sgl-project/sglang

[Feature] Create benchmark and dataset usage scripts for embedding models

Open

#9 674 ouverte le 27 août 2025

Voir sur GitHub
 (6 commentaires) (0 réactions) (0 assignés)Python (6 216 forks)auto 404
good first issuehelp wanted

Métriques du dépôt

Stars
 (28 442 stars)
Métriques de merge PR
 (Merge moyen 2j 1h) (1 000 PRs mergées en 30 j)

Description

Look at the following for related tests, and the benchmark script should be similar to bench_serving Existing Tests:

models/test_embedding_models.py — Backend, 73s — PR: core embedding model implementations (covers srt/models/*_embedding.py, srt/entrypoints/openai/serving_embedding.py).

models/test_encoder_embedding_models.py — Backend, 100s — Post-merge: encoder-based embedding models (BERT/Roberta-style).

models/test_cross_encoder_models.py — Backend, 100s — Post-merge: cross-encoder/reranker models (related to embedding-based tasks but a different architecture).

openai_server/basic/test_serving_embedding.py — Frontend/unit, 10s — Run on all PRs: embedding serving layer (validates the serving path).

openai_server/basic/test_openai_embedding.py — Frontend, 141s — Frontend (PR/post-merge as listed): end-to-end OpenAI embedding API endpoints.

test_input_embeddings.py — Backend, 38s — Post-merge: tests the input embedding handling/path.

Guide contributeur