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Remove the need to use gpu, switch this to ref.
change names to reflect static vs random data

In this case we have a batch size with no bound on the score threshold.
We end up evaluating a single huge batch on its own.

The concern here is this should just all the way through without completely
stalling or intractably running in a single thread fashion currently.

This saves us two copies of the entire box class to this call and instead
works on reference of these objects that are created within the loops instead

We're continually creating/destroying batch box in the while() check as we
run through the boxes_heap() by calling batch_box() constantly.

Make this next_box and only calculate it before we pop that box from the boxes_heap.

should get rid of function overhead of constant calls in the case of a large
batch size

Just quickly return if either boxes have zero area. Searching for intersection
and union is irrelevant here logically.

Expose the shape::type_t values to be used by the python api and is required by torch_migraphx to support torchbench models.

This removes the --cxx flags from the rbuild commands since it is not necessary. Also added a section about using rbuild to set up an environment for development.

Adds a matcher to split_single_dyn_dim to find all broadcast or multibroadcast with two static shape inputs and replaces the instruction with the one input version.
Sorts the get_output_parameters() list to ensure the correct ordering. (Was getting an error for some models.)

* Enable hiprtc by default

Converts can be inserted when the scales and input differ in the onnx file(we are already doing this implicit conversion in the ref implementation). This will also improve the compile-time of quantizelinear.hpp since we can remove the nested visit method.

Examples..

bin/driver verify /codes/onnx_models/resnet50-v1-7/resnet50-v1-7.onnx --split-single-dyn-dim --batch 3 --dyn-input-dim @data "[{min:1, max:4}, 3, 224, 224]"

bin/driver compile /codes/onnx_models/resnet50-v1-7/resnet50-v1-7.onnx --split-single-dyn-dim --default-dyn-dim "{min:1, max:10}" --output resnet50_batch1-10.mxr

bin/driver perf resnet50_batch1-10.mxr --batch 4

Automatically fuse multiple reductions and pointwise operations.

* Add MIGRAPHX_VALIDATE_MATCHES env variable to validate each matcher

This will replace conv(x+a, w) with conv(x, w) + conv(a, w) where a is a constant so conv(a, w) can be replaced with a constant.

Bug found due to failing torch benchmark. Added test case to reproduce issue causing the model to error out on compile.
Original logic results in the following error:
AMDMIGraphX/src/include/migraphx/op/unsqueeze.hpp:128: normalize_compute_shape: UNSQUEEZE: Axis dimenstion is not divisible by step

Makes the optimals into a std::set<std::size_t>
Changes shape object functions to handle the opts change
Changes to convolution, flatten, pooling, and convolution in that they no longer calculate the output optimal dimensions. Instead returns empty opts. Will need to change this in the future if we want to support dynamic shapes fully.
Many changes to tests and shape calls with respect to the new optimals

Adds the promote_literals compiler pass that moves literals from the submodules to the main module.
With the eliminate_common_subexpression pass, it will remove copies of literals created during split_single_dyn_dim.
Pass is enabled with the split_single_dyn_dim compile option.

Adds a new GPU compiler pass split_single_dyn_dim that handles when one input parameter has a single non-fixed dynamic_dimension.
commonly occurs for dynamic batch or BERT sequence length
Splits the dynamic shape into several submodules will static input parameters to handle all of the cases in the dynamic_dimension range.
Essentially does what I manually did for the select_module verify tests
Adds a compile option split_single_dyn_dim that toggles the pass on/off. Defaults to false.
Updates verify_program.hpp and run_verify.cpp to allow for the tests to change the compile_options

* Add hiprtc driver

* Remove version from check_context and bump program version

* [MLIR] add dot offloads with manual tuning support
* This commit adds dot + pointwise fusion support
along with manual tuning using rocMLIR.

Co-authored-by: Chris Austen <causten@users.noreply.github.com>

Useful to get more insight into Onnxruntime. Allows us to reuse the accuracy checker code while also allowing us to capture Execution Provider output with the --ort_run and --ort_logging flags

Also added the --target flag as well to allow us to force using either a specific target for the accuracy checking. Originally this was defaulting to the GPU. This now allows us to use ref, fpga, etc to quickly change targets.

prevent dynamically loading the target library that is not compiled with the same version of MIGraphX core lib.

Refactor to have select_module use output parameters
Disable select_module verify tests on cpu