GitLab

For dynamic shape handling

Merge branch 'refactor_dynamic_compute' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_ref_broadcast

Not needed, special case with dynamic padding

Merge branch 'refactor_auto_pad_conv' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into refactor_dynamic_compute

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.

Change find_mul_conv to work with multibroadcast also. Checks the strides instead of the broadcast axis.

Compute mean and variance in same reduction
Set block size to numbers divisible by 32 instead powers of 2
Global is also set exactly instead of being divisible by block size
More exact matching of global/local can help get rid of branching/loops
Reduce vectors first before doing dpp_reduce
Explicitly vectorize array operators since the compiler doesnt always vectorize them
Still uses old for loop when its computing at compile-time since the reinterpret_cast nor the all the vector types is supported