GitLab

By converting to fp32 : fp16 3d-unet model accuracy comes out the same as FP32 accuracy.

By using reduce_sum method on Fp16 : accuracy comes out ~0.9% lower compared to fp32 while keeping entire model in fp16.

Adds a new GPU compiler pass split_single_dyn_dim that handles when one input parameter has a single non-fixed dynamic_dimension.
commonly occurs for dynamic batch or BERT sequence length
Splits the dynamic shape into several submodules will static input parameters to handle all of the cases in the dynamic_dimension range.
Essentially does what I manually did for the select_module verify tests
Adds a compile option split_single_dyn_dim that toggles the pass on/off. Defaults to false.
Updates verify_program.hpp and run_verify.cpp to allow for the tests to change the compile_options

Updated each source file in the repo with the existing license.

* Add all_targets cmake target

* Rename target

* Add ref target

* Rename tests

* Refactor compiler target

* Formatting

* Verify for every target

* Formatting

* Add verify test suite

* Formatting

* Add initial test programs

* Formatting

* Add rnn tests

* Formatting

* Validate gpu

* Formatting

* Remove old gpu tests

* Fix gpu tests

* Fix ref error

* Fix tidy issues

* Formatting

* Tidy fixes

* Fix header in python api

* Rename to ref

* Use ref in verify_onnx

* Fix tidy issue

* Build with verbose on

* Fix typo

* Remove verbose

* rename some cpu prefix to ref
Co-authored-by: Shucai Xiao <Shucai.Xiao@amd.com>