* overload half operators for cuda codegen

* add float16 te test_op_level1

* fix test_op_level1.py

* fix lint

* disable fp16 test if gpu does not support

* disable fp16 test if gpu does not support

* bypass float16 test if gpu does not support float16

* Fix layer norm for fp16

* [Fix] Fix arange for fp16

* [Fix] Fix mxnet frontend for fp16

* [Fix] Fix arange for fp16

* remove comments

* x

* fix nnvm

* [tvm][any] broadcast with values other than 1

* Add test for incompatible runtime values

* Remove hybrid script compact buffer binding

* retrigger ci

* update build_gcn.py tutorial

updates
* support bias in GCN layer
* download pretrained gcn model
* verify model accuracy
* use time_evaluator to measure runtime

* fix adding bias in gcn layer

* remove printing output

* fix small bug

* add DGL-PyTorch comparison into the build_gcn tutorial

* add accuracy testing

* adjust import order

* handle different dgl versions

* update number for dgl version checking

* 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

* app init push

* fix on readme

* change name, add bit serial explanantion

* rm serialLoadMM, change doc

* syntax change for readme

* add parallel test functionality

* fix readme

* add python doc

* syntax

- Adding support for Mxnet flavored dequantization for both default and using MKLDNN. User can choose between the two at runtime. (#3945)

- Added tests for new methods added.

* move the number of nodes constraint in op fusion up to the dom tree level

* add test case of limiting the max number of ops to be fused

* uncomment other test cases

The torch package depends on the future package but the torch wheel
does not expose that dependency resulting in an inconsitent install.

Ideally the wheel should declare all of its dependencies, I'm not sure
why the packagers have choosen not to do this, for now the simple work
around is to explicitly install the future package.

Change-Id: Ic9f0f4bb4c78ab65706fc1b20c1b4fd287856a9e

* [Relay][VM] Fix constant folding issue in VM compiler

1. allow pass params when compile a module
2. enhance profiler robustness

* remove dead code

* fix lint

* add get_params

* fix test

* don't pass params back

* remove get_params

* docs

* move compile function to api

* compile clashes with builtin name

* fix compilation error

* remove dead code

* add valid autotune

* fix pylint

The current bounds checking infrastructure inserts checks like:

```
for (i, 0, bounds[n]) {
  if (likely(i < bounds[n]) {
     ...
  }
}
```

into the TVM IR which is currently not removed by simplification infrastructure.
This is a little unclean, as these are trivially true since for a loop var `i`
with a given min and extent, we are guaranteed that `i >= min` and `i < min +
extent`. Thus, we can insert these checks into the IR and use them to eliminate
trivial bounds checks early on.

Use fdiv in the tests for the deduce_bound

if n_trial is larger then config space.

Issue:
git clone latest TVM/VTA and run VTA on xilinx FPGA board, application
crashed due to the "call stack overflow" which caused by a infinite recursive
function call. this issue ever happen before and get addressed by PR 3843.

Analysis:
seems like de10-nano driver PR  used old code base then the  logic change
of 3843 get eliminated.

Solution:
add the logic back.

The -mfloat-abi hard option does not work for LLVM < 6.0 as it is ignored.
This adds a fatal error when using unsupported LLVM versions so that the failure
is not silent.

* Fix VM invoke with set_params

* add test

* tweak

In current implementation HIDE_PRIVATE_SYMBOLS hides symbols from TVM
itself but not from its dependent libraries. This is problematic when
other third-party libraries with the same symbols are linked to the
same executable.

One example is using TVM with Mesa OpenCL drivers: they depend on LLVM
and load its shared libraries with RTLD_GLOBAL flag, which results in
conflicts with LLVM symbols that TVM uses. Arguably this particular
issue belongs to Mesa (here's their tracking bug:
https://gitlab.freedesktop.org/mesa/mesa/issues/236) but in general
that's the right thing to do regardless of this particular bug.

Note that I'm not enabling this functionality for Darwin as in my
earlier tests their linker didn't seem to understand "--exclude-libs"
(but I don't have test platform ATM to double-check).

fix some typos

The tensorflow zeros_like operation provided in array_ops.py produces directly a tensor with zeros
without a graph, using only the shape and type of the input. This imposes the use of gen_array_ops.py
that produces both a tensor and a graph so a comparison between tflite and tvm can be done.

@zhiics @icemelon9

* [Relay][VM] Add more passes to VMCompiler

* Check build config

* Add todo

* fix

* tweak