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:

• for int8 we want a separate geomean (i.e. geomean of bfloat16, and geomean of int8). What's the best way to keep int8 and bfloat16 separate? I can introduce an environment variable and run the benchmark twice - once only bfloat16, once only int8. Open to other suggestions.
• for onednn comparison, there is no problem with bfloat16 but there is no support for GPU matmul w/ int8. I don't think we want to run the comparison vs CPU (it takes too long and gives us no info), so I might need to introduce the env variable anyway to do one run which is float16 w/ onednn, and another run which is int8 w/out onednn.

cc #3014