GitLab

Signed-off-by: Artur Wojcik <artur.wojcik@amd.com>

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