Add lora_path argument to bench_multiturn.py#10092
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Summary of Changes
Hello @Fridge003, 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 enhances the bench_multiturn.py benchmarking script by introducing support for LoRA (Low-Rank Adaptation) paths. This allows users to benchmark the performance of specific LoRA adaptors, particularly in scenarios involving radix cache, by integrating the LoRA path directly into the request generation process.
Highlights
- New command-line argument for LoRA path: The bench_multiturn.py script now accepts a --lora-path argument, allowing users to specify a single LoRA adaptor for benchmarking.
- LoRA path integration into request payload: The gen_payload function has been updated to accept a lora_path argument, which is then included in the request payload sent to the SGLang server.
- Dynamic LoRA path usage in benchmarking: The lora_path provided via command-line is now dynamically passed to the gen_payload function during initial request generation and subsequent response handling within the benchmark process.
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Code Review
This pull request adds a --lora-path argument to bench_multiturn.py to facilitate benchmarking with LoRA adapters. The changes correctly thread this new argument through to the payload generation. However, I've found a critical scope issue where the args object is accessed in a method where it's not available, which will cause a runtime error. My review includes a comment detailing this issue and how to fix it.
| gen_payload( | ||
| self.client_records[client_id]["history"], | ||
| self.output_length, | ||
| args.lora_path, |
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The args variable is not accessible within the response_handler method. It's a local variable from the main execution block and is passed to WorkloadGenerator.__init__, but it is not stored as an instance attribute. Accessing args.lora_path here will result in a NameError.
To fix this, you should store lora_path on the instance (self) within the __init__ method and use self.lora_path here.
In WorkloadGenerator.__init__:
# In WorkloadGenerator.__init__(self, args)
self.lora_path = args.lora_pathThen, update this call to gen_payload:
gen_payload(
self.client_records[client_id]["history"],
self.output_length,
self.lora_path,
),
Motivation
Used for benchmarking lora feature with radix cache
Usage:
Modifications
Accuracy Tests
Benchmarking and Profiling
Checklist