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@ekagra-ranjan ekagra-ranjan commented Jun 25, 2025

  1. add random seed in random dataset. This is needed if we want to simulate prompt caching and ttft analysis
  2. fix random dataset bug:
    prompt, add_special_tokens=False)[:input_lens[i]]
    Currently it picks 1st input len num of tokens which means if prefix len is 32k and input len is 32k then it will pick 1st 32k tokens which is all the prefix len instead of all 64k. This leads to ~98% prefix cache hit. Similar bug was fixed in [Benchmark][Bugfix] Fix Dataset Length Calculation #19868 but the original intention of RandomDataset is to have prefix and input len independent of each other so that context length can be sampled from [input_len * (1 - range_ratio), input_len * (1 + range_ratio)] as per this
  3. add --backend in serve.py for cases when sampling params are passed
  4. add model config, eagle dir in offline inference

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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 the total_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 both random and numpy 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) to spec_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 to vllm/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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@mergify mergify bot added documentation Improvements or additions to documentation performance Performance-related issues labels Jun 25, 2025
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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.

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ekagra-ranjan commented Jun 25, 2025

cc: @robertgshaw2-redhat since a change on similar note was made in #19868

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Thanks for the bug fixes!

@ywang96 ywang96 added the ready ONLY add when PR is ready to merge/full CI is needed label Jun 25, 2025
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Thanks for the quick review!

@ekagra-ranjan ekagra-ranjan requested a review from ywang96 June 26, 2025 04:16
@ywang96 ywang96 merged commit 9502c38 into vllm-project:main Jun 26, 2025
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m-misiura pushed a commit to m-misiura/vllm that referenced this pull request Jun 26, 2025
gmarinho2 pushed a commit to gmarinho2/vllm that referenced this pull request Jun 26, 2025
xjpang pushed a commit to xjpang/vllm that referenced this pull request Jun 30, 2025
wseaton pushed a commit to wseaton/vllm that referenced this pull request Jun 30, 2025
…de offline (vllm-project#20083)

Signed-off-by: Will Eaton <weaton@redhat.com>
wseaton pushed a commit to wseaton/vllm that referenced this pull request Jun 30, 2025
wwl2755-google pushed a commit to wwl2755-google/vllm that referenced this pull request Jul 1, 2025
avigny pushed a commit to avigny/vllm that referenced this pull request Jul 31, 2025
…de offline (vllm-project#20083)

Signed-off-by: avigny <47987522+avigny@users.noreply.github.com>
googlercolin pushed a commit to googlercolin/vllm that referenced this pull request Aug 29, 2025
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