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* Horizontally fuse contiguous

* 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>

Currently, while copying a host buffer to the device, it first registers/maps the host buffer pointer to address space of the device.

If the host buffer has been allocated by the hipHostMalloc then, it is implicitly registered to the device's address space, and no need to register again. This PR adds a check for the same.

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 

* Add is_supported to the target
* Add get_target_assignments
* Rename assignment to target_assignments
* Add ref target header to test
* Add fpga target
* Make context const in compute

C++ API is not printing thrown exception string. this improves on it.

Dynamic shape handling in shape object

Depends on #1199

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

Fix min_test.onnx generation as well as add a proper check to the parse program vs the expect program.
Adding this in to fix test converge for the min case.

This will ensure that migraphx.h can be included from a C compiler, and check that the C API can be called. This includes stdbool.h which is needed when using bool from C.

Added is_supported and get_target_assignments methods to the target and program, respectively, to eventually support multi-target compilation and execution.

Initial sketch for changes to shape to handle dynamic dimensions

Instead of just unsqueezing to an axis of 1 a step can be set to use instead. So instead of unsqueezing {3, 12} to {3, 1, 12} a step of 2 will unsqeeze to {3, 2, 6} instead

*In the verification tests, check that saving and reloading the program is the same program. This also fixes serialization to always load instructions in the same order. There is also fixes for deconv and quant_conv which didn't save the solution id, and was broken for serialization.

This reorders the transposes across slice to improve horizontal fusion for contiguous. This also improves eliminate_contiguous to remove contiguous better across splits.

* Add mlir c api

* Formatting

* Create a type attribute

* Formatting

* Parse module

* Formatting

* Add mlir dump function

* Add test case

* Formatting

* Fix tidy issues

* Update mlit version

* Update to newer mlir

* Format

* Move mlir to the gpu and update the test

* Formatting

* Fix bug when appending module

* Format

* Remove old cmake flag

* Update message

* Add return

* Format

* Add mlir_compile

* Format

* Register dialect

* Handle unsinged integers

* Dont provide output for return instruction

* Format

* Add code to insert memrefs

* Format

* Add mlir verification

* Formatting

* Enable pointwise_fusion

* Disable eliminate_data_type

* Set kernal name

* Format

* Fix device name

* Formatting

* Fix output arg

* Format

* Updates

* Upate hash

* Add fuse_mlir pass

* Format

* Add fuse mlir

* Format

* Update mlir

* Sort parameter names

* Format

* Reenable disabled passes

* Remove old mlir conv

* Remove asym default padding

* Add more verbose tracing

* Format

* Fix compilation errors

* Format

* Whitelist operators

* Format

* Add namespace

* Format

* Update triple

* Format

* Use func dialect

* Format

* Use func.return

* Format

* Upgrade mlir version

* Add comment

* Handle symetrical padding

* Format

* Cleanup debug output

* Format

* List failed tests

* Move mlir compile to jit pipeline

* Format

* Update version

* Add source locations

* Format

* Correctly add module

* Format

* Update failed tests

* Fix failures when mlir is disabled

* Format

* Update mlir version

* Check type for fp32

* Format

* Remove failed test

* Update mlir in driver

* Tidy fixes

* Foramt

* Tidy fixes

* Format

* Fix const

* Remove from requirements

* Fix cmake version

* Fix tidy warning

* Use another ifdef

* Fix tidy

* Other tidy fix

* Format

* Update hash

* Add missing license files

* Format

* Format

* Fix fnction name

This is an extension to insert_module_instructions, but instead of just inserting from a module, it can insert a range or a vector of instructions.

Allows PyTorch converted version of SSD-resnet34 to work

* Jit contiguous

Used to determine what files contain a license and are stamped. If not we exit and return an error code that can be later ingested by another script, as well as a list of the outstanding files in questions.

Currently baked in the list of files we should support or not support with licenses in them a well as some stuff to quickly ignore

Adds compute_method for the experimental custom ops.
Adds a test for the same using HIP APIs.
Depends on #1183
Solves #1101

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

* [#935] Update tf_parser to have add_common_op() for parse_relu6

Similar to that of the onnx_parser.cpp add a add_common_op template and functionality to support clip based operations. This is done so clip operations can be guarenteed to have the same dimensions.

* fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* fixup! fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* fixup! fixup! fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* fixup! fixup! fixup! fixup! [#935] Update tf_parser to have add_common_op() for parse_relu6

* Formatting

* fixup! Formatting
Co-authored-by: Umang Yadav <29876643+umangyadav@users.noreply.github.com>
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* add allocate op header

* formatting

* add replace_allocate pass

* formatting

* move output param to remove_allocate pass

* formatting

* fix bugs in replace_allocate pass

* formatting

* fix verify if tests

* formatting

* move if op logic

* formatting

* cleanup lowering

* cleanup lowering

* formatting

* fix tidy

* formatting

* fix tidy

* add cpu allocate check

* formatting

* change cpu allocate in pass

* formatting

* add some tests for replace_allocate pass

* formatting

* pass by ref

* fix run_pass

* formatting

* update variable name for module

* update dce to use contains() and fix tidy

* formatting

* update cppcheck

* add if test

* formatting

* add if test

* rename var to mod_output_names

* formatting

* remove conditional

* update allocate op and tests

* formatting

* update replace_allocate tests

* update create_output_names() and conditional in replace_allocate

* formatting

* remove extra variable in replace_allocate

* update tools script for allocation_model
Co-authored-by: Umang Yadav <29876643+umangyadav@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* Use custom distance function

* Pass module, skip order check if other module

* Change other valid()

* Remove unnecessary declaration

* test multiple module dependency

* Refactor to make more clear

* Code cleanup

* Simplify fix

* Test EXPECT
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

prioritizing int8 over int8x4 when it is applicable
Amend return to continue in apply loop
Adding error handling in case int8x4 compilation failed
Co-authored-by: Paul Fultz II <pfultz2@yahoo.com>

* Upgrade to cppcheck 2.8

* Improve applicable batched gemms for bert

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.

Fuse layernorm and added triadd_layernorm fusion.  This is a prep performance booster

Expose add_literal method in C/C++ api

Add compile tests for gpu math functions

Helps avoid dangling references. This also deprecates the constructors that didnt take a lifetime annotation since its ambiguous the lifetime.

Add ref and gpu implementations for ONNX op GatherND

Resolves #1032