* Add Auto TensorCore TensorCore Unit Test

* Rebase to tvm master branch & Add auto tensor core

* Code Refine

* Add tensor core switch by pragma

* Add pragma in tensor core example code

* Get real tile size to replace hard coded 16

* support more than 2 dimensions (e.g. batchmatmul) for buffer bind scope

* support batch matmul

* Move cuda env check to tensor_core.cc

* Coderefine for tensor_core.cc

* Refine comments

* Some refinements of code and comment

* Update TensorCore UT to pass the CPU test

* remove redundant code

* matmul's storage align for different layout

* Add support for differenct position of type cast

* Add formal tutorial for auto tensorcore codegen

* move tensorcore check up to tutorial code

* code and doc refine

* comment out tune_and_evaluate in tutorial

* fix cpplint error

fix the problem that android_rpc compilation failed

* fix_winograd_cuda_kernel_size

* add unit test

* Batch matmul tuning running but with errors.

* Default x86 schedule as good as before.

* Code Cleanup

* Remove unused argument.

* improved template documentation.

* Silly lint fix

* Removed leftover comment.

* Moved cfg declaration to schedule for batch_matmul

* Moved x86 dense cfg declaration to schedule.

* lint fix

* Removed duplicate cfg declaration in dense.

* Reverted changes to dense.

* fix winograd

* move get padding after kernel transform

* [Contrib] Fix error message at callback_get_section_size()

* Trigger notification

* Update TensorUtil.scala

* Update test_vta_insn.py

LLVM 8 will crash when loading the bitcodes

This is a runtime check as the file will be compiled in even when
USE_ROCM OFF is used in the configuration if ROCM is installed
in the default location.

Fixes: #4087

* Add StopGradient. Add batch_dims attr to ignore list for GatherV2

* Trigger CI

* [VTA] Performance optimize, remove unnecessary contigious memory use.

Issue:
Uop maintain a cache vector to copy uop data into contigious DRAM memory for
FPGA/Simulator use, but this cache vector not get clear after FPGA/Simulator
core run, in Resnet18 case, if we printf the cache size in UopQueue::ReadBarrier
function, we can saw such cache size keep increase, this would cause
no use data copy and unnecessary contigous DRAM memory malloc.

Analysis:
This issue caused by not clear cache_ vector when do
uop_queue_.Reset().

Solution:
Override BaseQueue Reset function in UopQueue and add cache_ clear
logic.

* address review comments, remove spacing.

* Support reshape for dynamic shape in tf converter

* Only allow reshape directly after shape function for symbolic input shape

* Fix lint

* [NODE][REFACTOR] Rename IRFunctor->NodeFunctor, use function pointer for dispatching.

Previously we used std::function for the functor dispatching.
It introduces additional overhead and problems during dll destruction(of std::function).

This PR changes the std::function to function pointers.
This change a bit restrictions around the set_dispatch that we can get around,
but will improve the general efficiency by reducing one level of indirection in the std::function.
We also no longer need special marcos to register functions to the Functor.

* [Relay][Pass] Avoid FoldConstant folding some ops

* rename

Signed-off-by: qinqiuping <autumnqin@126.com>

* [CI] Update the ci-gpu to use cuda10

* [CI] Enforce tensorcore gpu for unittest

* [CI] Move gpu docker binary to cuda10

* Fix the gcn tutorial

* [Doc] Update ANTLR instruction

* Update docs/install/from_source.rst

* [CI] use llvm9 for the gpu tests

* Update Docker script to support new nvidia docker

* Add support for Any op

* Support ONNX frontend

* Add doc

* Add to relay docs

* Dummy change to retrigger CI

* Added slice v10

* Added constantofshape operation and small refactor.

* Finished one_hot implementation.

* Reshape working across all bert layers.

* Fixed constantofshape and removed code duplication.

* onnx model fully ingested.

* Working on improving onnx tests.

* Changed onnx testing to use onnxruntime instead of caffe2, also formatted.

* Add arbitrary output nodes to onnx frontend.

* Added v6 tiling for bert squad 8 support.

* Small syntax fixes

* Reduced code duplication in split opset versions.

* Added batch matmul test

* Added unstack split testing.

* Adde onehot test, needs a little cleanup probably.

* Replaced deprecated constant fill with constantofshape and updated tests accordingly.

* Added tests for new opset version of slice and tile.

* lint clean up

* Lint fixes

* Changed onnx dependency

* Went back to caffe2 runtime for CI integration.

* Rebase and small typo/syntax changes.

* Added hard casting of onehot attributes to int.

* [QNN] Improving Dense lowering.

* - Moving get_shape method to util
- Finalizing the test cases and the code structure for optimized dense computation.

* - Fixing cpplint.

* - Addressing review comments.

* - Renaming the variables correctly.

* - Renaming the variables correctly.

* Add Python type functor and tests

* Lint roller