[core][optimization] use a pool of numpy ndarray to hold seq data#5942
[core][optimization] use a pool of numpy ndarray to hold seq data#5942youkaichao wants to merge 14 commits into
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| output_token_ids: Optional[List[int]] = None, | ||
| ) -> None: | ||
| self.tokens = _SEQUENCE_DATA_POOL.alloc_array() | ||
| self.prompt_token_ids_list = prompt_token_ids |
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Is there any opportunity to get rid of this list (and output token ids list)? This is completely duplicated to the numpy arrays and we should avoid that as possible.
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I want to delete it, too. However, sometimes we need to get the list of int of prompt token ids because users want list of int. If we don't store it here, we need to create a copy from numpy array, which is expensive.
Fortunately, this is just a reference, performance-wise it is fine.
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I searched the code base and seems like only batch expansion uses get_prompt_token_ids() and get_output_token_ids(), so it should be possible, as batch expansion is going to be removed by @LiuXiaoxuanPKU
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| def append_token_id(self, token_id: int, logprob: float) -> None: | ||
| self.output_token_ids.append(token_id) | ||
| self.tokens[self.num_prompt_tokens + self.num_output_tokens] = token_id |
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Ideally we should have an assertion to check the boundary, even 128k should always be sufficient atm. Let's add an assert if it doesn't hurt performance; otherwise we could just comment that we assume the context length won't go beyond 128k.
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I think numpy array indexing already has boundary check.
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I want to somehow know the max seq length in seqdata, but don't know how to pass that information across so many levels.
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Setting a fixed length makes sense to me considering the simplicity. Hmm maybe it's ok to keep the current implementation then. If someone really hits the boundary and see the numpy error, we could know what's going on...
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The remaining part of #5877 after separating #5882 out.
the same benchmark command:
python benchmarks/benchmark_throughput.py --output-len 256 --input 256 --model meta-llama/Llama-2-7b-hf -tp 8the same machine: 8*H100
before (current main): Throughput: 38.89 requests/s, 19909.29 tokens/s
after (this PR): Throughput: 40.12 requests/s, 20541.11 tokens/s