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Enable retrieving the code object to do tuning in the future.

Automatically fuse multiple reductions and pointwise operations.

* [MLIR] add dot offloads with manual tuning support
* This commit adds dot + pointwise fusion support
along with manual tuning using rocMLIR.

Avoids double global loads.  Strided loops are unrolled which lets store results in array which compiler will use registers for since the index access is constant.   Updated to handle large reductions so which results with a better stable diffusion result

JIT implementation of the gather operator
Added a few more unit tests to this one as well since I saw some odd behavior during bring up.

Update dialect registration interface
Update 2nd build pipeline call and use full arch name

Can be enabled via environment variable MIGRAPHX_ENABLE_NHWC

updated GPU pad to now use JIT version.
added range functions for JIT kernels.

Refactor dynamic compute
- add a compute_output_shape object that implicitly converts to a new dyn_output or shape object
- dyn_output object can handle computing the static output shape of an operator given the input arguments shapes
  change an operator's compute function to argument compute(const dyn_output& dyn_out, std::vector<argument> args) to 
  use dyn_output object

Dynamic ref unary functions
-  Included these changes to have an example of the refactored dynamic compute being used
-  Changes to unary base class to handle dynamic shapes
-  Changed elu and leaky_relu to use unary base class and pointwise JIT

* Enable non-standard shape
* Use perfdb for non xdlops
* Fix transpose+broadcast strides
Co-authored-by: jungpark-mlir <jungwook.park@amd.com>

optimize the softmax operator

Improvements/Additions to be made:

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

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

* Implement concat using jit compilation

* Remove unused headers

* Add in changes for onnx Mod operator

Initial operator for mod implementation and test cases for integer and floating based types.

Need to use fmod from stdlib for floating point types. half_float::half thankfully is specced to the use the existing std::fmod() call when looking at the half.hpp implementation.

fmod_flag should mirror the onnx fmod attribute. Right now using a floating point type without setting that on the user side to true will result in an exception.

Ref ticket #1283 

* Add softmax kernel