* 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

This PR moves the codegen related code into the target folder,
as they are target specific functionalities.

We also adopt the term "compiler driver" in common compiler infra
such as rust, GHC and clang.
As a result, build_module is moved into the driver folder.

TIR is the new namespace for low-level IR
for tensor-level optimizations and loop transformations.

This PR establishes the namespace and files.

- lowered_func.h,buffer.h,data_layout.h -> tir/buffer.h,tir/data_layout.h,tir/lowered_func.h
- ir.h -> tir/expr.h, tir/stmt.h
- ir_functor_ext.h -> tir/expr_functor.h, tir/stmt_functor.h

* unify vm and interpreter objects

* move closure back vm

* adt/closure back to vm.adt/vm.closure

* closure base