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

There is significant improvement on larger tensors with half almost 50% faster:

lens: [1024, 384, 768]
gpu::code_object[code_object=13832,symbol_name=kernel,global=39321600,local=256,]: 1.16685ms
gpu::reduce_sum[axes={2}]: 1.73126ms
Also for non-trivial layouts this can sometimes be over 2x faster:

lens: [64, 1024, 768, 4]
gpu::code_object[code_object=13832,symbol_name=kernel,global=39321600,local=256,]: 1.1706ms
gpu::reduce_sum[axes={1}]: 2.63375ms
Of course if the stride becomes larger this speed improvement diminishes due to poor memory access patterns. A lane_reduce instead of a block_reduce is needed for such type of kernels. I plan to address that in a future PR.

Finally, this also includes a MIGRAPHX_GPU_DUMP_ASM env variable which will print out the assembly when the kernel compiles.

* code backup

* clang format

* change corresponding tool files

* clang format
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

* 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>