GitLab

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