Fall back when sparse arrays are passed to MKLDNN-enabled operators

[luobao-intel]

Description

Currently, the MKLDNN-enabled operators, such as convolution and pooling, can't handle sparse arrays correctly. The reason is that the storage inference of these operators doesn't return the right dispatch mode.#11448

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

• Activation
• Convolution
• Deconvolution
• LRN
• Pooling
• Softmax

Comments

• If this change is a backward incompatible change, why must this change be made.
• Interesting edge cases to note here

@pengzhao-intel @xinyu-intel

[zheng-da]

Thanks. Is there any reason that you can't use MKLDNNStorageType?

[pengzhao-intel]

@zheng-da Sorry, this is a mistake to PR into official repo. Please ignore this one and @luobao-intel will submit the final change soon after the internal review.