[Bugfix] Fix layer norm for large input shape

[sxjscience]

Description

The previous implementation of LayerNorm is incorrect because it wrongly calculated the size of the reduction workspace.

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant JIRA issue created (except PRs with tiny changes)
• Changes are complete (i.e. I finished coding on this PR)
• All changes have test coverage:
• Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
• Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
• Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
• Code is well-documented:
• For user-facing API changes, API doc string has been updated.
• For new C++ functions in header files, their functionalities and arguments are documented.
• For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
• Check the API doc at http:https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$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

• Fix LayerNorm bug, test

Comments

MXNET_TEST_COUNT=1000000 nosetests tests/python/gpu/test_operator_gpu.py:test_layer_norm
/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
[INFO] Setting module np/mx/python random seeds, use MXNET_MODULE_SEED=80957650 to reproduce.
.
----------------------------------------------------------------------
Ran 1 test in 42.995s

OK

[sxjscience]

@eric-haibin-lin @szha

[anirudhacharya]

@mxnet-label-bot add [pr-awaiting-review]

[wkcn]

LGTM. Thank you for the fix!