GitLab

* add fix for the trace_eval

* Add throw for the debug builds

* Formatting

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

#1791 Added hash function for value class. It uses the Visit function and has specialization for the bool_type and <vector> type but was missing specialization for the nullptr. Nullptr caused compilation issues for RHEL, SLES and CentOS.

Enable with MIGRAPHX_ENABLE_CK=1 and --exhaustive-tune tune flag

Sigmoid approximation for GeLU was introduced in #1299 for Fp16. The sigmoid approximation is known to get better perf but lower accuracy. https://arxiv.org/pdf/1606.08415.pdf

Changed the doc for find_permutation(shape) to be more clear that it is finding the permutation that would make the shape standard

By converting to fp32 : fp16 3d-unet model accuracy comes out the same as FP32 accuracy.

By using reduce_sum method on Fp16 : accuracy comes out ~0.9% lower compared to fp32 while keeping entire model in fp16.

partially solves #1656
This PR only handles compilation part of multitarget.

Use generate_argument instead of generate_literal for python output as generate_literal doesnt exists
Shorten the names for variables from the main module
Use prefix p_ for parameters
Use shorter variable m for main module in python

* Allow quantizing for both int8 and fp16

Use std::numeric_limits::min/max() functions plus the appropriate value to encode -inf/inf 

Enable retrieving the code object to do tuning in the future.

Refactor supported gfx archs

back out changes for rocm-5.5

* use half hip functions to compute max and min
* add verify test for min and max

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

Co-authored-by: Sam Wu <sam.wu2@amd.com>
Co-authored-by: Paul <pfultz2@yahoo.com>

Move CI to support the rocm5.5 release

Adding support for broadcasted scalars to unsqueeze op.

Specifying steps other than 1 is disallowed in this implementation since we want the output the always be a tensor. We can support varying step sizes if we allow a broadcasted scalar output from this op.

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

Example of using the C++ API to run an ONNX model with dynamic batch

Remove various file not required for what we use Github runners for

Python API with documentation updates