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* Find2.0 changes for the Quant  and De-Convolution (#1408)
* fix for 3d-UNet

Local Threads of multiples 32 were introduced in #1348
But LocalThreads that are not multiple of 64 are causing correctness issues.

updated GPU pad to now use JIT version.
added range functions for JIT kernels.
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

* rearrange default pass list; adjust_allocation must be run after rep… (#1418)
* Regenerate driver models (#1422)
* Add support in mlir for transposed and broadcasted shaped (#1378)
* Add relaxed standard shape assertion (#1416)
Co-authored-by: Brian Pickrell <95253842+bpickrel@users.noreply.github.com>
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>
Co-authored-by: jungpark-mlir <jungwook.park@amd.com>

hipMemset is causing random failure.
hipMemsetAsync is doing the correct synchronization.

Allows for rank 2 tensors into batchnorm.  Specifically when spatial dimensions are all 1 and removed

Removes use_dynamic_same_auto_pad
Change padding_mode to be used for dynamic padding
Move compute_padded_shape to pad_calc.cpp as it will be used in other dynamic padding cases
Fix same_lower compute_padded_shape bug and add a test.

Rewrites the TF batch norm like operators to other MIGX operators
Removes the code related to batch_norm_inference

Simplified algebraic operations (x*1), x*(-1), x/1, 0+x & x+0,  x-0, 0-x, 0*x, x*0, and 0/x operations

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.

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

* fix typo for add_sigmoid

* remove deprecated constructor

* Replaced `find_library` with `find_package` to locate MLIR static library
* Unified the include dir for headers and remove backward compatibility
* Embedded the external/include dir into the exported library

The verify tests from pr #1354 were still causing some codecov timeouts after merge. This PR further reduces the problem sizes to avoid these failures.

* fix slice_dim1 for case

* Implement concat using jit compilation

expose underlying p data inter for migraphx.argument
Update python api documentation

Improves performance for 4/6 GEMMs used by huggingface BERT models with batch_size>1 by using a non-batched rocBLAS call for GEMMs where the B input has a broadcasted batch dimension.
The four verify tests added reflect the actual configurations used by bert-base-cased, with varied batch sizes.

Also adds a matcher to simplify_reshapes to move multibroadcasts after concats.

migraphx version is now 2.4

* Remove unused headers

Fixes TF literal parsing for relu6.  previously always made a float type literal, breaks for float16 as an example

* Fix accuracy bug when vectorizing slices

Using not and or improves readability. The cppcheck rule will help ensure we are doing it consistently.