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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

)

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updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
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Signed-off-by: dependabot[bot] <support@github.com>
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

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

Changed MessagePack file extensions to mxr.

This causes incorrect memory coloring, which was causing the accuracy failures in the vision model when enabling the pointwise fusions. Resnet50, inceptionv3 and inceptionv4 do verify now in the driver.

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.

This will also check the types using mypy on the CI.

* use the parse_resize to parse the upsample operator

Add cppcheck to examples

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

* Add mean op onnx parser and unit tests
* Refactor parse_mean to use add_broadcastable_binary_op

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

Add HardSwish to HardSigmoid parser

HardSwish formula is y = x * HardSigmoid<alpha=1/6, beta=0.5>(x)
HardSigmoid parser sets alpha to 1/6 and adds the mul instruction if op name is HardSwish

Resolves #1062

Add onnx parser for operator GreaterOrEqual