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Use ifdef instead of comment for the auto-generated method declarations for type erased classes (#1138)

It seems the formatting of comments are unreadable for larger methods, so instead just generate a struct with the methods in the interface and add a comment if its optional. It wraps this in #ifdef TYPE_ERASED_DECLARATION(assuming this would never be defined) instead of #if 0, so most editors can still provide syntax highlighting(although I think vscode with clangd will still gray it out unfortunately).

* Use ccache for runtime compilation

* Handle string literal in construction
* Improve get_default with vector

This creates a custom op which has name() and compute_shape() methods. 

Operators using arg.reshape() method the lifetime will be extended.

* LpNormalization ONNX parser

Add exclusive and reverse modes to gpu implementation of prefix_scan_sum, which completes support for ONNX op CumSum

The get_context may change in the future(when we support multi-targets) so make this experimental for now.

API includes following
create_module,
get_main_module
add_instruction without module args
add_instruction with module args
add_parameter
add_return

This adds iterators to tensor_view, which can allow kernels to work with non-standard shapes like for roialign.

To improve the performance of indexing when using the iterators, the shape class was updated to use integral_constants since the compiler doesn't always fold the const values. An integral_constant will at least enforce that in the AST.

Finally, since index calculations with single integers are improved, I also updated pointwise to use single index rather than multi index. There is about 4% improvement in some cases.

Have git ignore build directory and compiled python
Add ignores from other ROCm projects that look applicable
Ignore downloaded models in test/
Remove including visual studio settings

change max number of groups in a kernel to 1B for greater performance

* Show the operator fields in the driver

The module::debug_print(ins) is very slow, which makes the trave_eval==1/2 very slow. The reason is printing an ins involves search the whole module to get the instruction, the print it.  This change is to fix that by calling module::print() to get names of all instructions of a program, then print the instruction by getting its name from a hash map.

Add Celu ONNX operator

Add python API to construct shape class

Add a callable C++ API to migraphx

* Implement size ONNX operator and tests

add_common_op for parse_clip
Should fix #1119

Adds EyeLike ONNX parser and unit tests.

Changed the pooling values for two structures from strings to specialized enum classes. Many test and operator parsing changes to support this. Introduces one new source file, op_enums.cpp.

Boost the max number of workgroups for pointwise ops by matching what we are doing in launch.hpp

better performance doing it this way

Add onnx parser and ref and gpu implementations of ONNX op ScatterND

Implements the IsNaN operator, ref, gpu, and onnx parser.

Update the base version of clang-format from 5.0 to 10.0

* Free space on github jobs

* Delete more stuff

* Use sudo

Release branch created for ROCm 5.1 so moving develop branch to 2.2

Add with_type to shape class

Needed for custom_op so we can generically convert the C type back to the C++ type in the function pointer.

wrapped in a any_ptr class so the type can be checked at runtime for a mismatch.

Make doc/CMakeLists.txt standalone
Switch to use rocm-cmake modules for document generation
Add CONFIGURE_DEPENDS to file(GLOB) so it will update without an explicit cmake run
Add STRINGS property for build type to make it easier to switch build types with ccmake
Various fixes and improvements

This PR is the resolve two problems in the issue#999, i.e., non_standard_shape input to reshape and reduce_mean.
Three fixes:

Any operator that has a standard shape requirement will add a contiguous input for its input.
Eliminate_contiguous, when computing whether a contiguous can be removed, we should use all the updated args, not just the one that is being checked.
In two optimization in the simplify_reshape, we remove the contiguous in the reshaper name list, since eliminate_contiguous will remove the contiguous if it can be removed.
the solution is add an attribute to the operator that requires standard input shape, then in the auto_contiguous pass, add a contiguous to every input of such operators.

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.2 to 2.5.3.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.2...v2.5.3

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
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Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Support nonstandard shapes like slice, broadcast and transpose for the unsqueeze op

* add submodule test
* remove for loop
* simplify reshape test

Update copyright for a python file that was modified when added to our code 

There is now a MIGRAPHX_DISABLE_POINTWISE_FUSION to disable it