* [REFACTOR][PY][API-CHANGE] establish tvm.ir, migrate corresponding relay files.

This PR establishes tvm.ir and migrates the corresponding relay
files into the new folder.

API Change:
- relay.Module -> tvm.IRModule

* Update with ADT

* Migrate transform

* address comments

* Migrate module

* Migrate json_compact

* Migrate attrs

* Move LoweredFunc to stmt temporarily

* temp migrate container

* Finish migrate container

This reverts commit dcf7fbf1.

* added conv1d operators to topi.

* Started to add python testing.

* Added python conv1d implementation for testing.

* Wrote test but need to add cuda schedule :(

* Cuda schedules working for both conv1d layouts.

* All topi tests passing.

* Formatting topi.

* Removed pad_method option as its probably overkill.

* Added relay op definition of conv1d.

* End2end conv1d working with onnx.

* Lint fixes.

* Formatting fixes.

* Rebase fix.

* Switched to array based attributes for consistency across convs.

* Improved onnx parsing and testing for convolutions.

* lint fix

* Tiny tweak.

* Bug fix

* Rebase fix.

* Add group ignore to onnx conv1d frontend.

* Unified MakeConv and fixed documentation.

* improved autopadding

* Addressed feedback and simplified onnx frontend.

* Format fix.

* Basic X86 NCW schedule working.

* Added nwc schedule.

* fixed name

* Added more tests and basic x86 schedules.

* Format fix.

* Added non power of two shape tests.

* Add output_padding to generic

* Add output_padding to the reference impl

* Add output_padding to arm_cpu

* Add output_padding to the test

* Add output_padding for cuda

* Add output_padding for x86

* Make use of the new output_padding argument in Relay

* Adjust conv2d_transpose Relay test

* Fix lint errors

* Fix the VTA declaration of conv2d_transpose

* support for output padding in conv2d transpose

* some output padding will break IR pass

* Fix new conv2d_transpose test

* Update tophub

* Fix conv1d output_padding too.

* Fix the conv1d_transpose reference function.

* Fix the cuda impl

* fix the topi test for conv1d

* Update the versions in tophub.py

Co-authored-by: Thierry Moreau <tmoreau@octoml.ai>

* Added 1D pooling to Topi

* Added 1D pooling relay op and tests.

* Added onnx parsing and tests for maxpool1d and averagepool1d

* formatting

* moved partial import.

* Fixed typo.

* [FRONTEND][TF] Add conv3d

* fix high rtol

* [TOPI] add 3D upsampling Op.

* fix lint issues

* change align_corners to coordinate_transformation_mode

* fix resize3d half_pixel

* make a simple function and clean up trilinear_resize3d_python

* fix doc

* Added tvm function stencil for subpixel operations to topi.

* Topi subpixel operators added and tested.

* Added subpixel attrs.

* Added depth_to_space relay attributes.

* depth_to_space fully working.

* Fixed NHWC shape bug.

* SpaceToDepth in and all tests passing.

* lint fixes.

* Added string include

* Fixed topi formatting.

* Added DCR/CDR mode to depthtospace operator.

* [Relay] add max_pool3d in relay and TF converter

* fix comments

* implement conv3d op

* add back missed conv2d_output_shape by mistake

* fix typo and docs, add topi test

* rebase to master and merge 2d/3d unification

* use cudnn.conv_forward

* [ARM CPU] Fix contrib_spatial_pack error

* PyLint error fix

* diable no-else-return as other files

* Change the test case split OC not be 1 to cover 5D weight layout

* Add shape functions

* Fix get_const_tuple

* Fix cpplint

* Fix pylint

* Fix pylint

* rebase and fix

* Check Any for infer type

* Fix expand_dim shape func for zero rank input

* Fix pooling infer type

* Address comment

* Register layout transform attr

* :add scale2 for upsample

* update unit test for upsampling

* support latest upsample op for multiple frontend

* fix lint

* fix lint

* fix lint

* fix lint

* update scale description and rebase

* save

* lint

* support conv2d HWCN in AutoTVM and Relay

* fix lint

* fix comments and unit tests

* Add FIFO buffer op to enable explicit computation re-use in convolution

* Add a test

* Add end-to-end test with 1D convolution

* Add a stub in MXNet frontend

* Address reviewer comments

* Add back stub for MXNet frontend

* save

save

redo max test

save

address comment

fix

* address comment

* increase rtol

* address review comment

* Added arm_cpu NHWC schedules.

* Fixed kernel shape legalization.

* Added bitserial ops to relay.

* Snapshot and more missing files.

* Added dense testing.

* Added tests

* Added ASF header to new files.

* cc lint

* Pylint change.

* pylint fixes.

* Change arm legalize test.

* Added assert check to arm legalize.

* Added better documentation, fixed some bad style

* Reverted arm conv2d nhwc changes.

* [TensorFlow] Fix limitation that depth_mult can only be 1 for DepthwiseConv2dNative

* Improve code readability

* Support cblas library in dense

* start to add support for generic batch_matmul compute

* Add x86 override for batch_matmul

* Fix linting

* reset file

* Fix typos

* dummy change to re-trigger CI

* 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

* [Relay] Rewrite pass.

This pass transforms an expression to other expression.

This pass has many usecases
 * Replace a expr to another expr, if the other expr has faster performance.
 * For ASICs, we might want to modify the inputs to adapt to the HW support.
 * Alter op layout can work in conjunction with this pass.

The supporting usecase is the Intel i8 x i8 conv. Intel HW supports u8 x i8 conv
in HW. Using this pass, we can replace an i8 x i8 conv to a sequence of
operators where one of the operators is now u8 x i8 conv. This will also help
automatic quantizaion performance.

* Better API name.

* Removing the conv2d legalization for x86. Will send a separate PR.

* Test name changes.

* Registering one funtion to register FTVMLegalize.

* Better comments.

* 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

* Fixed issue #3069 by adding in_channels

* Registerd group_conv2d_nchw as topi compute

* Improved by checking tag value

* Removed group_conv2d_nchw topi registration

* Added test for relay group_conv2d_nchw

* Added assertions to forbid small group size

* Removed hard-coded oc_block_factor

* Added explanatory comments to group_conv2d_nchw_cuda

* Updated group_conv2d_nchw_cuda schedule

Removed 'direct' CUDA tests

* Reverted an accidental change in a conv2d test

* Fixed indentation problems

* Fixed a mis-commented line

* Reverted change in group_conv2d_nchw tag

* Removed commented int8 group_conv2d test

* Fixed group size assertions in group_conv2d_nchw_cuda

* 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

* Implement nn.bias_add compute in C++

* Address comments

* Remove unnecessary check

* [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