[Tests] Update online DP tests to verify that requests are balanced #20157
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Summary of Changes
Hello @njhill, 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 significantly enhances the testing suite for data parallel (DP) configurations by introducing comprehensive checks to ensure that incoming requests are effectively balanced across different data parallel ranks. It addresses a previously identified gap in test coverage by leveraging both internal custom statistics logging for the AsyncLLM interface and external Prometheus metrics for the API server, providing robust verification of request distribution.
Highlights
- Enhanced Data Parallel Tests: New tests and modifications are introduced to verify that requests are balanced evenly across data parallel ranks in both
AsyncLLMand API server contexts, addressing a previous gap in test coverage. - Prometheus Metrics Integration: For API server tests, new utility functions (
get_prometheus_metrics,get_engine_request_counts) are added to fetch and parse Prometheus metrics, specificallyvllm:request_success_total, to determine request distribution per engine. - Custom Stats Logger for AsyncLLM: A
SimpleStatsLoggeris implemented and integrated intoAsyncLLMtests to track and verify request completion counts per engine, ensuring proper balancing within the internal engine. - Request Balancing Assertions: Assertions are added to both API server and
AsyncLLMtests to confirm that all data parallel engines receive requests and that the distribution of these requests is reasonably balanced.
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| @@ -38,7 +38,7 @@ def default_server_args(): | |||
| ]]) | |||
| def server(default_server_args, request): | |||
| if request.param: | |||
| default_server_args.extend(request.param) | |||
| default_server_args = default_server_args + request.param | |||
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Code Review
This pull request adds tests to verify that requests are balanced across data parallel ranks, which is a great addition for ensuring the correctness of the DP implementation. The approach of using Prometheus metrics for the API server and a custom stats logger for the AsyncLLM interface is sound. The code is generally well-written. I've provided a few suggestions to improve the robustness and maintainability of the new test code.
Signed-off-by: Nick Hill <[email protected]>
Signed-off-by: Nick Hill <[email protected]>
Signed-off-by: Nick Hill <[email protected]>
Signed-off-by: Nick Hill <[email protected]>
Signed-off-by: Nick Hill <[email protected]>
…llm-project#20157) Signed-off-by: Nick Hill <[email protected]>
…llm-project#20157) Signed-off-by: Nick Hill <[email protected]>
…llm-project#20157) Signed-off-by: Nick Hill <[email protected]>
…llm-project#20157) Signed-off-by: Nick Hill <[email protected]> Signed-off-by: avigny <[email protected]>
…llm-project#20157) Signed-off-by: Nick Hill <[email protected]>
This was a gap in our current DP tests.
Updated to use metrics to verify that requests are balanced evenly between the data parallel ranks.
Via custom stats logger in the AsyncLLM interface case and external prometheus metrics in API server case.