Add support for the /rerank endpoint in vllm bench serve (#26602)

Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
This commit is contained in:
Maximilien de Bayser
2025-10-14 01:25:43 -03:00
committed by GitHub
parent 29350922c6
commit fe3edb4cf0
3 changed files with 218 additions and 6 deletions

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@@ -35,6 +35,7 @@ th {
| Sonnet (deprecated) | ✅ | ✅ | Local file: `benchmarks/sonnet.txt` |
| Random | ✅ | ✅ | `synthetic` |
| RandomMultiModal (Image/Video) | 🟡 | 🚧 | `synthetic` |
| RandomForReranking | ✅ | ✅ | `synthetic` |
| Prefix Repetition | ✅ | ✅ | `synthetic` |
| HuggingFace-VisionArena | ✅ | ✅ | `lmarena-ai/VisionArena-Chat` |
| HuggingFace-MMVU | ✅ | ✅ | `yale-nlp/MMVU` |
@@ -878,6 +879,51 @@ vllm bench serve \
</details>
#### Reranker Benchmark
Benchmark the performance of rerank requests in vLLM.
<details class="admonition abstract" markdown="1">
<summary>Show more</summary>
Unlike generative models which use Completions API or Chat Completions API,
you should set `--backend vllm-rerank` and `--endpoint /v1/rerank` to use the Reranker API.
For reranking, the only supported dataset is `--dataset-name random-rerank`
Start the server:
```bash
vllm serve BAAI/bge-reranker-v2-m3
```
Run the benchmark:
```bash
vllm bench serve \
--model BAAI/bge-reranker-v2-m3 \
--backend vllm-rerank \
--endpoint /v1/rerank \
--dataset-name random-rerank \
--tokenizer BAAI/bge-reranker-v2-m3 \
--random-input-len 512 \
--num-prompts 10 \
--random-batch-size 5
```
For reranker models, this will create `num_prompts / random_batch_size` requests with
`random_batch_size` "documents" where each one has close to `random_input_len` tokens.
In the example above, this results in 2 rerank requests with 5 "documents" each where
each document has close to 512 tokens.
Please note that the `/v1/rerank` is also supported by embedding models. So if you're running
with an embedding model, also set `--no_reranker`. Because in this case the query is
treated as a individual prompt by the server, here we send `random_batch_size - 1` documents
to account for the extra prompt which is the query. The token accounting to report the
throughput numbers correctly is also adjusted.
</details>
[](){ #performance-benchmarks }
## Performance Benchmarks