* [Relay][Op] Add instance norm op

* mend

[Relay][Op] Add instance norm op

MXNet pad is described at:
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.pad

Add support for parameter 'None' in MXNet slice operator.

MXNet 'slice' is described at
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.slice

Add support for MXNet cos, sin, arctan

MXNet 'cos' is described at
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.cos

MXNet 'sin' is described at
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.sin

MXNet arctan is descirbed at
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.arctan

Add support for MXNet 1D Convolution and 1D Deconvolution

MXNet convolution is described at:
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Convolution

MXNet Deconvolution is described at:
https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Deconvolution

* Added relay and topi mirror_pad operator.

* Added mirror_padding to tensorflow frontend.

* Added mirrorpad testing in tensorflow frontent.

* Added space_to_depth in tf frontend.

* Added tests for spacetodepth.

* spacetodepth bug fix.

* Lint fix

* Added mirror pad python attrs.

* Pad code formatting.

* Syntax improvement

* Hopefully last lint fix

* Add LayerNorm op

* update

* fix

* Add mean_std and mean_variance

* add std and update doc

* add license

* x

* lint

* x

* fix

* fix doc

* add build gcn tutorial

* add transpose operator for square sparse matrices

* remove extra files

* change loop tag

* comply with lint

* comply with lint -- line too long

* comply with lint

* lint check

* lint check

* lint check

* apply marisa and theirry's reviews

internally and externally, interested in replacing standard dense layers with
block-sparse matrix multiplication layers. The motivations are generally: higher
performance (due to reduction in FLOPs, memory bandwidth/cache footprint),
enabling larger models (e.g. fitting more layers in a given memory budget).

Some public work along these lines:

* https://openai.com/blog/block-sparse-gpu-kernels/
* https://openai.com/blog/sparse-transformer/
* https://arxiv.org/abs/1802.08435
* https://arxiv.org/abs/1711.02782

Various groups have been able to successfully train models with reasonable
levels of sparsity (90%+) with marginal accuracy changes, which suggests
substantial speedups are possible (as this implies a >10x reduction in FLOPs).

It is fairly straightforward to realize these theoretical speedups, see e.g. TVM
benchmarks for Intel CPUs in
https://gist.github.com/ajtulloch/e65f90487bceb8848128e8db582fe902, and CUDA
results in https://github.com/openai/blocksparse, etc.

* https://github.com/openai/blocksparse (CUDA)
* https://software.intel.com/en-us/mkl-developer-reference-c-mkl-bsrmm (MKL BSRM)
* https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.bsr_matrix.html (SCIPY BSR representation)

This is extracted from an internal patch we've been using internally. There are
various extensions possible (int8/fp16/bf16, CUDA/other GPU architectures), but
this is a reasonable starting point. This needs more thorough unit test coverage
however.

We follow the conventions established by scipy.sparse.bsr_matrix and other
libraries, see the unit tests for details.

For folks interested in experimenting with scheduling/AutoTVM etc,
https://gist.github.com/ajtulloch/e65f90487bceb8848128e8db582fe902 is a useful
starting point.

* Add topi adaptive_pool

* Use adaptive_pool to compute global_pool

* Add relay adaptive pool2d

* Fix lint

* Fix typo

* Minor change

* Change support level to 10

* Add contrib

* Remove global pool schedule

* Add contrib module

* Fix lint

* Update doc

* Update doc

* Quantize dense layers

* Add out_dtype arggument to dense; Add dense_int8 on CUDA

* Add topi unittest of dense int8

* Fix relay

* Fix topi integration

* Fix quantization

* Update dense_rewrite

* Triger CI

* Change qconfig quantize_dense to quantize_op

* Fix

* Remove quantize_op from qconfig

* [HEADER] ASF header dir=include

* [HEADER] ASF Header dir=src

* [HEADER] ASF Header -dir=python

* [HEADER] ASF header dir=topi

* [HEADER] ASF Header dir=nnvm

* [HEADER] ASF Header -dir=tutorials

* [HEADER] ASF Header dir=tests

* [HEADER] ASF Header -dir=docker

* fix whitespace

* [HEADER] ASF Header -dir=jvm

* [HEADER] ASF Header -dir=web

* [HEADER] ASF Header --dir=apps

* [HEADER] ASF Header --dir=vta

* [HEADER] ASF Header -dir=go

* temp

* [HEADER] ASF Header --dir=rust

* [HEADER] Add ASF Header --dir=cmake

* [HEADER] ASF Header --dir=docs

* [HEADER] Header for Jenkinsfile

* [HEADER] ASF Header to toml and md

* [HEADER] ASF Header to gradle

* Finalize rat cleanup

* Fix permission

* Fix java test

* temporary remove nnvm onnx test

* [Relay, TOPI] Add deformable conv2d

* Moved to op level2

* Fix lint

* Moved to level2 & bug fix

* Update comments

* Disabled flaky test of conv2d

* [RELAY] Finish alter op pass

* [RELAY] AlterOpLayout Pass

* fix broadcast operators

* fix broadcast operators

* fix broadcast operators

* Support concatenate

* address comments

* address comments

* add comments

* rebase

* [RELAY] BiasAdd, MLP, Resnet testing

* fix review comments

[RELAY][OP] Operators.  	 pool2d, global_pool2d, batch_flatten, tanh, sigmoid, floor, ceil, trunc, abs, negative, multiply, mod, pow,  resize (#1813)