Add int8 to gemm w/ addmatrix and consider onednn provider #3040
+60
−42
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Update the gemm addmatrix benchmark to support int8 inputs as well as bfloat16.
The int8 benchmark is pretty slow - not because Triton performance is bad (it is at least on par with bfloat16) but because PyTorch does not support int8 matmul on GPU, so we have to do the matmul on the GPU. This makes the benchmark something like 20x slower. To fix that, I changed the PyTorch accuracy check to only run for a few shapes instead of all the shapes - I tried to pick shapes that I thought were representative of different cases but am open to suggestions. Now the benchmark runs in reasonable time.
A few open items need to be addressed:
cc #3014