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Fix to stop types failing for logical_xor during our fusions. 

Extends the ref contiguous operator to handle dynamic shapes
Updates the eliminate_contiguous pass to use the dyn_output struct

* Update migraphx docker container to include half and cmake 3.24.2

* Include timestamp while tracing

Updated Multibroadcast op to have a two input version for dynamic shapes
Current dynamic shape broadcasting logic
dynamic_dimensions must be the same or one of them is {1, 1, 0} or {1, 1, 1}
Works for dyn-dyn, dyn-static, and static-static shape combinations
Changed common.cpp for multibroadcasting for binary ops with dynamic shapes
Extended binary.hpp for dynamic shapes to test the new common.cpp stuff

fix comment for sub graph throw
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

Just grab the last output from the non empty branch and use the identity
operator to get the proper shape for the output branch. In the case of
an empty branch (empty tensor, of some type) this tends to mean "Do nothing"
so we're folding the output of the other flow branch here, and thus if somehow,
we do reach this at eval, should throw an error signalling either one of two things

1. Onnx model is invalid
2. The model has run into an error condition with its control flow.

Since IF is an odd operator that can adjust axes, and other operators in a data driven
fashion, this would serve as a check at compile and or/runtime.

* free up more github runner space
* upgrade versions

Change things in the test cases so that we're not just replicating what
the code does but use add_instruction to dictate what we should expect
for the output of fixing empty const cases.

Had to also switch an insert to add of a literal in the empty case to achieve
this in parse_if as well.

Moved the return instructions to the end of each subgraph to also fix readability
of each test.

Default to smaller dimension with trailing 1 case instead of unsqueezing to
the larger dimension.

More analysis on other networks concludes that when putting in two operands to
and IF block, the output should take the smaller of the shapes instead of the larger

Modified tests in onnx_test.cpp to parse to the correct output as well.

Can be enabled via environment variable MIGRAPHX_ENABLE_NHWC

Allows for a model to be converted to the same opset but turn on infer_shapes through
onnx. This allows us to get an idea of what should be valid for the output of nodes
in a network.

Usecase:

python3 tools/convert_onnx_version.py --model <model_name> --opset=<same_as_model> --infer_shapes --output <new_model_name>

Newer split moves the split attribute to an input. In this case we check the
number of input args then.

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

Upgraded Dockerfiles and fixed tidy issues to make Ubuntu 20.04 and ROCm 5.3.0 the default 

- Added cases to handle error cases of invalid splits for split-11 and split-13
- should solve issue with code coverage.

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

Newer split moves the split attribute to an input. In this case we check the
number of input args then.

This changes allows us to move the accumulte check to outside each conditional branch
Added more debug on this as well to show more details on this failure mode.

Adds an additional test case using a generated split-13 operator styled
test.

Fixes an observed regression error on certain Frozen Protobuf models due to PR 1280

use_dynamic_same_auto_pad was removed from convolution, but the driver models still retain the fields. This PR regenerates the files so that they are compatible again.

Reiterate the assertion on the standard shape but relax it for the multibroadcast ops deliberately inserted to explicit the broadcast.

- Initial fix to handle scalars on input for empty constant values
- Using scalar, multibroadcast, contiguous
- Fixed appropriate unit tests for simple single output constants
- Added unit tests for multi if outputs.

- TODO - multibroadcast to handle scalar so we don't use scalar

Adding test to validate what a "valid" multi input should look like and
that we correctly handle trailing 1s and correctly sized outputs.

Generated and added the two tests from gen_onnx.py with matching test
in onnx_test.cpp

-if_then_else_multi_output_shapes_test.onnx
-if_then_else_multi_output_shapes_test2.onnx

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

* use find2.0 for the convolution
Co-authored-by: Vasilii Filippov <DrizztDoUrden@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>