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- 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

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

- Handle checks for each IF output
- add const to inputs of all_but_last_dims_equal
- add std::equal instead of using equal
- Use .back() for vectors in getting last value
- Use input().front() instead of prev(prev()) when replacing the last value.

Check which shape is larger and adjust the check using equals so we never
have the option of going out of range with different sizes outside of the smaller
vector.

- rename then/else_shapes -> then/else_lens
- change assert for shape size to reusing throw()
- return lengths in handle_empty_branch
- use handle_empty_branch return value for lens update
- use std::prev() instead of predecriment operator for modifying nodes

Allows for rank 2 tensors into batchnorm.  Specifically when spatial dimensions are all 1 and removed

Outline of an empty branch is correct typically seen if we parse in an empty
constant for the other side of the if then/else onnx logic.

Need to do this to match shape even though the other branch is just empty.

Just clean up the logic on this so we're not 5 levels deep on ifs.

Removes use_dynamic_same_auto_pad
Change padding_mode to be used for dynamic padding
Move compute_padded_shape to pad_calc.cpp as it will be used in other dynamic padding cases
Fix same_lower compute_padded_shape bug and add a test.

- use empty() instead of size() == 0 for checking each condition
- Make each branch a lambda instead of repeating code.

Fixes cases for trailing one testcases

This makes sure we're getting the correct output value of the branch if an
empty shape is given as a parameter to the IF operand.

- Gets past to the split section of the resnext model
- adding outline seems to solve if issues but verify calls broken
- Referencing wrong element now instead of output of correct if block?
- Need to determine proper output through verify tests.
- Modified protobuf to handle case of extra 1 to "vectorize" scalar
- Modified verify/tests to get things to "work", may need to be revised further.

- Update if_then/else_empty test protobuff and cases
- Need to update rand() vector used
- Make y empty instead of x for if_else_empty_test.onnx
- Regenerate protobufs with updates
- Add changes to handle empty/scalar input branch size to if operator.
- Add case where if both branches empty throw an error.
- Update verify tests with gold vectors and new shapes for empty input vec
  which we handle like a scalar before broadcasting

Still busted work in prgoress. Keep running into

terminate called after throwing an instance of 'migraphx::version_1::exception'
  what():  /code/AMDMIGraphX/src/normalize_attributes.cpp:91: tune_attribute: TUNE_VECTOR: value out of range!
Aborted (core dumped)

Comming back to this once I've fixed parse_constant, looks like unallocated empty literals break this right now.

Need to have this to force the output to be a compatible to both the else/then cases

Still a work in progress

Need to handle static shapes explicitly since onnx says we should be able to just output a compatible output type.

The onnx spec mentions that the output shape of the resulting then/else branches must share the same type, but not the same shape. The only requirement is that the first dimension is compatible should one of the inputs have rank of one.

Without this we prematurely assert when an if is requred on the following case

int64, {1234, 1} (this is a 1 rank tensor)
int64, {1234}  (this result is scalar)

Rewrites the BatchNormalization ONNX operator into other MIGX operators
- Added handling of 1D input tensor case (edge case in ONNX spec)
Removes the spatial and per_activation functionality (not in the ONNX spec)
- Did not remove the batch_norm_inference related code as the TensorFlow parser still uses it
- Can remove that code when the TF version is updated

Using not and or improves readability. The cppcheck rule will help ensure we are doing it consistently.

Has NMS op output a dynamic shape (ONNX spec behavior)
Allows for dynamic input shape to NMS op

* Imply type of literal returned based on input protobuff for zero element constant values.

This saves us the default behavior as the onnx parsing assumes that every zero value is float. This way we're still grabbing relevant type information from the protobuff instead and wont fail our data type checks for if them/else blocks from onnx

* Revert "Imply type of literal returned based on input protobuff for zero element constant values."

This reverts commit 390bb853

.

* Add  test case to parse in empty constant int64 proto buffer

I think the previous test case was aliasing an issue where we default to float but need to actually read in int64 instead of int32

* fixup! Add  test case to parse in empty constant int64 proto buffer

* Add test for non empty int64 scalar

Add one item in the np array to use for the constant we're parsing in.

* Draft partial fix

* Fix test failures from previous change to read in protobuf data types correctly for empty constants.

Instead of assuming things are empty and thus we default to float, reading in the correct types broke some assumptions code was using for an empty literal.

* Fix formatting and naming

* Fix naming with var in constant_one_val_int64_test
Co-authored-by: charlie <charlie.lin@amd.com>
Co-authored-by: kahmed10 <15948690+kahmed10@users.noreply.github.com>

* Dynamic shape handling in shape object

* rewrite empty lens multibroadcast test

* Shape class changes to handle dynamic
* More throw errors for functions that don't make sense for dynamic shape
* Print output changes
* Serialization changes

* Fixing serialization errors

* Remove const on dyn_dim copy getters

* Dynamic shape tests

* Fix serialize errors

* Add dyn_data struct to avoid ambiguous constructor

* Tidy fix: emplace_back() over for loop

* Tidy fix: use move

* Use std::initializer_list in constructor
Reverts the dyn_data struct change
Should get around the ambiguous braced initialization list error

* avoid typedef

* element_space, min,max,opt _lens change

* formatting

* Comments fix

* dynamic bytes() test

* Seralize and reflect changes

* formatting

* Test the dynamic lens functions

* progress

* Formatting

* Dynamic conv draft progress

* Add operator<< tests for coverage

* Coverage update

* Add to conv dynamic batch test

* Dynamic image size test

* Dynamic weight handling

* Dyn image shape test change, fix dyn weight cond

* Comment update

* Dynamic weights shape test and fix

* Use ternary operator

* Tidy fixes

* Handle dynamic graph input shapes in ONNX parser

* Formatting

* Handle dynamic shape for convolution

* formatting

* cppcheck fixes

* Add onnx test files

* Fix typo

* Disable auto_pad for dynamic input shape

* check_shapes object checks for allowing dynamic shapes

* Fix any_of

* Change to maintain const objectness

* Formatting

* Check shapes allow dynamic

* Refactor compute_shape() call into op.compute()
Allows for per operator differences with handling dynamic shape
Fix operation.hpp change to use the generator

* Comment fix

* Refactor normalize_attributes() calls to use max_lens()

* Comment addition

* Update other normalize_attributes() calls

* Change to using constructor and add tests

* Use const member function

* Add more dynamic shape support

* Add tests for error code coverage

* Fix opt shape bug and add shape tests

* capture all by ref

* Fix typo with img shape calculation

* Add more tests

* dynamic auto pad attempt
Linker error with pad_calc.cpp

* Fix parse dyn auto_pad
Should only need to use dynamic auto pad when the image shape or kernel
shape are dynamic. For a dynamic batch size, the auto pad calculation is
the same.

* Fix linking error

* Fix auto_pad bug
Fixed input tensor with auto_pad setting on

* auto_pad onnx tests

* Fix auto_pad calculation, evaluate in ref_conv
add ref_ops tests

* Add shape tests, fix bugs

* Refactor first two output dynamic len calculation

* Conv MLIR test update

* i64 MLIR test fix

* Fix MLIR test typo
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

Gives better clarity to which argument is throwing an error, especially in cases with nested IF statements in the network.

instancenorm parser always creates literal of type float which would fail in type check while creating binary ops if model is fp16.

* Add in changes for onnx Mod operator

Initial operator for mod implementation and test cases for integer and floating based types.

Need to use fmod from stdlib for floating point types. half_float::half thankfully is specced to the use the existing std::fmod() call when looking at the half.hpp implementation.

fmod_flag should mirror the onnx fmod attribute. Right now using a floating point type without setting that on the user side to true will result in an exception.

Ref ticket #1283 

Depends on #1199

Adds ONNX parser functionality for dynamic input shapes.
Uses options parameter in parse_onnx()

Updated each source file in the repo with the existing license.

* Upgrade to cppcheck 2.8

As described in #1196, the ONNX mean parser does not work correctly for integral types. This update fixes the issue by handling integral types separately, where summation is performed before division. Additional test cases have also been added for handling integral types.