* [Relay] Added MergeComposite pass

This pass allows for patterns to be wrapped
in a function marked with 'Composite' and a
composite function name. This is intended to be
used with the external codegen for the cases where
an external operator maps to multiple Relay
operators. In that case, the mapping can be expressed
as a pattern and assigned a name.

For more information on this pass and its motivation,
see the RFC:
https://discuss.tvm.ai/t/rfc-external-codegen-defining-composite-relay-operators/5470

Change-Id: Icb1b803a9f0ac57c529143200228f3bb5793afc0

* [Relay] Merge composite tests

Added tests for the merge_composite pass.

Change-Id: I1728b4a05b0c1c36140a40f1afe028fde62185dd

* Merge composite additional test

Change-Id: I9bc7d6053c575e9468ac5abc31214c6ad8507e46

* Support priority order in merge_composite

The order in which the patterns are matched
was currently random as an unordered_map was
used to store the pattern table. This uses
arrays instead so that a distinct priority
order of matching can be defined. Additional
tests have also been added to verify this
behaviour.

Change-Id: Ief347df4262639138d5d9d7c8cee7ef233af7b56

* Improved merge composite docs

Change-Id: Ie3a72045ecc3f13ad3c302fbdf192b7296a306a8

* Removed unused variable

Change-Id: I7814d5fde368ffaf1b3d6d806060c774c7720364

* Remove unnecessary op check

Change-Id: I38e78d2acd5b86cb8e837be72ff9d72cd10bcf33

* Improve styling on composite function creation

Change-Id: I37add1c3134e0b5d5085fe1eb9daf8e06890fa8c

* Comment reword

Change-Id: Ie05872dcbbe0c3e1190b0597083b9a64e6b66c66

* Stylistic changes to avoid std::move

Change-Id: I43a93995bbf10530399900c992aa99dd4ae4575f

* Relax a check in ExtractPattern

Change-Id: I0faef77a66c55f83f09e6e47c561ffaea63dedfa

* Remove new line

Change-Id: Ifdd02c12087a7e1a0a9b54825669bc0de8f13c3d

* Removed MatchPattern from MergeComposite

This is not necessary now that ExtractPattern
can fulfill the same purpose.

Change-Id: I14dc020afa8e50f2df4c0a2efb88a011987f8196

* Removed a new line

Change-Id: I8b50f0c9069aa1bcaccbe68eb421031f01a64842

* Improved docs for merge composite

Change-Id: Ib1959a35c856e7ea5639de2e4ef314a54f44caf5

* Fixed free vars in test

Change-Id: I2b7f273db275964ec0e9820560663f0808adee79

* Handle case where root arg might not be a call

Change-Id: I4eeea3ce723d3ba337d110dcc690377daebe8626

* Removed blank line

Change-Id: I07f5392c0e95cfe3cfa5c333703cc6f82d6034fb

* Change to CHECK_EQ

Change-Id: I5c5d62d3cd57f72508b30b926f72091ae6f0d1cc

* Revised a conditional

Change-Id: I23a7897ca15a7cd076db5039dc653a4b8c27e803

* Improved doc styling

Change-Id: I377f0a1c1ac70f3b8d7584b0c49bddc8c6c134ef

* Fail extraction if vars conflict

Change-Id: I78e36d805e8ed6b55e61d490212a967c857554a4

* Added further merge composite tests

Change-Id: Ib1d800409fca4c1834c7fe0cab5a26ab99a26820

Co-authored-by: lhutton1 <35535092+lhutton1@users.noreply.github.com>

Fixed bug in ExprOp that caused bitwise operators to fail when a basic python type was on the left hand side of the expression. Added regression test for crashing cases. (#4852)

* Fixed process termination routine in windows

addresses and Fixes AttributeError: module 'os' has no attribute 'killpg' error in #4821

* Update server.py

* [REFACTOR][PY-API] Polish tvm.runtime, tvm.runtime.module API update

This PR updates the tvm.runtime to use the new FFI style.

- Remove top-level tvm.module to avoid confusion between runtime.Module and IRModule
- API changes wrt to runtime.Module
  - tvm.module.load -> tvm.runtime.load_module
  - tvm.module.enabled -> tvm.runtime.enabled
  - tvm.module.system_lib -> tvm.runtime.system_lib
- Remove dep on api_internal from runtime.

* Update module.load in the latest API

* [Relay][Frontend][TFlite] Add support for quantized LOGISTIC

 * add qnn implementation
 * add qnn test case for qnn logistic

* Helper functions for quantize and dequantize.

* Initial version working and passing tests.

* WIP on supporting other activations.

* add support for multiple activation functions in lstm

* All tests working and code cleaned up.

* Undo import swap to avoid conflict with masahi.

* Added new tests and related bug fixes.

Co-authored-by: Matthew Brookhart <mbrookhart@octoml.ai>

* 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