mkldnn.py

Comments

1. For the correctness check on gluon computation, it follows the design used by tests/python/unittest/test_gluon.py, and therefore, the helper functions defined in tests/python/unitest/common.py is also used.
2. A minor change on the existing test case in same source file is just addressing the additional module import supporting the newly added test cases.