* add half and mix precision support to cublas backend

* add TensorCore support in CuDNN

* enhance CuDNN support

* address comments and fix lint

* fix

* add fp16 test

* [LINT] Improve the check tool to handle ASF copyright message.

* [LINT] Remove unnecessary copyright message as per ASF requirement.

* Fix codegen hybrid

* [LINT] Broaden license checks to include html, xml

* [LINT] Fix rest of the files

* Fix notice

* [LINT] Improve check file type error message

* [VM] add a few more API to vm

* [VM][Fix] fix vm convert args

* [VM] a few fixes

* rename fields

* update

* update vm profiler

* x

* add doc

* lint

* fix test

* address comments

* Add qnn conv2d attributes for input_tensor_scale and
kernel_tensor_scale.

The lowering in the tflite frontend loses the input_tensor_scale
and the kernel_tensor_scale by multiplying it and putting it into
the Requantize operation. This means that any graph partitioning
passes or other passes that need to access this information no longer
have it available in the qnn dialect.

regards
Ramana

* Store input tensor scale and Weight tensor scale for Dense as well

As for conv2d, the tflite frontend drops the input tensor
scale and the weight tensor scale from the relay op. Store
it as separate fields in there.

* Fix unintentional tab

* Rename input_tensor_scale to input_scale and kernel_tensor_scale
to kernel_scale for conv2d.

* input_tensor_scale -> input_scale weight_tensor_scale->weight_scale

* Rework dense testcase

And use input_scale and kernel_scale

* Be consistent in use of input_scale and kernel_scale values

* Fixup qnn conv2d tests for input_scale and kernel_scale

* Make pydoc identical between conv2d and dense for weight_tensor

* Fix up conv2d parameters to be in the same order between C++ and python

* Fix ordering of parameters for dense.

* Add input_scale and output_scale to try and satisfy ci gods

* Delete input_scale and kernel_scale.

nn.conv2d does not contain input_scale and kernel_scale. We need
to delete it when lowering it to nn.conv2d.

* Add input_scale and kernel_scale for qnn.conv2d

[Relay][VM][Interpreter] Enable first-class constructors in VM and interpreter via eta expansion (#4218)

* Fix constructor pretty printing

* Make Module::HasDef name consistent with API

* Add VM constructor compilation via eta expansion

* Lint

* Fix CI

* Fix failing test

* Address comment

* Retrigger CI

* Retrigger CI

* add gcnArch query

* kGcnArch query for cuda is a no-op

Previously runtime::Module was supported using shared_ptr.
This PR refactors the codebase to use the Object protocol.

It will open doors to allow easier interpolation between
Object containers and module in the future.

* Add pass manager tutorial

* fix some examples

* retrigger ci

* Update tutorials/dev/relay_pass_infra.py

Co-Authored-By: 雾雨魔理沙 <lolisa@marisa.moe>

* Add ToANormalForm link

* 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

* [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.

* Add support for Any op

* Support ONNX frontend

* Add doc

* Add to relay docs

* Dummy change to retrigger CI

* [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 scale2 for upsample

* update unit test for upsampling

* support latest upsample op for multiple frontend

* fix lint

* fix lint

* fix lint

* fix lint

* update scale description and rebase

* Add support for attaching params

* Fix types

* Fix test

* [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

* save

lint

* address reviewer comment

* [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][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

* [RUNTIME] Refactor object python FFI to new protocol.

This is a pre-req to bring the Node system under object protocol.
Most of the code reflects the current code in the Node system.

- Use new instead of init so subclass can define their own constructors
- Allow register via name, besides type idnex
- Introduce necessary runtime C API functions
- Refactored Tensor and Datatype to directly use constructor.

* address review comments

* [RUNTIME] Introduce new object protocol.

This PR introduces a new object protocol to unify the node and object.
We also updated the existing runtime::vm code to make use of the new system.

Update to the node will be done in a follow up PR.

Other changes:

- Remove object related code in json serializer as that code logic was not complete
  and we have a separate serializer for VM, can revisit later.

* address review  comment

* Fix the child slot logic

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

* Add test for incompatible runtime values

* Remove hybrid script compact buffer binding

* retrigger ci