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* Update onnxruntime main 635d3faa3b3908d2806d009dc6872152cfcfcdda
* Update script for build_and_test_onnxrt.sh

This sets up the virtual environments for running python onnx tests.
ctest's fixtures are used to ensure that the virtual environment is
set up before the tests are run.

Co-authored-by: Attila Dusnoki <attila.dusnoki@htecgroup.com>

let the user know which targets migraphx was built for and how to build migraphx for their gpu.

Allows us to build wheel file for onnxruntime builds found in
/onnxruntime/build/Linux/Release/dist/*.whl

This adds a format.py file which will check or apply formatting using git clang-format or yapf.

throw on use_len with non-fixed dynamic dimensions
change normalize_attributes to use input shape rather than input dimensions

Export API symbols for migraphx, migraphx_ref, migraphx_cpu, migrphx_gpu, migraphx_device, migraphx_tf, and migraphx_onnx. There is a separate PR for migrahx_c.

API symbol exporting affects only Windows. It is transparent on Linux.

This is to help enable multi-target execution. We store a vector of targets and contexts. Currently this will only compile a single target, the PR #1672 is needed to enable multiple targets.

This will also serialize the targets and contexts.

When using the execution_environment or prog.get_context() it will always use the context from the first target assuming this is the "primary" target. Although, its unlikely a user would use execution_environment with a multi-target environment.

* Update instal_prereqs.sh to handle 22.04 defines

Needed to run containers with 22.04

* Add Dockerfile for Ubuntu 22.04 and ROCm 5.5

Updated dockerfile to use ROCm 5.5 and Ubuntu 22.04 for use with building MIGraphX
Able to run make -j$(nproc) check successfully with this

* Clean this up since its breaking CI

* cleanup install preq some more.

-use one protobuf version
-remove extra python3.8 installs from 3.10 case

* Move comment for protobuf comment

* Move Dockerfile for 22.04 to Dockerfiles/ folder

* Move and rename 2204 docker file

remove Docker_** from the name. Move these to tools/docker

* Add pip3 installs to be shared between python versions

* Add Package pin from repo.radeon.com

* Add CMAKE_ARG ONNX_USE_PROTOBUF_SHARED_LIBS for every default python dist

Set this to be default as part of installing prereqs

---------
Co-authored-by: Charlie Lin <charlie.lin@amd.com>
Co-authored-by: Umang Yadav <29876643+umangyadav@users.noreply.github.com>
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

Co-authored-by: Sam Wu <sam.wu2@amd.com>
Co-authored-by: Paul <pfultz2@yahoo.com>

Relies on Removed split_single_dyn_dim compile flag #1711
Exposes dynamic_dimension as a opaque object with dynamic_dimensions and optimals
Exposes ONNX dyn_input_dims and default_dyn_dim to run with dynamic batch
Updates api.py to be able to create objects from aggregate initialization (used for dynamic_dimension)
Uses offload copy for now

* Enable hiprtc by default

Useful to get more insight into Onnxruntime. Allows us to reuse the accuracy checker code while also allowing us to capture Execution Provider output with the --ort_run and --ort_logging flags

Also added the --target flag as well to allow us to force using either a specific target for the accuracy checking. Originally this was defaulting to the GPU. This now allows us to use ref, fpga, etc to quickly change targets.

Fixes #1595

Enable python 3.10 bindings

* deprecate HCC

* Add driver flag "--exhaustive-tune" to enable tuning, add support for the same in C/C++ and python API

Use requirements-dev.txt instead of the other instances

* add dynamic shape support to concat operator.  Includes new op_shape_test and ref_ops_test cases

Change download location for onnx model from AWS ONNX to download.onnxruntime.ai