* save

* lint

* [NODE][REFACTOR] Refactor reflection system in node.

- Removed the old Node, Node is now just an alias of runtime::Object
- Introduce ReflectionVTable, a new columnar dispatcher to support reflection
  - This allows us to remove vtable from most node objects
  - The VisitAttrs are registered via TVM_RESGITER_NODE_TYPE,
    they are no longer virtual.
- Consolidated serialization and reflection features into node.

* Explicit type qualification when calling destructor.

* Fix SPIRV, more comments

* add tensor core support

* avoid memory bank conflict

* fix thread sync & better performance

* better performance

* add schedule test for conv2d

* extend into BatchMatMul

* support config fragment shape and layout using intrinsic

* add TensorCore tutorial

* add int support and fix lint

* address comment

* add 32*16*8 TensorCore test

* fix wmma include logic

* support conv2d HWCN in AutoTVM and Relay

* fix lint

* fix comments and unit tests

* save

lint

* address reviewer comment

* Fix example code in comment of tvm.build_module.build()

* Update build_module.py

* Support setting path to ANTLR jar

* Update comment

This reverts commit 6f9d028b.

* Start to update TF frontend docs

* Add rst

* Remove markdown

* Update wording

* Resolve comments

* [rpc] use callback func to do send & recv. don't get fd from sock as it is deprecated in java

* fix java build

* fix min/max macro define in windows

* keep the old rpc setup for py

* add doc for CallbackChannel

* [Relay][Frontend][TF] Fix Size operator

* Uncomment tests

* [bugfix][codegen] fix casting bug in llvm codegen

* update example

* retrigger ci

* check llvm version

* count MAC for BatchMatMul

* update doc

FIX "After connecting he usb" with "After connecting the usb"

This patch adds multiply operator for quantized tensors.
The details of the quantized multiplication are outlined
in the code.

This builds on pull request 3927 and includes the changes
Animesh mentions in the comments on that request.

Change-Id: I555715b53d0266a91d5c03dc3dfe8fc31e7ce4e1

* [REFACTOR][NODE][RUNTIME] Move Node to the new Object protocol.

This PR removes the original node system, and make node as a subclass of Object.
This is a major refactor towards a better unified runtime object system.

List of changes in the refactor:

- We now hide data_ field, use Downcast explicitly to get a sub-class object.
- Removed the node system FFI in python.
- Removed the node C API, instead use PackedFunc for list and get attrs.
- Change relay::Op::set_attr_type_key(attr_key_name) to relay::Op::set_attr_type<AttrType>().
  - This change was necessary because of the new Object registration mechanism.
  - Subsequent changes to the op registrations
  - The change revealed a few previous problems that is now fixed.
- Patched up a few missing node type registration.
  - Now we will raise an error if we register object that is not registered.
- The original node.h and container.h are kept in the same location.
- Calling convention: kObjectHandle now equals the old kNodeHandle, kNodeHandle is removed.
- IRFunctor now dispatches on ObjectRef.
- Update to the new type checking API: is_type, derived_from are replaced by IsInstance.
- Removed .hash member function, instead use C++ convention hasher functors.

* Address review comments

We think it will reduce the confusion with the meaning.

https://discuss.tvm.ai/t/discuss-consider-rename-vm-datatype/4339

* Add LiftIfThenElse pass

* Add more comments

* Rename and refactor

* Add description for internal data structure

* Rename a test

* Minor change

* Address comments

* Improve update_for

* [Relay][Frontend][TF] Add tensor array ops

* rename

* delete test

* Move utility function

* Refactor

* fix tensor array ops

* fix test

* fix rebase

* Fix serializer bug

* Improve tf convert name lookup to use prelude api

* Fix lint

* Fix test

* [relay][vm] Separate VM runtime with executable

* Address comments

* move ctx back to vm

* make only vm related fields and methods protected

* integrate seriliaztion/deserialization to executable

* create stream

Arm architecture provides optional FP16 floating point support in two alternative formats, IEEE and an an alternative Arm format.

The ACLE (Arm C Language Extension) defined preprocessor symbol __ARM_FP16_FORMAT_IEEE can be used to distinguish between implementations providing IEEE and the Arm alternative format, but cannot, on its own, be used to determined if FP16 HW support is actually present.

Testing this preprocessor symbol can lead to undefined __floatdihf at runtime on an aarch64 target where no FP16 HW is present.

The relevant preprocessor symbol to determine whether FP16 HW support is present in the target is __ARM_FEATURE_FP16_SCALAR_ARITHMETIC, this symbol implies  __ARM_FP16_FORMAT_IEEE.

The relevant preprocessor symbols are defined by the ACLE standard, section 5.5.21 16-bit floating-point data processing operations, https://static.docs.arm.com/101028/0008/Q2-ACLE_2019Q2_release-0008.pdf

The existing sequence of pip install commands fetches and installs
torch==1.0.1.post2 then fetches an unpinned version of torchvision,
recent torchvision packages hardwire the specific torch version they
depend on, the overall effect is that we install a pinned torch
version then replace it with whatever version the torchvision package
depends on.

The most recent torchvision==0.4.1 package results in some test case
failures.

This patch pins torchvision back to 0.4.0, the most recent version
that the test suite worked.  Removing the explicit torch install
because it is implied and pinned as dependency of torchvision.

Change-Id: Ib30bf6aed79ff130ea15ef5134fefb0508790574