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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.

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>

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

outline seems to be bugged, and going to be depreciated, thus switching to
creating a new literal with the proper shape for the empty output branch.

-make all_but_last_dims_equal func instead of lambda
-rename dim_delta -> rank_delta
-make unsqueeze_last_op func instead of lambda
-Handle multi output cases of changing output instructions
-capture shape at each output shape at start of loop via .at() operator
-replace instances of && with and

This test should error out as, having different output shapes for each
branch with one non empty is invalid.

Changes to gen_onnx.py as well as generated onnx file provided

Add the testcase and onnx file generated to handle the case of two output
shapes that vary in rank by one, with a trailing 1 but sub lengths are not equivalent

This should be flagged as an error always for static inputs

- added changes to gen_onnx.py for different type, shape, and incompatible lens
- added test cases that should except in onnx_test.cpp

- gen_onnx.py changes for onnx output of empty const input branches (seen in resnext50)
- updated onnx_test.cpp to validate parsing of input.
- new onnx files generated from onnx tests

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