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[Benchmark][Bug] Fix multiple bugs in bench and add args to spec_decode offline #20083
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[Benchmark][Bug] Fix multiple bugs in bench and add args to spec_decode offline #20083
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Summary of Changes
Hello @ekagra-ranjan, 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 primarily focuses on enhancing the robustness and configurability of the benchmarking and offline inference systems. It addresses critical bugs in the random dataset generation for benchmarks, ensuring more accurate simulation of prompt caching and TTFT analysis. Additionally, it introduces new command-line arguments to provide greater control over model paths and speculative decoding configurations in offline inference, and adds a backend selection option for the benchmark server.
Highlights
- Benchmark Dataset Bug Fix: Corrected a bug in the
RandomDataset
generation where thetotal_input_len
was incorrectly calculated, leading to an overestimation of prefix cache hits. The fix ensures that the encoded sequence truncation correctly accounts for both prefix and input lengths. - Benchmark Reproducibility: Implemented random seed initialization for
RandomDataset
using bothrandom
andnumpy
to ensure reproducible benchmark results, especially important for simulating prompt caching and TTFT analysis. - Offline Inference Configurability: Added command-line arguments (
--model-dir
,--eagle-dir
,--max-model-len
) tospec_decode.py
, allowing users to specify the main model, Eagle draft model, and maximum model length directly, enhancing flexibility for offline inference benchmarks. - Benchmark Server Backend Option: Introduced a
--backend
argument tovllm/benchmarks/serve.py
, enabling explicit selection of the backend (e.g.,vllm
,tgi
,hf
) for benchmark runs, which is useful when passing specific sampling parameters.
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Code Review
The pull request addresses a bug in the random dataset generation, adds arguments for backend selection in serve.py, and includes model configuration options for offline inference. The changes improve the accuracy of benchmark simulations and provide more flexibility in specifying model directories and configurations.
cc: @robertgshaw2-redhat since a change on similar note was made in #19868 |
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Thanks for the bug fixes!
Thanks for the quick review! |
…de offline (vllm-project#20083) Signed-off-by: Will Eaton <weaton@redhat.com>
…de offline (vllm-project#20083) Signed-off-by: avigny <47987522+avigny@users.noreply.github.com>
vllm/vllm/benchmarks/datasets.py
Line 380 in 0d06b53
[input_len * (1 - range_ratio), input_len * (1 + range_ratio)]
as per this