[MKLDNN] Support quantized rnn towards v1.6.x

[zixuanweeei]

Description

Mirror PR of #18001, towards v1.6.x branch. In this PR, we add support of quantization flow of the rnn operator. Currently, only the LSTM mode supports INT8 inference.

Checklist

Essentials

Please feel free to remove inapplicable items for your PR.

• 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 https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
• To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

• Add _contrib_quantized_rnn op.
• Add asymmetric quantization - _contrib_quantized_asym op, to quantize FP32 data to U8 data using scale and shift.
• Add MXNET_USE_WEIGHT_CACHE to control rnn init behavior.
• Support data layout in NDArrayIter. Specifically, NDArrayIter supports only NCHW layout by default, and there is no way to support other layouts, like sequential TNC layout. This PR makes some changes to NDArrayIter to leverage the feature (assuming that N represents the batch).
• Move MKLDNNRnnMemMgr to individual layer.

##Comments##

• Performance results will be added to [MKLDNN] Support quantized rnn #18001.

@ciyongch @TaoLv @pengzhao-intel

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[zixuanweeei]

@mxnet-label-bot add [mkldnn]

[zixuanweeei]

Accuracy and performance: #18001 (comment)

[zixuanweeei]

CI checks passed. Please take a review. @ciyongch @TaoLv @pengzhao-intel

[ciyongch]

LGTM for the current implementation, but we still need another DNNL patch (or DNNL version upgrade) to mitigate the overhead of _contrib_quantize_asym operator.

[zixuanweeei]

LGTM for the current implementation, but we still need another DNNL patch (or DNNL version upgrade) to mitigate the overhead of _contrib_quantize_asym operator.

Sure, let's wait for the patch.

[pengzhao-intel]

LGTM for the current implementation, but we still need another DNNL patch (or DNNL version upgrade) to mitigate the overhead of _contrib_quantize_asym operator.

Thanks, we can upgrade DNNL a little later and I will merge this PR for early testing.

[pengzhao-intel]

LGTM