* one weird trick.

* Added schedule knob for different workloads.

* Initial conv3d tensorcore working.

* Added conv3d tensorcore strategy.

* Added layout conversion to tensorcore friendly format for conv2d and conv3d.

* Add target name check.

* Fixed bad names and depthwise check.

* Removed duplicated attribute assignment.

Signed-off-by: windclarion <windclarion@gmail.com>

* [PYTORCH]Reduce_ops support added

* Review comments updated

* typo bug in qnn test

* use funcs from prelude, pass around convert_map

* get relay input type from user ishape

* handle tuple unpack

* experimenting with static tensor array

* use prelude concat instead of cons + rev

* minor clean up

* fix layer norm conversion bug, unwrap tensor array

* add infer shape on tensor array

* pass around prelude for now

* compile worked but runtime error

* fix tensor array wrapping

* begin list dynamic test

* is_list_dynamic first version

* finish dynamic list test

* a few fix

* use shape_of function if Any is found

* improve size conversion

* working on adding free vars to loop block

* fixed inlined inner loop issue

* clean up free var handling

* add support for tensor array concat

* adding ta concat on last axis

* fix concat, but got runtime error

* disable concat on axis -1 for now

* add lstm tests

* revert unrelated change

* fix stacked bidir test

* minor fix to test

* relax tol a bit, revert dnnl change to avoid conflict

* simplify infer type, use input tensor shape rather than concat shape

* more shape fix

* [Intrinsic] Add log1p, ldexp, atan2, hypot, nextafter, copysign

* Lint

* Adding Cast back to Int32 in FixedPointMultiply.

* Removing extra clip.

* Fix space.

* Retrigger.

* Retrigger.

This PR enables the copy on write optimizations passes:
- Enable COW for IRModule both TIR and relay passes.
- Enabled COW for PrimFunc in TIR passes.

Need more thoughts into whether/how to enable COW
for relay::Function, due to some function passes depend
on the presence of IRModule for context information,
and the std::move of the related function to nullptr
might affect the related behavior.

* [PYTHON]Abs, Arange, Softplus ops

* Review comments updated

* [LLVM] Fix generation of LLVM intrinsics

The type list in the call to llvm::Intrinsic::getDeclaration is not
the intrinsic's signature, it's the list of overloaded types. Without
this fix, the updated unit test would cause the following error:

TVMError: LLVM module verification failed with the following errors:
Intrinsic name not mangled correctly for type arguments! Should be:
llvm.ctlz.i32
i32 (i32, i1)* @llvm.ctlz.i32.i1

Special handling for llvm.prefetch, sig matching for overloaded ints only

The prefetch intrinsic returns void in LLVM, while it returns i32 in TVM.
This case needs to be handled specially, because rule-based intrinsic
translation would cause invalid LLVM type to be created.

Do the signature matching only for overloaded intrinsics. It's not needed
for non-overloaded ones, so this can save a bit of compile-time.

* Include intrinsic name in the error message

* Fix number of arguments for llvm.fmuladd and llvm.pow

* merge change from dev branch

* fix string issue

* bring comanic's change back

* Support TF Frontend Static TensorArray

* Fix pylint

* Fix lint

* Move get_tensor_array_shape into prelude

* Fix lint

* Fix common

* [RUNTIME] Introduce RValue reference(move) support to TypedPackedFunc

This PR introduces RValue reference support the PackedFunc calling convention to address the above issue.
Specifically, when an argument is a r-value reference, we will use a assign a different type code(`kObjectRValueRefArg`),
and pass `Object**`  (the address to the Object pointer) instead through the values array.
The callee can choose to move out this Object pointer and set the original Object pointer from the caller side to be nullptr.

We also add an experimental move support to the python side(marked as _move so to indicate the dev nature).
This enhancement will enable copy on write optimizations through out the TVM stack.

* Address review comments

* fix compilation

* add target to region

* refactor annotate_target

* Make all unit test working

* quick fix

* enable BN, unit test failed

* Fix vm test, unit test. Refactor annotate_target a bit.

* quick fix fusion

* revert fusion change

* style fix

* Refactor merge region pass

* format

* minor fix

* Skip e2e test

* lint

* support AnnotateTarget multiple runs

* Add HasAttr and revert DNNL codegen

* address comment

Co-authored-by: Zhi Chen <chzhi@amazon.com>

* [arith] linear system and equation solver

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

* avoid constructing analyzer every time

* generate random test cases and address comments

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

* rename linear_system to int_constraints

* add comments and use random seed

* message for reporting failure with seed

* add SEqualReduce to IntConstraints; allow variables & ranges to be None

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

* [FRONTEND][TENSORFLOW] Fix gather_nd indices

* retrigger CI

* Use runtime::String

* move string to tvm namespace

* add const char* constructor

* implicit cast from std::string

* [RUNTIME] Initial implementation of Hexagon runtime support

This is only the TVM runtime. The FastRPC libraries, simulator driver,
etc. will be provided in subsequent commits.

* Fix pylint complaints

* Fix some more pylint complaints

* Add link to the Hexagon SDK website

* Extract VTCM marker into a common variable

* Implement device->device memory copy

* Disable unsigned PDs by default

* Ensure that --hvx_length is present in sim_args if HVX is enabled

* Remove the line about clang from README.md

Apparently things work with libstdc++.

* Mention to set USE_RPC=OFF when building libtvm_runtime.so for Hexagon

* Remember to use codegen_hvx in validate_hvx_length

* Add a line about minimum version of LLVM

* Improve DNNL

* Add bind params

* trigger ci

This PR refactors the serialization mechanism to support general
serialization of leaf objects into bytes.

The new feature superceded the original GetGlobalKey feature for singletons.
Added serialization support for runtime::String.

* Legalize - Use Non-recursive Rewriter.

* Cleanup.

* Set IterVar index to 0 for local thread bound IterVars.

* Lint fix

* Use rank instead of scope name to predicate.  Add tests.

* Handle cases other than local/threadIdx.

* Turn warp to the old behavior.

* Modify test to cover global/blockIdx.

* Fix a typo.

* Update test_te_schedule_ops.py with more testing coverage in test_local_stage_predicate; remove test_schedule_schedule_ops.py which was added by mistake.

* [CI][DOCS] Make sure to refresh the cython part

* [BUGFIX] Fix CRT static test bug

* Fix demo_static

* resolve review comment

* fix String SEqual

* retrigger ci

In newer versions of LLVM, this header is no longer included by one of
the already included headers in llvm_common.h, so include it explicitly.

* [RELAY] Add 'check' functions to MergeComposite

Currently, MergeComposite can only perform structural
matches. This patch introduces the ability to specify
a 'check' function alongside the pattern which can include
custom logic to determine whether an extracted pattern
should be merged.

For example, if you only want to merge 'NHWC' convolutions,
you can specify a 'check' function which queries the
data_layout value of the extracted pattern (see the test).

Change-Id: I9337ce39f10997051a286d888be38ed0d410d340

* [RELAY] Reformat merge_composite.cc

Run clang-format on merge_composite.cc

Change-Id: I1736bff798cc6d93e57519b08ab3362869098779

* [RELAY][BYOC] Support composite functions in AnnotateTarget

This patch introduces support to annotate composite functions
in the AnnotateTarget pass. In order for a composite function
to be annotated, you should name it according to the style:

{codegen}.{name}
eg. dnnl.add_relu

Change-Id: I74d6c0b506153d866f6d1feb203b32dad59f2871

* Add implementation of TVMDSOOp

* feat: Update cmake script to work with c++11 and in-repo build

* feat: Use libtvm as oplib dependency

* fix: Add missing link dependency to libtvm

* feat: Update tf tvmdso op by review comments

* fix: Update with pr comments

* fix: Fix lint

* feat: Add test script and fix gpu shape

* feat: Add test script and fix gpu shape

* fix: Conditional build tftvm op for gpu

* fix: Conditional build tftvm op for gpu

* fix: Fix pylint of tf_op module.py

* fix: Fix pylint of tf_op module.py

* feat: Conditional enable gpu test for tftvm op

* feat: Conditional enable gpu test for tftvm op

* feat: Add tf_tvmdsoop test script as an app test

* fix: Fix gpu/cpu enabled check on tvm in test script

* fix: Make tf tvmdso op test script runnable with pytest

* remove unused test script test_tfop_module.py

* fix: Remove pushd & popd in tfdsoop test script

* fix: Upgrade tftvmop use python3 to find TensorFlow

* fix: Upgrade tftvmop use python3 to find TensorFlow

* fix: Change target_link_options to target_link_libraries

* fix: Add tftvmop build script's c++ option

* fix: Add tvm library path to tf op test library path

* fix: Debug ci build for tftvm dso op

* fix: Fix cmake error and skip tfop test

* fix: Fix typo and indentation issues

* feat: Use TF list input op def

* fix: Fix style and unexpected changes

Co-authored-by: baoxinqi <baoxinqi@4paradigm.com>
Co-authored-by: Chen Dihao <chendihao@4paradigm.com>
Co-authored-by: wrongtest <wrongtest@4paradigm.com>