21 lines
1.1 KiB
Markdown
21 lines
1.1 KiB
Markdown
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.
|