* Add bitwise ops to topi

* Add the bitwise ops to relay.

* [CONTRIB][CC] Enhance cc.cross_compiler

- Enhance cc.cross_compiler to take str argument.
- Remove cc.build_create_shared_func as it is dupilicated with cross_compiler
- Add examples to cc.cross_compiler

* address review comments

* enforce 4-way padding

* add util with get_pad_tuple

* delete unnecessary arguments

* fix lint

* add container.Array case

* fix cudnn conv2d asymmetric padding logic

* rename get_pad_tuple to get_pad_tuple2d

* revert change for topi/python/topi/nn/conv2d.py

* add get_pad_tuple2d for several contrib conv2d ops

* add get_pad_tuple2d for all conv2d ops

* [Relay][Frontend][TFLite] Add parser support for logical operators

* Add parser support for logical_and, logical_or
* Add boolean dtype as a valid tensor type
* BOOLEAN dtype is supported only from tf 1.15
  so logical ops work only in that and newer versions
* Logical_not is ommited since tflite can't convert it -->
  throws errors for addv2

* Add TFLite vesion check in tests for logical ops

* Check is added because of boolean dtype lack of support

* [QNN] Optimize lowering for requantize and FixedPointMultiply.

* Add check for requantize scale gt 1.

* Added test case.

* [TFLite] Dynamically calculate input_stats of any fake_quant range

* pass the input range to the convertor and caclulate (mean, scale) there
* change the range of the second tensor in elemwise operations
  so that we test inputs with different quant params
* change the possible output range for elemwise ops wrt the updated ranges
* update the comments for (m, s) calculations
* add input range dict to reduce_mean op

* Apply requested changes

* add exception handling for zero division in input_stats
* fix range of the input tensor in elemwsie

* fixed subprocess creation under windows 

this addresses  the issue #4819

* Update server.py

* [REFACTOR][PY] Establish tvm.runtime

This PR establishes the tvm.runtime namespace that contains the core runtime data structures.
The top-level API are kept inact for now via re-exporting.

We will followup later to cleanup some of the top-level APIs.

* Fix ndarray name

* - Additional util methods needed for mxnet frontend for qnn dialect.

* - Fixing call to quantize.

* [QNN] MxNet-MKLDNN parser support for QNN

* [QNN] Relax conv check.

* - Merge from origin

* [QNN] Channel wise changes

* [QNN] Dense changes

* Dense fix for QNN ops.

* - Removed non-mkl code from utils.

- Small refactoring

- Remove "with_sum" from conv

- Simplified code

* - Fixing ring buffer name.

* - Fixing pylint issues.

* - Fixing lint
- Removing redundant commented code.

* - Adding test cases
- Removing unused methods.

* [WIP] end to end test case for mxnet qnn parser

* Changes to parse large CV models.

* Pylint issues.

* Fix Conv2D with sum and quantized pooling.

* Reverting the changes made for mxnet-mkldnn test cases. Because of #4753, mxnet could not be updated to mxnet-mkldnn.

Co-authored-by: Animesh Jain <anijain@umich.edu>

* [REFACTOR][PY] tvm._ffi

- Remove from __future__ import absolute_import in the related files as they are no longer needed if the code only runs in python3
- Remove reverse dependency of _ctypes _cython to object_generic.
- function.py -> packed_func.py
- Function -> PackedFunc
- all registry related logics goes to tvm._ffi.registry
- Use absolute references for FFI related calls.
  - tvm._ffi.register_object
  - tvm._ffi.register_func
  - tvm._ffi.get_global_func

* Move get global func to the ffi side

* [TOPI][x86] Injective Schedule Improvement.

* Add tiling.

* Vectorize when there is an axis.

* [AutoTVM] Minor bug fixes in AutoTVM for QNN graphs.

* Bring back strided_slice.

* Replace tvm.nd change.

* [LINT] Fix -Wextra

* Fix virtual-dtor

* [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>