[Core] Use array to speedup padding

[peng1999]

Using array.array in SequenceData greatly improves performance of make_tensor_with_pad in Sampler.

Micro-benchmark using 1024 input length and 2048 batch size shows a great latency improvment (79ms to 22ms):

Before:

After:

End-to-end test on qwen-1.5-0.5b model also shows improvement on throughput:

main:

Processed prompts: 100%|███| 2048/2048 [01:22<00:00, 24.76it/s, est. speed input: 25352.26 toks/s, output: 3165.44 toks/s]

This PR:

Processed prompts: 100%|███| 2048/2048 [01:09<00:00, 29.44it/s, est. speed input: 30150.97 toks/s, output: 3764.60 toks/s]

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[github-actions]

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[casper-hansen]

Nice to see an 18% speedup from this optimization. Is it mainly for small models?

[peng1999]

Is it mainly for small models?

Yes. This PR is for small models and large batch sizes. The from_sampling_metadata function, optimized by this PR, primarily runs on the CPU and is independent of logists. Therefore, it can overlap with the GPU work of model inference. It will only be on the critical path if its execution time exceeds that of model inference, which occurs with smaller models.

[mgoin]

LGTM any concerns @youkaichao?

[daquexian]

Great PR! Would you mind sharing what tool you used to get this image, is it nsight system?

[peng1999]

Great PR! Would you mind sharing what tool you used to get this image, is it nsight system?

Yes. The blue spans are recorded using NVTX.

[youkaichao]

Thanks for the great job! Please merge the latest main to pass the tests.

I once tried to replace the whole prompt/output tokens to numpy array, but it involves changing too much code, so I gave it up due to limited bandwidth.

It's good to see this speedup with a self-contained change.

cc @alexm-neuralmagic if you are planning to change the underlying data structure in block managers.