[Bugfix] Fix support for dimension like integers and ScalarType

[bnellnm]

Some of the custom ops take integers which are Tensor dimensions. These can sometimes be SymInts if those Tensor dimensions are marked as dynamic. The appropriate int arguments for these class of custom ops have been changed to SymInt to support dynamic dimensions.

The inductor is not currently able to support passing custom C++ classes to custom ops. This PR fully implements ScalarType in python. ScalarTypes are now passed by id to C++ where they are reconstructed into C++ ScalarTypes. This has the side effect of removing the _core_C extension.

fixes #9234

cc @SageMoore , @ProExpertProg , @LucasWilkinson , @youkaichao

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[LucasWilkinson]

Thanks for doing this! Sorry scalar type caused this issue, stinks that inductor doesnt support custom classes. But more red then green in this PR so an overall win I guess 👍. I am a bit worried about python based id function calls on the hot path as the id function can be a bit slow in python (im seeing about 14us)

although we do appear to saving an expensive _must_dispatch_in_python which appears to more than offset that,

but might still be good to lift the id call out of _custom_ops.py so users can optimize that if they please

[bnellnm]

Thanks for doing this! Sorry scalar type caused this issue, stinks that inductor doesnt support custom classes. But more red then green in this PR so an overall win I guess 👍. I am a bit worried about python based id function calls on the hot path as the id function can be a bit slow in python (im seeing about 14us) although we do appear to saving an expensive _must_dispatch_in_python which appears to more than offset that, but might still be good to lift the id call out of _custom_ops.py so users can optimize that if they please

I've memoized id and made sure it is populated when the types are constructed. If you can send me the commands you used to benchmark, I can see how much difference the memoization makes.

[LucasWilkinson]

I was using:

python examples/offline_profile.py --model neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 --batch-size 4 --prompt-len 512 --json trace_test --save-chrome-traces-folder chrome_traces --max-num-batched-tokens 8196

off this branch: #8337

Then opening prefill.json in chrome_traces using https://ui.perfetto.dev/, you have to merge the branches though so I just went ahead and re-ran it here:

looks good! 👍 not seeing any overhead, thanks!

overall I think this is actually much better than the original now! (assuming we won't need more fields and overflow 64bits haha), thanks!

[ProExpertProg]

Is there a way to make this more robust? What about Llama-3.1-8B and Llama-3.2-8B?

[bnellnm]

I'd rather the check be specific to the problem model(s) rather than making it more generic, e.g. once we upgrade to 2.5 we can remove the fp8 check. It's possible this llama model will also run without oom'ing.

[tlrmchlsmth]

RIP ScalarType

[tlrmchlsmth]

Should size_n and size_k be regular ints since they will be known at torch.compile time?

[tlrmchlsmth]

Ditto for all Ns and Ks

[youkaichao]

thanks for the great efforts! please address comments from @tlrmchlsmth ?

[tlrmchlsmth]

thanks for the great efforts! please address comments from @tlrmchlsmth ?

From an offline discussion with @bnellnm, it sounds like there's no downside to leaving N and K as SymInts -- good to go from my end. Interested to see if there's any documentation on SymInt though.