Remove int_scaled_mm's dependency on triton for cpu

[Xia-Weiwen]

In #121, the CPU version of int_scaled_mm was implemented by @torch.library.impl in torchao/kernel/intmm_triton.py. It requires triton installed to call this op. However, the CPU implementation should not depend on triton. This PR moves the implementation to torchao/kernel/intmm.py and does not use @torch.library.impl.
AUTOTUNER_ENABLE is not required anymore, either. (Not sure if this is reasonable)

Test is still covered by UT in test/kernel/test_autotuner.py.

[Xia-Weiwen]

Hi @cpuhrsch Could you please review and see if the changes are reasonable to you? Thanks.

[Xia-Weiwen]

Hi @cpuhrsch Could you please suggest how to deal with the issue (CPU impl availability depends on triton and AUTOTUNER_ENABLE)? Thanks!

[cpuhrsch]

Hey @Xia-Weiwen - Thank you for the PR! Sorry for the delay in review. Also, please note the CI hasn't run green.

Another way to resolve this could be to move

@torch.library.impl(lib, "int_scaled_matmul", "CPU")
def int_scaled_matmul_cpu(a, b, scales1):
    c = torch._int_mm(a, b)
    return c.to(scales1.dtype) * scales1

into torchao/kernel/intmm.py which shouldn't have a dependency on triton. Just be sure to also define lib = torch.library.Library("torchao", "FRAGMENT")

[Xia-Weiwen]

@cpuhrsch Thanks! I will give it a try. A question is what AUTOTUNER_ENABLE is and whether CPU impl should depend on it or not.

[cpuhrsch]

@Xia-Weiwen - it's used for a Triton autotuner that allows us to cycle over a very large number of configs for a given fixed input shape. See https://github.com/pytorch-labs/ao/tree/main/torchao/kernel#autotuner-and-custom-triton-kernels

[Xia-Weiwen]

Thank you @cpuhrsch. Looks like CPU impl does not need this.