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This adds the infrastructure so we can compile everything in parallel, whereas before only pointwise kernels were compiled in parallel. This will also directly integrate with lowering and the gpu-driver. The kernels for pointwise and roialign are using this infrastructure. Scatternd is not since it does require standard shape.

This also makes it easier to add new runtime compiled kernels in the future.

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

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

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.

* 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.

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.

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

Add with_type to shape class

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

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.

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

Support slice, broadcast and transpose shapes for the squeeze op.

Enforce types to avoid compilation error in pointwise fusions
This fixes compile failure: gpt-2, fp16 on Navi

Currently, MIGRAPHX_TRACE_EVAL=2 prints out the entire output buffer, but this can produce a lot of output. To make it easier to inspect and debug, using MIGRAPHX_TRACE_EVAL=2 now only prints 10 elements from the buffer(the first 5 and last 5) and shows any fp classifications found in the buffer(ie nans, infinity, etc). The previous behavior can still be enabled with MIGRAPHX_TRACE_EVAL=3.

* Enable auto vectorization
* Handle vector types with convert function
* Dont vectorize when it will cause problems with preload

allow nonstd shape for the arg ops, non-standard shapes include broadcast, slice and transpose

Make clip a pointwise op