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Add benchmark dataset for mlperf llama tasks #20338
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Add benchmark dataset for mlperf llama tasks #20338
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Signed-off-by: mgoin <mgoin64@gmail.com>
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Summary of Changes
Hello @mgoin, 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 vllm
benchmarking capabilities by integrating official MLPerf Llama datasets. It introduces a new dataset class that intelligently processes these datasets, formats prompts using chat templates, and dynamically sets expected output lengths based on reference answers. Furthermore, it adds a useful logging feature to visualize the distribution of prompt and output token lengths, aiding in performance analysis.
Highlights
- New Dataset Support: Added support for MLPerf Llama2 and Llama3.1 datasets within the
vllm
benchmarking framework, allowing users to specify these datasets forbench serve
runs. This enables more standardized and relevant performance evaluations for Llama models. - Dynamic Output Lengths: Implemented logic to dynamically determine the expected output length for each sample based on the tokenized length of the reference answer provided in the MLPerf datasets. This ensures that benchmark scenarios accurately reflect real-world generation lengths.
- Chat Template Integration: Incorporated the use of the tokenizer's chat template to format system and user prompts from the MLPerf datasets into a conversational style. This aligns the benchmark requests with how these models are typically interacted with in chat-based applications.
- Benchmarking Insights: Introduced a new logging feature that generates and displays histograms of prompt and output token lengths. This provides valuable insights into the distribution of request sizes during benchmark runs, aiding in performance analysis and bottleneck identification.
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Code Review
This pull request successfully integrates the MLPerf inference datasets for Llama models, enhancing the benchmark capabilities. The new MLPerfDataset
class correctly handles prompt formatting and output length determination. A useful histogram logging feature has been added to provide insights into token length distributions. The changes are well-structured and follow existing patterns within the codebase.
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: Patrick von Platen <patrick.v.platen@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: avigny <47987522+avigny@users.noreply.github.com>
Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: x22x22 <wadeking@qq.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: Jinzhen Lin <linjinzhen@hotmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: Paul Pak <paulpak58@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com> Signed-off-by: Diego-Castan <diego.castan@ibm.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
Adds support for running the mlperf inference datasets for llama-2-70B and llama-3.1-405B through vllm's bench serve script. Datasets: https://huggingface.co/datasets/mgoin/mlperf-inference-llama2-data and https://huggingface.co/datasets/mgoin/mlperf-inference-llama3.1-data
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