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Outline of an empty branch is correct typically seen if we parse in an empty
constant for the other side of the if then/else onnx logic.

Need to do this to match shape even though the other branch is just empty.

Just clean up the logic on this so we're not 5 levels deep on ifs.

- use empty() instead of size() == 0 for checking each condition
- Make each branch a lambda instead of repeating code.

Hand verified results before running test using random data generated from
the new protobuf files for this.

Everything is correctly functionally within tolerances.

Had to readjust input sizes to that of the .onnx file as one input is deliberately
made smaller (without the trailing 1) for theses tests to test the case correctly

Fixes test which verifies how we interpret vectors with trailing ones, between input branches to
an IF module block.

Update test for new generated onnx file, as well as using unsqueeze output
for when we parse in a network with mismatched shapes with a trailing 1

Inverted logic when I wrote generated statements for these. Regenerated files

Fixes cases for trailing one testcases

This makes sure we're getting the correct output value of the branch if an
empty shape is given as a parameter to the IF operand.

- Gets past to the split section of the resnext model
- adding outline seems to solve if issues but verify calls broken
- Referencing wrong element now instead of output of correct if block?
- Need to determine proper output through verify tests.
- Modified protobuf to handle case of extra 1 to "vectorize" scalar
- Modified verify/tests to get things to "work", may need to be revised further.

- Update if_then/else_empty test protobuff and cases
- Need to update rand() vector used
- Make y empty instead of x for if_else_empty_test.onnx
- Regenerate protobufs with updates
- Add changes to handle empty/scalar input branch size to if operator.
- Add case where if both branches empty throw an error.
- Update verify tests with gold vectors and new shapes for empty input vec
  which we handle like a scalar before broadcasting

- Verification tests that test each then/else branches for parsed IF operator
- Testing empty shape tensors for one branch -> output must be the other branch's shape
- Testing trailing 1 shape for one branch -> output must be union of both inputs

Current issue with IF operator is that we can't handle training vectors that match
in size correclty while also running into issues with empty inputs for one of the
branches for size/type checks.

This seemed to be missing, just leveraging the existing protobuf made
to test parsing of if_then_test.onnx for this and using the tensor of all
ones to default to an ADD operation to ensure cond =1 is being handled and parsed
in correctly.

Still busted work in prgoress. Keep running into

terminate called after throwing an instance of 'migraphx::version_1::exception'
  what():  /code/AMDMIGraphX/src/normalize_attributes.cpp:91: tune_attribute: TUNE_VECTOR: value out of range!
Aborted (core dumped)

Comming back to this once I've fixed parse_constant, looks like unallocated empty literals break this right now.

Need to have this to force the output to be a compatible to both the else/then cases

Still a work in progress

Need to handle static shapes explicitly since onnx says we should be able to just output a compatible output type.

The onnx spec mentions that the output shape of the resulting then/else branches must share the same type, but not the same shape. The only requirement is that the first dimension is compatible should one of the inputs have rank of one.

Without this we prematurely assert when an if is requred on the following case

int64, {1234, 1} (this is a 1 rank tensor)
int64, {1234}  (this result is scalar)

Stream sync changes and associated API level changes

optimize the softmax operator

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.

Improvements/Additions to be made:

changes for the quant_convolution,
changes for the deconvolution,
Macros for MIOpen status checks

* Fix invalid program from find_splits

test_gpu_pack_int8_args fails on gfx908 machine, because it doesn't set compute_fp32 flag correctly. This PR fixes the test such that it checks for the device-name, and rocblas-versions and sets this flag accordingly.

Implement operator for CPU and GPU implementations

Rewrites the BatchNormalization ONNX operator into other MIGX operators
- Added handling of 1D input tensor case (edge case in ONNX spec)
Removes the spatial and per_activation functionality (not in the ONNX spec)
- Did not remove the batch_norm_inference related code as the TensorFlow parser still uses it
- Can remove that code when the TF version is updated

Upgrade cppcheck to 2.9 

Workflow has concurrency reintroduced with different set of rules. New expected behavior is to check concurrency on PR level with one running and one pending performance tests. In case of multiple commits in same PR, always the latest commit is queued after initiated performance test execution is completed. Any other PRs/commits are in pending/queued state

Codecov announced deprecating the bash uploader. Using updated uploader

* Remove device functions
* Update tests

This PR allows for other values of epsilon to be matched when finding layernorm. Similarly, the calculation now uses the variable for epsilon.