* [TOPI] Average Pool2D Bug.

Issue - https://github.com/dmlc/tvm/issues/3581

* Add uint16 test.

quickfix

* small bug fix for DataTypeObject

* retrigger ci

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.

* Update the Relay adding pass doc to reference the new pass infrastructure

* Correct pass name

Co-Authored-By: Zhi <5145158+zhiics@users.noreply.github.com>

* Align header equals signs

In cases where we have multiple models or threadpools active, spinning around
`sched_yield()` may not be desirable, as it prevents the OS from effectively
scheduling other threads.

Thus, allow users to conditionally disable this behaviour (via an environment
variable `TVM_THREAD_POOL_SPIN_COUNT`, similar to existing environment flags for
the thread pool such as `TVM_BIND_THREADS`, etc).

This substantially improves tail latencies in some of our multi-tenant
workloads in practice.

Unit tests have been added - on my laptop, running:

```
TVM_THREAD_POOL_SPIN_COUNT=0 ./build/threading_backend_test;
TVM_THREAD_POOL_SPIN_COUNT=1 ./build/threading_backend_test;
./build/threading_backend_test;
```

gives https://gist.github.com/ajtulloch/1805ca6cbaa27f5d442d23f9d0021ce6 (i.e.
97ms -> <1ms after this change)

* [RFC] Initial support for Tflite operator SPLIT

This patch adds initial support for the tflite operator split. However
I am not yet sure how to handle the axis parameter for the split
operator and support it in the test infrastructure. Putting this up for
an initial review and comment.

The split operator in tflite according to
https://www.tensorflow.org/lite/guide/ops_compatibility

appears to take num_or_size_split as a 0D tensor.

I also note that tflite.split is one of the few operators that returns
multiple outputs and thus the helper routines in the tests needed some
massaging to make this work.

@apivarov , could you please review this ?

Thanks,
Ramana

* Fix the axis parameter

Add more tests

* Address review comments

* Try out frozen_gene's suggestion

* Handle split of 1 element

* int32 is only supported in tflite 1.14, let's check that version here.

* Keep this at python3.5

* Add packaging as a python package to be installed

* updated runtime to support non-shared memory FPGAs for instruction and micro-op kernels

* adding driver-defined memcpy function to handle F1 cases

* refactor to include flush/invalidate in memcpy driver function

* update tsim driver

* bug fixes

* cleanup

* pre-allocate fpga readable buffers to improve perf

* fix

* remove instruction stream address rewrite pass for micro op kernels

* fix:

* white spaces

* fix lint

* avoid signed/unsigned compilation warning

* avoid signed/unsigned compilation warning

* fix

* fix

* addressing comments

* whitespace

* moving flush/invalidate out of memmove

* clearnup

* fix

* cosmetic

* rename API

* comment fix

* do

* fix test

* Improve boundary nodes in graph tuner

* Limit output node number

* Fix test

* Improve warning.

* Fix test

* Support additional architectures beyond x86_64 in ubuntu_install_java

While attempting to get a development environment going for TVM
on my AArch64 desktop I ran into some hardcoding of relevant architectures.

Let's welcome Zhi as a new Apache TVM Committer!

Apply suggestions from code review

Co-Authored-By: Wei Chen <ipondering.weic@gmail.com>

* [docs] Add a tutorial for the pass manager

* address comment

* address more comments

* retrigger ci

* address steven's comments

* address comments

* retrigger ci

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Steven S. Lyubomirsky <slyubomirsky@gmail.com>

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Steven S. Lyubomirsky <slyubomirsky@gmail.com>

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Steven S. Lyubomirsky <slyubomirsky@gmail.com>

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Steven S. Lyubomirsky <slyubomirsky@gmail.com>

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Steven S. Lyubomirsky <slyubomirsky@gmail.com>

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Logan Weber <36520469+weberlo@users.noreply.github.com>

* Update docs/dev/relay_pass_infra.rst

Co-Authored-By: Logan Weber <36520469+weberlo@users.noreply.github.com>

* [Relay][VM]Fix debug statement

* Change debug statement