1.1 KiB
1.1 KiB
Objectives
- Auto LLM inference config tuner
Key Results
- [9/10] Build the first version auto tuner system
- [7/10] Check the current situation of parallelism config optimization
- [4/10] Understand the possibility/challenges in LLM inference compute graph arrangement automatically
- [0/10] Trace vLLM compute graph and data flow
- [3/10] Implement a minimal Rust inference framework
- [1/10] Define the IR for automatic optimization
- [5/10] Profile different parallelism setup with real trace and analysis their difference
- [0/10] Meta-analysis for the theory maximum improvement with heterogenous setup [offtrack]
Last Week
- Refine the story. Focus on heterogenous workloads are classified by labels or input length, which is not enough. We should define a classification method through the grouping of similar performance under the same config.
- Prepare slides to summarize the story and what to do next.
- Prepare slides for IPADS group meeting.
Next Week
- Run benchmark for current workload classification to prove different classes need different configs to max the goodput.