* 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

The patch adds support for Tensorflow operators log1p and cos
Tensorflow log1p is described at https://www.tensorflow.org/api_docs/python/tf/math/log1p
Tensorflow cos is described at https://www.tensorflow.org/api_docs/python/tf/math/cos
Tensorflow sin is described at https://www.tensorflow.org/api_docs/python/tf/math/sin

* [TOPI][CUDA] schedule for group_conv2d

* Fix #flops

* Fix traverse_inline not inline zero input op properly

* Add where to python and set tag to broadcast

* Fix inline

* test

* fix test target

* fix

* [TOPI][CUDA] Schedule for pool_grad

* Relay test

* Fix fused op

* doc

* Remove set scope local

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.

= Motivation

It's useful to expose the tvm::reinterpret functionality to Relay/TOPI users, as
this allows them to build (fused) operators leveraging the bitwise
reinterpretation of an operator. An example is approximate transcendental
functions, which can be implemented similar to:

```.py
    def C(x):
        return relay.expr.const(x, "float32")

    def approx_exp(x):
        x = relay.minimum(relay.maximum(x, C(-88.0)), C(88.0))
        x = C(127.0) + x * C(1.44269504)
        xf = relay.floor(x)
        i = relay.cast(xf, "int32")
        x = x - xf
        Y = C(0.99992522) + x * (C(0.69583354) + x * (C(0.22606716) + x * C(0.078024523)))
        exponent = relay.left_shift(i, relay.expr.const(23, "int32"))
        exponent = relay.reinterpret(exponent, "float32")
        return exponent * Y

    def approx_sigmoid(x):
        # <2.0e-5 absolute error over [-5, 5]
        y = approx_exp(x)
        return y / (y + C(1.0))

    def approx_tanh(x):
        # <4.0e-5 absolute error over [-5, 5]
        x = x * C(2.0)
        y = approx_exp(x)
        return (y - C(1.0)) / (y + C(1.0))
```

See unit tests for implementations of these approximate transendentals.

* Pre-allocate buffer for x86 roi_align

* Fix typo

* Add sequence_mask

use exactly the same arguments as mxnet

fix

* fix lint

* fix lint

* add mxnet conversion + relay

* update

* update doc

* fix pylint

* fix doc

* address comment

* try to address comments

* try to enable shape check for valid_length

* fix

* try to fix

* fix bug

* try to fix

* address comment

* address comment

* fix flaky test

* fix flaky quantize pass

* Support x86 dilation conv2d and improve multi-batch conv2d

* Fix lint

* Improve non_max_suppression for CPU

* Improve get_valid_counts

* Minor change

* Skip some unnecessary computes

* Fix x86 depthwise conv2d alter_op_layout

* Small fix

* Add test case

* Fix test

* Assert kernel layout

* Minor fix

* Add get_shape function

* Minor change

* Improve roi_align performance for x86

* Change test

* init impl for topk

* Fix cpu for topk

* init cuda impl for topk

* Add cuda for topk

* fix

* Add doc

* update doc

* lint

* lint

* lint

* x

* fix warning

* [Relay] Add TopK in tf converter

* Add frontend converter

* fix

* Support the 1x1 int8 conv with NHWC layout and weight packing

fix linter

* fix the memoize issue

* fix the failed nhwc test

* add the schedule for pack to unbreak other tests

* skip avx512 compile

* Support the 1x1 int8 conv with NHWC layout and weight packing

fix linter

* fix the memoize issue

* fix the failed nhwc test

* add the schedule for pack to unbreak other tests

* skip avx512 compile

* Unify the data_layout and kernel_layout relation

* add asf header

* fix the comment

* retrigger the build/test

* [Relay][TOPI] operator All

* Update tests/python/frontend/tensorflow/test_forward.py

Co-Authored-By: yongwww <55wuyong@163.com>

* fix comments

* change to level 4

* 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

* deconv tests

* deconv bug fixed for certain cases tests added

* ssd gluoncv gpu op updated

* ssd gluoncv gpu op updated

* tutorials and testes modified

* tutorials and testes modified

* fix lint

* fix lint

* address comment

* multibox bug fixed

* space line added

* use less threads per block

* use less threads per block

* less threads per block for get valid count

* less threads per block for get valid count

* merge with master

* Revert "less threads per block for get valid count"

This reverts commit 08896cfccc34b0b2a1646d01d01ea4cad73941c4.

* Revert "less threads per block for get valid count"

This reverts commit 08896cfccc34b0b2a1646d01d01ea4cad73941c4.

* typo fixed

* elem length made to a variable

* fix lint error

* fix lint error

* lint fixed

* bug fixed

* bug fixed

* lint fixed

* error fixed

* error fixed

* test ci

* test ci

* seperate argsort to be an independent op

* seperate argsort to be an independent op

* fix lint

* fix lint

* remove unsupported models

* typo fixed

* argsort added to realy

* solve conflicts with master

* fix lint

* fix lint

* test push

* Revert "test push"

This reverts commit 6db00883fab6cc06bddf564c926bb27c874397d8.

* fix lint error

* fix more lint

* cpu test_sort udpated

* debug ci

* nms fixed

* expose argsort to relay frontend

* test ci

* fix lint

* sort register error fixed

* fix nnvm

* nms type fixed

* adaptive pooling added to relay

* Revert "adaptive pooling added to relay"

This reverts commit 1119f1f2c055753e0cc5611627597749134c5c8c.

* fix lint

* expose argsort op

* fix lint

* fix lint

* fix lint

* sort test updated

* sort bug fixed

* nnvm error fixed

* fix argsort default data type returned to be float insteaf of int

* fix lint

* fix lint

* test fixed

* fix valid count

* fix titanx bug

* tutorial add both targets

* titanx error fixed

* try to fix CI old gpu error

* try to solve CI GPU error

* get_valid_count added

* reverse get_valid_count

* get valid count optimized

* address comments

* fix ci error

* remove unessesary block sync

* add back one sync

* address comments

* address more comments

* more comments

* move sort to be indepent algorithm

* typo fixed

* more typos

* comments addressed

* doc updated

* fix pylint

* address final comments

* apache license added

* 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

* Update take

* Add special case for canonical simplify and fix test cases

* Use lower case for wrap and clip

* remove unnecssary lower

* Fix mxnet converter for take

* fix

* [Relay, TOPI] Add deformable conv2d

* Moved to op level2

* Fix lint

* Moved to level2 & bug fix

* Update comments

* Disabled flaky test of conv2d