Feat/multiturn

[tttennnnn]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @tttennnnn, 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 introduces a significant enhancement by adding multiturn benchmarking capabilities, allowing for a more comprehensive evaluation of model performance in conversational contexts. It integrates a new run_multiturn function to orchestrate these benchmarks across various providers and models, ensuring that relevant performance metrics are collected and visualized. Additionally, a small but important refactoring was made to improve variable naming within the provider interface for better code clarity.

Highlights

• Multiturn Benchmarking Feature: A new run_multiturn method has been introduced in benchmark_main.py to enable multiturn benchmarking for evaluating model performance over sequential interactions.
• Provider-Specific Multiturn Calls: The run_multiturn method now includes conditional logic to call provider.perform_multiturn, with a special case for the vLLM provider to pass an additional vllm_ip argument.
• Metric Plotting for Multiturn: The newly added run_multiturn function incorporates logic to plot various performance metrics such as response_times, timetofirsttoken, totaltime, and timebetweentokens, depending on whether streaming is enabled.
• Variable Renaming for Clarity: A minor refactoring was performed in provider_interface.py where the variable gen_tokens was renamed to target_tokens within the data_handler function to improve code readability.

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[gemini-code-assist]

Code Review

This pull request introduces a new run_multiturn benchmark capability. While the feature addition is a good step, there are several areas for improvement. The most critical issue is that the new perform_multiturn method is not defined in the ProviderInterface, which will likely lead to runtime errors. Additionally, the new method continues a problematic pattern of special-casing the vLLM provider, violating polymorphism principles. I've also identified code duplication in the metrics plotting logic and a hardcoded time interval that should be made configurable. Addressing these points will improve the robustness, maintainability, and flexibility of the benchmarking framework.

[gemini-code-assist]

The perform_multiturn method is called on provider instances, but it is not defined in the ProviderInterface abstract base class. This will cause an AttributeError at runtime for any provider that does not implement this method. To ensure all providers adhere to the expected contract, you should add perform_multiturn to ProviderInterface, likely as an @abstractmethod.

Additionally, the special handling for vLLM by checking provider_name and passing an extra vllm_ip argument violates the Liskov Substitution Principle and indicates a design issue. This pattern is repeated across run, run_trace, and now run_multiturn. A better approach would be to configure the vLLM provider with its IP address during its initialization. This would allow all provider methods to be called with a consistent signature, eliminating the need for these if/else blocks.

[gemini-code-assist]

The time_interval is hardcoded to 15 seconds. This reduces the flexibility of the multi-turn benchmark. Consider making this a configurable parameter, either by passing it to run_multiturn or by adding it as an attribute to the Benchmark class, initialized from the configuration file.

[gemini-code-assist]

This block of code for plotting metrics is duplicated in run(), run_trace(), and now in run_multiturn(). To improve maintainability and reduce redundancy (following the DRY principle), you should extract this logic into a private helper method, for example, _plot_results(), and call it from all three run* methods.