[PERF] Use faster way of decode in tokenizer: avoid useless list-to-list conversion

[vadiklyutiy]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.

Purpose

to_py_obj is called inside transformers tokenizer.decode. In our case to_py_obj convert list to list what is useless.
Avoid to_py_obj to speedup decoding.

Test Plan

Change is really short and trivial.

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[gemini-code-assist]

Summary of Changes

Hello @vadiklyutiy, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a performance optimization to the token decoding process within the tokenizer utility. By intelligently selecting the most efficient decoding method available, it aims to eliminate redundant data conversions and improve the speed of token decoding.

Highlights

• Performance Optimization: Modified the decode_tokens function in vllm/transformers_utils/tokenizer.py to prioritize using the _decode method of the tokenizer if it exists, falling back to the standard decode method otherwise. This change aims to bypass an unnecessary list-to-list conversion (to_py_obj) that can occur during token decoding, thereby improving performance.

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[gemini-code-assist]

Code Review

The pull request modifies the decode_tokens function in vllm/transformers_utils/tokenizer.py to use the _decode method of the tokenizer if it exists, falling back to the decode method if it doesn't. This is done to potentially speed up decoding by avoiding unnecessary list-to-list conversions. The change is concise and seems reasonable.

[22quinn]

Interesting. This is the code pointer for anyone interested.

I feel this kind of optimization is better done in huggingface. I dig a bit and found there was already some discussion and optimization in huggingface/transformers#36885

Have you measured the speedup for this PR?

[vadiklyutiy]

Interesting. This is the code pointer for anyone interested.

I feel this kind of optimization is better done in huggingface. I dig a bit and found there was already some discussion and optimization in huggingface/transformers#36885

Have you measured the speedup for this PR?

@22quinn you are right. This change from my backlog and I did it some time ago. I measured performance without patch to HF you mentioned and that saw a lot of to_py_obj calls for every list element. I will check performance improvement on the latest version. Maybe after HF patch performance improvement too minor to worry about it.
Thank you for pointing this out.

[ProExpertProg]

Congrats on #20000!

[DarkLight1337]

Oops accidentally merged this PR, feel free to revert if there's a problem with it

[vadiklyutiy]

@DarkLight1337
Should I create PR to revert it?

[DarkLight1337]

Is this change still relevant? If not then yeah let's revert

[vadiklyutiy]

Ok, let's me collect up to date numbers. Mentioned above merge to transformers improved performance but not fully - there is still some overhead. With specific numbers we can decide.

[vadiklyutiy]

@DarkLight1337
Sorry for late reply.
Ran Qwen-2.5-VL-3B with high load on latest main with and without this PR.
decode_token itself speed up is sufficient - 28%.
But after transformers optimizations we don't spend a lot of time in it. E2E improving is tiny - around 0.2%.
Please let me know what do you think.

[DarkLight1337]

OK, let's revert this PR then. Thanks for investgating this!