GitLab

Remove both workarounds and the llvm threading patch that Jungwook thought might be hiding the problem.

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

Adds support for slice,transpose,contigous and reshape fusions into input tensors for a fused mlir kernel.

add option to print tf supported ops
sort both onnx and tf ops alphabetically

When multiplying either the input or output across the K dimensions then the multiple can be applied to the constant which can then be folded with propagate_const.