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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

[Experimental] Update accuracy checker for tf support and map input dims (custom input shape) (#1514)

* update file reading function to fix external data loading

* Update MIGraphx Docker to point to latest working onnxrt branch.
* Add output for ONNXRUNTIME_COMMIT in CI and add env var in brackets

Change to python ONNX package 1.10.0 to handle new ONNX test generation.

Allows for a model to be converted to the same opset but turn on infer_shapes through
onnx. This allows us to get an idea of what should be valid for the output of nodes
in a network.

Usecase:

python3 tools/convert_onnx_version.py --model <model_name> --opset=<same_as_model> --infer_shapes --output <new_model_name>

Refactor dynamic compute
- add a compute_output_shape object that implicitly converts to a new dyn_output or shape object
- dyn_output object can handle computing the static output shape of an operator given the input arguments shapes
  change an operator's compute function to argument compute(const dyn_output& dyn_out, std::vector<argument> args) to 
  use dyn_output object

Dynamic ref unary functions
-  Included these changes to have an example of the refactored dynamic compute being used
-  Changes to unary base class to handle dynamic shapes
-  Changed elu and leaky_relu to use unary base class and pointwise JIT

Stream sync changes and associated API level changes

Adds two methods for the custom_ops virtual class.

bool runs_on_offload_target(), if the custom op runs directly on the gpu then it should be set to true. in this case, custom op expects its parameters to reside in GPU memory and writes output to the GPU memory. If it is set to false then, custom op expects it's parameter to reside on the host and puts back the result into the host memory.

output_alias, if output of the custom op is aliasing the input buffer. i.e. interpreting the same input buffer with differnet shape and strides.

Update as_vector() in C++ API to handle non-standard shapes. It required exposing element_index to space_index conversion method for the shape class.

Using not and or improves readability. The cppcheck rule will help ensure we are doing it consistently.