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[Spec Decoding] Use target model max length as default for draft model #7706

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merged 1 commit into from
Aug 21, 2024

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@njhill njhill commented Aug 20, 2024

We currently assume a hard-coded default of 2k for the max model length in the case that it can't be inferred from the model's configuration.

In the case of spec decoding draft models, it would instead be better to assume the same max length of the target model in this case.

This is particularly helpful for MLPSpeculator models whose length can't be determined from their config.json files and should be assumed to match that of the corresponding target model that they were specifically trained for.

We currently assume a hard-coded default of 2k for the max model length in the case that it can't be inferred from the model's configuration.

In the case of spec decoding draft models, it would instead be better to assume the same max length of the target model in this case.

This is particularly helpful for MLPSpeculator models whose length can't be determined from their config.json files and should be assumed to match that of the corresponding target model that they were specifically trained for.
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👋 Hi! Thank you for contributing to the vLLM project.
Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which consists a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of default ones by unblocking the steps in your fast-check build on Buildkite UI.

Once the PR is approved and ready to go, please make sure to run full CI as it is required to merge (or just use auto-merge).

To run full CI, you can do one of these:

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@njhill njhill added the ready ONLY add when PR is ready to merge/full CI is needed label Aug 21, 2024
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LGTM

@DarkLight1337 DarkLight1337 merged commit 9b73a2f into vllm-project:main Aug 21, 2024
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@njhill njhill deleted the draft-default-len branch August 21, 2024 17:11
Alvant pushed a commit to compressa-ai/vllm that referenced this pull request Oct 26, 2024
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3 participants