* [ONNX]Pool3d and Upsample3d op updated

* Pool3d and Upsample3d testcase

* Review comments fixed

* Review comments

* Fix x86 conv2d and depthwise conv2d auto tuning

* Fix depthwise conv2d infer layout

* Use random data instead of empty data for autotvm

* Fix pylint

* Keep empty array for now for autotvm

* Support mixing normal and cross-thread reduction

* minor improvements

* [REFACTOR][TIR] Migrate most of low-level build to use the Pass Manager.

- SplitHostDevice
- ThreadSync
- BindDevice
- LowerThreadAllreduce
- Provide a temp fix for printing IRModule with PrimFunc before the formal text printer.

* Address comments, fix tests.

* Fix relay tests

* Explicit move

* First pass a defining a non-recursive Graph Vistor and Rewriter

autoformat

remove a currently empty test until testing is solidfied

* Make CalcDep from Dead Code Elimination non-recursive

* Partially working, not passing all tests yet

passes tests when disabling GetExprRefCount, I think I have a bug in visit counting

fix GetExprRefCount

Fix a subtle bug with nested recursive/non-recursive scopes

* Refactor

* improve comments

* respond to review comments on comments

* Fix a problem with default recursion for dataflow nodes

mark DataflowVisitor methods as override

* implement ScopeMutator

* convert forward_rewrite to ScopeMutator, remove DataflowMutator

* rewrite ExprRewriter and convert fast_math to use it

* switch BiasAddSimplifier to ExprRewriter

fix a clang warning

fix cpp lint

fix doc param error

* respond to review comments

* fix a typo in the iterative looping

* add a regression test for GetExprRefCount issue

* Normalize naming

* fix lint

* First pass a defining a non-recursive Graph Vistor and Rewriter

autoformat

remove a currently empty test until testing is solidfied

* Make CalcDep from Dead Code Elimination non-recursive

* Partially working, not passing all tests yet

passes tests when disabling GetExprRefCount, I think I have a bug in visit counting

fix GetExprRefCount

Fix a subtle bug with nested recursive/non-recursive scopes

* Refactor

* improve comments

* respond to review comments on comments

* Fix a problem with default recursion for dataflow nodes

mark DataflowVisitor methods as override

* implement ScopeMutator

* convert forward_rewrite to ScopeMutator, remove DataflowMutator

* rewrite ExprRewriter and convert fast_math to use it

* switch BiasAddSimplifier to ExprRewriter

fix a clang warning

fix cpp lint

fix doc param error

* respond to review comments

* fix a typo in the iterative looping

* add a regression test for GetExprRefCount issue

* Normalize naming

* fix lint

* respond to review comments

For certain network topologies, MCR could hang.
This patch fixes that case.

Change-Id: I3edd8a8a6b452b2b838b777720adea22a3b995b4

* [KERAS]upsampling3d and zeropadding3d op

* [KERAS]upsampling3d and zeropadding3d test case

* Review comments updated

- Support vectorized casts

- It is incorrect to extract elements from int8x4 with

   0x000000ff & (x >> i * 8)

  as this value is of type int in C/C++. If this expression
  is used for sign extensions, the sign bit will be wrong.
  Simply use C style casts instead and sign bits will just work.

Signed-off-by: Wei Pan <weip@nvidia.com>

Rationale: The current hybrid module is more aligned with the te part.
We might consider add a new varient of hybrid script that support the unified IR later.
This refactor paves for the potential later changes.

- Reduce CI docs task log size.
- Update the relation to halide to the latest state.

* [PYTORCH]AvgPool3d, MaxPool3d and Squeeze op support

* Testcases added

* review comments

- Migrate LowerTVMBultin
- Migrate inferFragment, LowerThreadAllreduce
- Migrate ThreadSync
- Refactor target::Build to directly take IRModule.
- Remove un-used legacy functions.

Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn>

This PR introduces BufferLoad/Store to TIR. The new nodes will replace
Provide and Call with Tensor arguments in the subsequent refactors.

* expose runtime::String to Python

* retrigger ci

* expose runtime::String to Python

* kExternalSymbol -> kGlobalSymbol

* [Frontend][Torch] Simplify operator input handling

* [Frontend][Torch] Allow user supplied input names to override graph inputs

* Fix pylint issues

* Updates from code review feedback

* Fix tutorial to use shape list input

* Disable intermittent test failure in topi vision test

Signed-off-by: Wei Pan <weip@nvidia.com>

* [REFACTOR][TIR] Introduce ExprDeepEqual, Remove IRDeepCompare

This PR introduces ExprDeepEqual which reuses the StructuralEqual infra.
We migrated the usecases of ir_pass::Equal to ExprDeepEqual and StructuralEqual.

* Address comments

* [RELAY] Fixed issues with MergeCompilerRegions

This PR addresses a few outstanding issues with
the implementation of MergeCompilerRegions. In
particular, it now handles TupleGetItem nodes properly
and other minor bugs related to region merging have
been fixed.

Change-Id: I07783afc56183a6f798a510209f23b0a5f252255

* Fixed issue using pre-merged regions

Change-Id: I0a844ac59bda1089ae0c67cef52f0b0c7ab2cbd7

* Removed some debugging logic

Change-Id: Ib6f2eede6f38bbb270073eb8d4c4dc19f60832c6

* Remove default annotations

Change-Id: I9b7696a51c95871491cbea33c40f92ec327e417f

* Annotate default 'if's

Change-Id: I0098bd1bf6788dd6366810dcefa84f1ebbffaab0

* Clang format

Change-Id: I944365cd3080a97a9261f643a8f1efa5a63cf82b

* Use src/dest in merge

Change-Id: Ie43113492bda8f1ce63eaf9615cb645bb9e2ee86

* Fixed partition test

Change-Id: I46f9e349b1a813a9140f7e4f8a2241687e2df73b

* Removed comments

Change-Id: I309afdd1951d7e796e41d13788aa487707e0ac4c

* [PYTORCH]Dropouts And InstanceNorm support added

* Review comments fixed

* [BUGFIX]bugfix in tensorflow space_to_batch_nd

* Test case added

* [DOCS] Use https link

* use http for sphinx

* [RELAY] Codestyle fixes for Graph Partitioner
	*ran through clang-format

* *formatting comments

* *further codestyle changes (after clang-format)

* [PYTORCH]Activations for pytorch

* Review comments updated

* [TIR][TRANSFORM] Migrate LowerIntrin

* LowerDeviceStorageAccessInfo

* Migrate LowerWarpMemory

* [RELAY] Re-wrote the Graph Partitioner to support multiple outputs

Input : A Relay module that have functions with disjoint annotated regions
        using compiler_begin and compiler_end. There could be multiple outputs.

Output : A Relay module with global functions for such disjoint annotated regions
         with calls inserted at the respective location

Dependencies : AnnotatedRegionSet Utility class.

Methodology :
      1) The AnnotatedRegionSet utility class is able to construct a collection of
         nodes that are bound by a give annotation -- here we use compiler_begin
         and compiler_end
      2) Initially, for each function in the module AnnotatedRegionSets are populated.
      3) Then, Vistor pass is traversed until a compiler_end node is encountered
         that belongs to a "region".
      4) When the first compiler_end of a given annotated region is found, a function is
         formed and inserted.
         a) if the region has multiple outputs, a Tuple node (capturing all outputs)
            is returned.
      5) Thereafter, if we encounter an another output of the same annotated region,
         it is important to note that the function is already formed. Therefore, it will
         lookup the function and add a TupleGetItemNode is inserted.
          a) We will use the location index of "rets" of each "Region" of AnnotatedRegionSet
             as TupleGetItemNode index.
      6) Therefore, functions will be created for all annotated regions. The name for each
         global function is created using "Region" id and the compiler name.

Change-Id: I1372f02a845b6d3da03b561763e03a378dca263c

* [RELAY] Re-wrote the Graph Partitioner to support multiple outputs

    *removed the expected use-case as we are taking broken-down PR approach
    *code style fixes
    *some trivial one liners

* [RELAY] Re-wrote the Graph Partitioner to support multiple outputs

    *fixed an implicit copy to a move

* [RELAY] Re-wrote the Graph Partitioner to support multiple outputs

    *code style changes for comments
    *renamed test case multiple outputs --> mixed single multiple outputs
        Since the existing test case checks for both single and multiple
        output scenarios
    *added a new test case with conv2d + batch_norm
    *some var name changes in the test

* [RELAY] Re-wrote the Graph Partitioner to support multiple outputs

	*rebased

* [Torch] Add support for split

* fix

* fix test class

* [KERAS]Pool3d support added

* Keras pool3d testcase added

* refactor

* path udpate

* [TE] reverse-mode autodiff without any optimization

Co-authored-by: Sergei Grechanik <sergei.grechanik+h@gmail.com>

* address review comments

* add comments and retrigger CI

* move unittest to debug ci

* move test back and add seed

Co-authored-by: Sergei Grechanik <sergei.grechanik+h@gmail.com>