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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)

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.

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

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

This reverts commit 390bb853.

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

* Improve type printing in driver
* Improve error with incorrect order for command
* Add spell checking of arguments
* Add validations and required checking
* Add required arguments and groups

Added an Accuracy checker to the tools directory.  Currently compares ONNX FP32 models against ORT CPUEP

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.

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 

* 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

* Add performance check per commit

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

Remove specific person name from deb created packages and move toward a general maintainer id/email

Dynamic shape handling in shape object

Bug 1: create_literal was using back_inserter to copy vector with already allocated size, causing double the size of literal.
Fix 1 : not use back_inserter
Bug 2: Input param to model can be from operation that has multiple output, in that case name of the input param would contain : e.g. input_1:0
Fix 2: Look for : and take substring

Depends on #1199

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

Changes to operator includes:

removed some includes that were not used
included argument.hpp where clang-tidy wanted it

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.

Reduce header inclusion in op headers

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.

Enable the migraphx-dev package when using make|rbuild package

bitbucket needs a port that some servers do not make available. Move the Blaze dependency from a bitbucket to a github source repo.

* Only run __syncthreads when there is data to preload
* Improve loops
* Add const attribute to improve optimizations

Show the number of operators and per operator avg time in summary...

Summary:
gpu::gemm: 8.738ms / 73 = 0.119699ms, 64%
gpu::triadd_layernorm: 0.831381ms / 24 = 0.0346409ms, 7%

Improve the assembly dump to track where certain instruction come from.

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