Fix reshape to add in-place back

[TaoLv]

Description

Previously in-place option was removed when build with MKL-DNN. It makes reshape operator taking much time in mxnet-mkl package. This PR adds the in-pace option back and uses a temporal buffer for reordering when the input is a MKL-DNN layout.

Besides, this PR also adds a unit test from #14766

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

• Feature1, tests, (and when applicable, API doc)
• Feature2, tests, (and when applicable, API doc)

Comments

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

[pengzhao-intel]

@TaoLv please share the performance data too

[TaoLv]

I use the code snippet below for benchmarking. Transpose is added before and after reshape operator in order to make it in-placed.

import time
import mxnet as mx
from mxnet import Context

data = mx.symbol.Variable('data')
res = mx.symbol.transpose(data=data, axes=(1, 0, 2, 3))
res = mx.symbol.reshape(data=res, shape=(-1, 16, 16, 16))
res = mx.symbol.transpose(data=res, axes=(1, 0, 2, 3))

shape = (64, 4, 16, 16)
exe = res.simple_bind(Context.default_ctx, data=shape, grad_req='null')


tic = 0
for i in range(205):
    if i == 5:
        mx.profiler.set_config(profile_symbolic=True, profile_imperative=True, profile_api=False, profile_memory=False, aggregate_stats=True, filename='profile_output.json')
        mx.profiler.set_state('run')
        tic = time.time()
    q = exe.forward(is_train=False)
    q[0].wait_to_read()

toc = time.time()
print("total time: %f ms " % ((toc - tic) * 1000))
mx.profiler.set_state('stop')
print(mx.profiler.dumps())

With mxnet-mkl==1.5.0b20190506, profile as following:

Profile Statistics.
        Note that counter items are counter values and not time units.
operator
=================
Name                          Total Count        Time (ms)    Min Time (ms)    Max Time (ms)    Avg Time (ms)
----                          -----------        ---------    -------------    -------------    -------------
WaitForVar                            400           1.2550           0.0020           0.0080           0.0031
Reshape                               400           7.5040           0.0150           0.0320           0.0188
transpose                             800           7.0800           0.0070           0.0260           0.0088

With this PR, reshape time is reduce from 7.50 ms to 1.73 ms.

Profile Statistics.
        Note that counter items are counter values and not time units.
operator
=================
Name                          Total Count        Time (ms)    Min Time (ms)    Max Time (ms)    Avg Time (ms)
----                          -----------        ---------    -------------    -------------    -------------
WaitForVar                            400           1.2640           0.0020           0.0080           0.0032
Reshape                               400           1.7300           0.0030           0.0110           0.0043
transpose                             800           7.9200           0.0060           0.0330           0.0099

[vandanavk]

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

@mseth10 for review

[pengzhao-intel]

Minor comments.

[pengzhao-intel]

what's the situation of else?

[pengzhao-intel]

Thanks for the explanations. Will merge the PR after CI pass.

[pengzhao-intel]

The PR is verified internally with performance and accuracy.
Merging now. Thanks for your contribution.