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MIGraphX verification by default uses normalized RMS error as the basis for the verification.  This change adds some logic to allow migraphx to do "np.allclose" type of elementwise verification using atol and rtol.

Commit also includes changes to consistently pass "gold" or "expected" results as the second argument for "verify_range()" calls.  Default RMS tolerance inside driver is set to 0.001 which IMO is high for FP32 compared to what we had earlier. Need better defaults

Modify reshapes to use reshape_lazy for aliasing and then reshape for a reshape copy operation to eliminate contiguous

Currently, the is_mlir_conv predicate wasn't being used when
offloading standalone convolutions to MLIR on Navi3x, which caused
failures relating to being unable to construct the MLIR program when a
3D convlolution was passed in.

This commit amends the standalone lowering to use said predicate, as
well as to include quant_convolution and quant_dot into the set of
operations that get a standalone lowering.

Add mlir_logger, which registers a MLIR diagnostic handler that
captures any information generated by a MLIR compile and saves it to a
string.

This will be useful during tuning, where some such errors may be the
result of an inapplicable tuning configuration and should be
suppressed.

New compiler pass that simplifies dynamic shapes related operators to their static versions if possible
Will normally be used after a split_single_dyn_dim pass

Skip verifying literals and parameters
Print the correct trim value
Print failure for an exception and continue reduce
Print if shapes do not match

update matcher, update tests

* Rectify flipped coordinate_transformation_mode logic in ROIAlign
* Handle both opset 10 and 16 versions
* Fix version check and clang tidy warning
Co-authored-by: Dino Musić <dino.music@htecgroup.com>

Add parsing support for value_float, value_floats, value_int, value_ints attributes
Disable failing tests
Resolves Test failures due to IndexError: _Map_base::at migraphx-benchmark/AMDMIGraphX#76

This resolves an edge case found in multiple huggingface models  in some cases the find_split_reshape matcher will match with reshape2, but vec_rsp will consist of reshape1 dims causing a shape mismatch error. Solution is to include rsp when checking all reshapes are the same.

let the user know which targets migraphx was built for and how to build migraphx for their gpu.

Implements a fill operator that sets the values in an output buffer to a given value
Will be used when parsing ONNX ConstantOfShape
Can also be used when a buffer needs to be filled with a value that is determined at runtime

MIOpen fusions are not serialized with tuned solutions. Print warnings for such cases.

New op that populates a shape with random numbers with a uniform distribution. The rand_uniform op. can implement the Onnx RandomUniform instruction, and can also create the random number sequence necessary to implement Multinomial. (At this time, our Onnx Multinomial parsing generates a random sequence of numbers when parsing as a workaround, so that the resulting program uses the same "random" set every time.)

Arguments: shape, seed. Shape is required; can be static or dynamic. Seed is still optional in this version. If it's not given at inference time, use the value in the creation attribute seed. Update: deleted A boolean use_auto_seed causes any given seed to be ignored.

Useful for CI purposes as well as keeping track in our .deb creation which
version of Onnxruntime MIGraphX has had CI tested against it.

In addition, since the bug in the sigmoid test is resolved, also uncomment that temporary disable.

Makes a version of allocate that takes in dimensions and allocates a buffer
Going to create a simplify_dynamic_ops compiler pass that will use the use_shape_attr flag
The ONNX op ConstantOfShape needs the buffer to be filled with a specific value, so going to make another PR for that and a fill operator

Reduce memory footprint by std::move of temporary (potentially very large) containers.
Minor cleanup for performance optimization: e.g. of Index() calculation -- which can get repeated millions of times in large tensors/vectors in a single Visit.

Adds support for dynamic input shape in pooling operator along with auto-padding. This combination requires that the padding (and therefore the output shape) can't be computed until runtime.