* [TOPI] upsample operator 'NCHWinic' format support.

some hardware accelerator ask packed format data like NCHWinic to fit the
hardware resource, here add upsample NCHWinic format support to help
such requirement.

* address review comments, add assert for 'else must be NCHWxc' logic.

This is motivated by the want to send an
array of strings across the python/C++
boundary. Arrays only support ObjectRef types
and so can't carry StringImmNodes. This creates
a string reference type, StringImm, which can
be used with tvm::Arrays.

Change-Id: I598a44536c156b97dbfe3e9518e0a1f705da850c

* QNN doc fix on conv and dequantize

* fix param name in tflite frontend

* make different fix

* Expose VM OptimizeModule to python

* added missing imports

* fix import

Add support for: greater_equal, less, less_equal, equal, not_equal
Add tests for the elemwise relational ops

- Do not use numpy.prod which ignores integer (64 bits) overflows.
  This leads to an incorrect number of points in the search space.

* Implement pass tracing API

* Set is_before correctly

* Add docs for trace function

* Fix lint

* Remove PDB

* Ensure trace_func is set before calling

* Fix conditional

* ONNX frontend broadcast condition

* fix

* fix style

Co-authored-by: Jon Soifer <jonso@microsoft.com>

Co-authored-by: Jon Soifer <jonso@microsoft.com>

* Explicitly link to cublasLt

* Only link cublasLt if it's found

Co-authored-by: Jon Soifer <jonso@microsoft.com>

fixed a spelling mistake.

* fix formula for calculating end indices when size[i] == -1
* add a test case for size[i] == -1
* discard expanding dimension of begin_value & end_value since
  it is needed only if you pass them as scalars not as tensors.
* discard 'slice_tensor' variable so that implementation matches
  the tf parser pattern

* remove cpp upsampling

* remove cpp resize

* [VTA] Support network which have no unique operator as start/stop name
for graph pack.

[Issue]
  Current vta use 'start' and 'stop' name to define the pack start point
  and end point, but this method not work for these network which have
  no 2 unique operator as  start point and stop point.

[Solution]
  In this solution we give 2 addtional parameters start_name_indx and
  stop_name_indx to make vta pack logic work with the said network,
  for exampl for following networks which have no unique operator,

  %0 = nn.add
  %1 = nn.conv2d
  %2 = nn.batch_norm
  %3 = nn.leaky_relu
  %4 = nn.add
  %5 = nn.conv2d
  %6 = nn.batch_norm
  %7 = nn.leaky_relu
  %8 = nn.add

  with this solution we can use following parameter format to make
  vta work on it.

  relay_prog = graph_pack(
                //....
                start_name="nn.add",
                stop_name="nn.add",
                start_name_idx=0,
                stop_name_idx=4)

  to apply on new network, by printing the network we can get index information like following.

  print(mod.astext(show_meta_data=False))
  relay_prog = graph_pack(mod
                          ...
                          start_name="nn.add",
                          stop_name="nn.add",
                          start_name_idx=0,
                          stop_name_idx=4)

* address review comments and fix index count bug

issue:
when do print(mod), the output not only the Call is also have other type
like Var, need add logic to count all except meta.

solution:
add related logic

* address review comments.

* address review comments

* add more detail comments.

- combine pad and dilate;
- fix for the issue https://discuss.tvm.ai/t/compile-error-for-cuda-target/4164
- fix for the issue https://github.com/apache/incubator-tvm/pull/4472

"driver" normally refers to the "main" function.
Rationale: the header exposes set of APIs to drive compilation
and should be named as driver api to best reflect its usage.

* BYOC Tutorial -- part 2

* Fix comments

* Address comments

Bring up namespace te -- Tensor expression language DSL.

* [REFACTOR] Establish printer in the source folder.

As we move towards the unified IR, we will eventually want to build a unified
printers for both relay and TIR.

This PR isolate the printer component into a separate folder in src as a first step.

- Refactored the Doc DSL using Object, clean up APIs.
- Isolate out the meta data into a header.
- move printer into relay_text_printer, add comments about further TODos.

* Rename NodePrinter -> ReprPrinter to distinguish it from other printers

* expose BindParamByName to python

* fixed alpha equal test

* [REFACTOR][TYPE] Finish move all types to IR.

- Move definition of Ref and TensorType to ir
- Move type_functor.h to public header.
- Rename RefType -> RelayRefType for clarity.

* Add atol