bdfb065b5d
### What this PR does / why we need it?
1. Enable pymarkdown check
2. Enable python `__init__.py` check for vllm and vllm-ascend
3. Make clean code
### How was this patch tested?
- vLLM version: v0.9.2
- vLLM main:
29c6fbe58c
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Signed-off-by: wangli <wangli858794774@gmail.com>
32 lines
1.8 KiB
Markdown
32 lines
1.8 KiB
Markdown
## Online serving tests
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- Input length: randomly sample 200 prompts from [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json) and [lmarena-ai/vision-arena-bench-v0.1](https://huggingface.co/datasets/lmarena-ai/vision-arena-bench-v0.1/tree/main)(multi-modal) dataset (with fixed random seed).
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- Output length: the corresponding output length of these 200 prompts.
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- Batch size: dynamically determined by vllm and the arrival pattern of the requests.
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- **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
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- Models: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
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- Evaluation metrics: throughput, TTFT (median time to the first token ), ITL (median inter-token latency) TPOT(median time per output token).
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{serving_tests_markdown_table}
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## Offline tests
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### Latency tests
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- Input length: 32 tokens.
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- Output length: 128 tokens.
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- Batch size: fixed (8).
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- Models: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
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- Evaluation metrics: end-to-end latency.
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{latency_tests_markdown_table}
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### Throughput tests
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- Input length: randomly sample 200 prompts from [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json) and [lmarena-ai/vision-arena-bench-v0.1](https://huggingface.co/datasets/lmarena-ai/vision-arena-bench-v0.1/tree/main)(multi-modal) dataset (with fixed random seed).
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- Output length: the corresponding output length of these 200 prompts.
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- Batch size: dynamically determined by vllm to achieve maximum throughput.
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- Models: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
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- Evaluation metrics: throughput.
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{throughput_tests_markdown_table}
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