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Implements dynamic shapes in reduce_op and all its child operator classes (reduce_max etc.)

No major changes required, use dyn_output and pass dynamic shape when calling compute_shape()
Adds dynamic shape tests

Extends the Argmax operator to handle dynamic input shapes.
Only shape function changes

Extends unsqueeze and squeeze to work for dynamic input shapes
Does not handle the steps parameter
Adds some additional negative axes shape tests

Fix problem with the contiguous operator constructing non-standard shape literals.  A non-standard literal will almost never be used, since a literal is known at compile time.  Added some comments on the intended behavior:

- literal{shape, vector} constructor with a non-standard shape is intended to keep the same ordering as the given vector. The data buffer will be populated such that when the non-standard indexing is used the original order is as given.
- literal{shape, argument} constructor directly copies the data buffer from the argument
- Changed non-standard literal fill() to use tensor_view iterators as it handles non-standard shapes now
- Changed the contiguous ref_ops_test to be more helpful

Extends the pooling operators for dynamic shape inputs

AveragePooling
GlobalAveragePooling
MaxPooling
GlobalMaxPooling
LpNormPooling
GlobalLpNormPooling
y.github.com>

Extends ref transpose operator for dynamic shapes
Make dynamic tests more consistent naming

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

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

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

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

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

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.

Rewrites the TF batch norm like operators to other MIGX operators
Removes the code related to batch_norm_inference

Upgrade cppcheck to 2.9 

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

* 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>

* 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 

Allows PyTorch converted version of SSD-resnet34 to work

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

Add ref and gpu implementations for ONNX op GatherND

Resolves #1032

Refactored the reference implementation of pooling to something like what was done for roialign. Moved the reference implementation of pooling from targets/ref/lowering.cpp to pooling.hpp.
Removed cpu_pooling, instead using reference pooling in pooling.hpp
Added reference implementation of Lp Norm pooling and the global version
Added tests for the Lp Norm Pooling

Change the "scatter" struct and op to a base/child set of three: scatter_none, scatter_add, scatter_mul to mirror Onnx' ScatterElements op. and its three reduction options. (Onnx Scatter op is deprecated and is equivalent to scatter_none.)

Provides both a reference op. and update to Onnx parsing. Tests updated and new test case added.

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.

Resolves a problem in parsing the ssd-10 model.

The problem is, after inserting contiguous in the auto_contiguous pass, standard output shape of some operators becomes non-standard. Then, if the next operator requires standard input shape, an exception is throw.

For example, if we pass the following model:
Input (standard shape) -> transpose (transposed) -> softmax (transposed) -> transpose (standard) -> gather.
It works fine, and no contiguous is required.

In the auto_contiguous pass, a contiguous is inserted after the first transpose. Then we need to replace the first transpose with the contiguous and recompute all shapes. When it comes to the gather operator, its input is a transposed shape, and an exception is thrown.

The solution is in the recompute_shape() function. If it is called by the auto_contiguous pass and shape of an instruction is changed, and the shape is non_standard, we do not recompute shape of its output. The reason is: since its output shape is non_standard, a contiguous op will be added after the instruction, which will recompute shape for later operators.

This PR is the ref implementation of the nonmaxsuppression operator. It always returns the max possible output shape, which is the problem tracked in issue #948.

Implementation of the roialign operator. For now, we have only the ref implementation. When we run a model on the GPU, we fall back the execution to use the ref implementation.

Adds a pass to fuse pointwise operators into one "pointwsie" op that has a submodule which does the calculation.

This PR is for the nonzero operator with static output shape.  
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

Add multinomial op to onnx parser with ref and GPU implementations.

The onnx parser inserts a literal of shape {batch_size, sample_size} with random values in the range [0, 1) and inserts existing ops to compute the cumulative density function. The multinomial operator multiplies the random values by the sum of the CDF and returns the index of the first element of the CDF that is greater than the result, representing samples randomly drawn from [0, class_size) that follow the log-probability distribution.

Resolves #821
Co-authored-by: Shucai Xiao <shucai@gmail.com>

Add operators, refactor parsers, add rewrite passes, add tests
Add ref implementations
Move broadcasting of scales and zero points to onnx parser
Allow for x and zero_point to have different types in quantizelinear; fix zero_point default type
fp16 and fp8 quantization to include subgraph and parameters
fix unit test to use qdq operators for int8 quantization
Co-authored-by: turneram <alturner@amd.com>

Implement the Where operator for the CPU and GPU.  This is for better performance.

* add topk operator doe ref, cpu and gpu
* Hash modules for quicker lookup of modules
* add onnx unit test
* add unit tests for the topk operator
Co-authored-by: Paul <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>

* rename broadcast and multibroadcast output_lens attribute to out_lens attribute, and change tests and source code to reflect the same

* change the reshape attribute from dims to out_lens

* change transpose attribute's name from dims to perm to reflect better meaning

* use permutation instead of perm for transpose

clang formaating

* use dims instead of out_lens for reshape

clang formatting

* Add operators, refactor parsers, add rewrite passes, add tests

* Formatting

* Fix cppcheck

* Review comments

* Formatting

* Combine rewrite passes

* Formatting

* Add ref implementations

* Formatting

* Review comments

* Formatting

* Tidy warnings

* Apply review comments

* Formatting

* Fix CI error

* Formatting

* Increase code coverage

* Formatting

* Move broadcasting of scales and zero points to onnx parser

* Formatting

* Allow for x and zero_point to have different types in quantizelinear; fix zero_point default type

* Formatting

* Increase code coverage

* Formatting

* Switch certain variables to int64_t

* Formatting

* Fix overflow in implicit constant conversion

* Formatting

* Increase code coverage

* Formatting

* Remove operators.hpp from includes in tf_test.cpp

* Formatting

* Add conversion for int32 input to quantizelinear and add test case; remove operators.hpp from onnx_test.cpp includes

* Formatting

* Switch dequantizelinear math from int32 to float

* Formatting

* Remove changes to operators.hpp

* Simplify apply_quantizelinear

* Formatting

* Add verify test for int32 data

* Add rewrite_quantization back to CMakeLists

Use C++ functions instead of hardcoded gold vales for comparison with MIGraphX ops test evaluation (#863)

* use c++ functions instead of hardcoded gold vales for comparison with migraphx

* binary_ops transform

* fix cppcheck

* format fixes

* rebase and fix tidy

tidy fixes

formatting

* float type trigonometric functions

* formatting

* tidy fixes and review comments

clang format

* tidy fix

* Add definitions for all pointwise operators

* Formatting

* Add cpp generator class

* Formatting

* Move compilation to core

* Formatting

* Add clock to tmp name

* Add dynamic loader

* Formatting

* Add tests for code gen

* Formatting

* Add test for literals

* Formatting

* Use with_char

* Add missing header

* Fix mismerge

* Ignore tidy warning

* Fxx gcc 5 errors

* Apply fixits

* Skip signed bitwise of status

* Remove unused parameters

* Explicitly add c++14 flag

* Fix tidy warning

* unify the compute function signature

* clang format

* make another change

* unify the compute function

* clang format

* remove unnecessary code

* more refinement about the operator compute funciton

* clang format

* add an overload function

* clang format

* add support for axes inputs for sequeeze/unsqueeze/reduce_sum

* clang format

* fix build problems

* backup code changes

* clang format

* Add tuple type to shape class

* Formatting

* fix a bug in parsing quantizelinear operator

* clang format

* fix a cppcheck error

* disable different versions of unit tests for different onnx version

* clang format

* upgrade onnx to 1.8

* update onnx to 1.8.1

* disable two more real models

* clang format

* Make data member private

* Formatting

* Add sub arguments

* Formatting

* Trun clang format off

* Disable clang-format

* fix review comments

* fix the function of assign axes in parsing the squeeze operator

* add unit tests and fix a bug

* clang format

* fix review comments

* clang format

* fix a build error

* backup code changes

* clang format

* add more unit tests and add parsing opset version

* clang format

* Improve visiting tuples

* Formatting

* fix cppcheck error

* adding installing the onnx package

* resolve no protobuf compiler

* add an inline subgraph pass

* clang format

* Add more argument tests

* Formatting

* Handle tuple in load

* Formatting

* code backup

* clang format

* Remove .o files

* Add tuple type to api

* Formatting

* fix build errors

* clang format

* code backup

* code backup

* add unit tests for the inline subgraph

* clang format

* refine the inline subgraph and parse if operator

* clang format

* fix cppcheck issue

* clang format

* add unit test for inline subgraph pass

* clang format

* fix format issue

* remove the context from the if operator

* clang format

* simplify the compute functions

* Fix tidy warnings

* fix cppcheck error

* clang format

* fix cppcheck error

* Fix tidy warnings

* fix a cppcheck error

* clang format

* Add a test for share method

* Formatting

* Add a test cpp_type

* add unit tests for more code coverage

* clang format

* add unit tests to have more code coverage

* clang format

* try a comment in jenkins build

* include the install onnnx line

* code backup

* reorder the dependenciesd installed

* refine dockerfile

* fix review comments

* clang format

* remove unnecessary overload function

* fix cppcheck error

* change back the argument test

* Suppress tidy warning

* add the operator get_tuple_elem

* clang format

* add get_tuple_elem to operator include file

* chang if to support multiple operation outputs

* clang format

* optimize inline subgraph

* clang format

* code backup

* clang format

* fix bug

* refine unit tests for tuple output of the if operator

* clang format

* refine a instruction replacement code

* add a unit test and sort all the unit tests alphabetically

* fix cppcheck error

* add more unit tests for multiple op outputs

* clang format

* fix cppcheck error

* Update pass manager to get modules after every pass

* more unit test to cover more scenarios

* clang format

* fixed a bug in a unit test

* add more tests

* clang format

* add more unit tests to have more code coverage

* fix a bug in a unit test

* Add program overload for module

* Formatting

* Hash modules for quicker lookup of modules

* Bump file version

* Add methods to remove modules

* Formatting

* add the tuple type to the support list

* Eliminate unused modules

* Formatting

* Fix test errors

* Foramtting

* Fix tidy issues

* fix problem related to inline subgraph

* clang format

* fix review comments

* fix review comments

* fix review comments

* fix review comments

* clang format

* fix a unit test

* one more code change

* remove an optimization related to the if operator

* clang format

* fix review comments
Co-authored-by: Paul <pfultz2@yahoo.com>
Co-authored-by: mvermeulen <5479696+mvermeulen@users.noreply.github.com>