sgl-project/sglang

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

Open

#9.674 aberto em 27 de ago. de 2025

Ver no GitHub
 (6 comments) (0 reactions) (0 assignees)Python (6.216 forks)auto 404
good first issuehelp wanted

Métricas do repositório

Stars
 (28.442 stars)
Métricas de merge de PR
 (Mesclagem média 2d 1h) (1.000 fundiu PRs em 30d)

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.

Guia do colaborador