21 lines
1.1 KiB
Markdown
21 lines
1.1 KiB
Markdown
Objectives
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- Auto LLM inference config tuner
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Key Results
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- [6/10] Build the agentic tuner system
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- [10/10] Build the first version auto tuner system
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- [2/10] Workload grouping methods
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- [8/10] Check the current situation of parallelism config optimization
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- [4/10] Understand the possibility/challenges in LLM inference compute graph arrangement automatically
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- [1/10] Define the IR for automatic optimization
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- [5/10] Profile different parallelism setup with real trace and analysis their difference
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Last Week
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- [KR1] Update agentic AITuner to support new trace benchmark / new vLLM flags/ objective score. [0a012bdd](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/0a012bdda53086cd24277962abb0cb559bd313bb) ~ [788da3d8](https://ipads.se.sjtu.edu.cn:1312/wangjh/auto-tuner/-/commit/788da3d8bc546620e8c76800dfb7070372cb3540)
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- [KR1] Survey the related works. Some works build an agent for LLM training / storage system / ..., a work use BO for LLM inference config tuning.
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- [misc] Prepare an open-sourced version of new traces (thinking and coder) and update readme.
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Next Week
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- Optimize the agentic AITuner.
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- Test SCOOT as one of the baseline.
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