A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 14, 05:30 UTC
[CI] Pooling models mteb test uses enforce_eager (#22878)
Signed-off-by: wang.yuqi <noooop@126.com>
wang.yuqi committed
Aug 15, 2025 at 16:16 UTC
5406ebf5c9606b717ee11a40db8ba56d8c8dde17
1 file changed
+5
-1
tests/models/language/pooling/mteb_utils.py
+5
-1
@@ -18,7 +18,7 @@ from tests.models.utils import EmbedModelInfo, RerankModelInfo
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# - Different model results in differences more than 1e-3
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# 1e-4 is a good tolerance threshold
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MTEB_EMBED_TASKS = ["STS12"]
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-MTEB_EMBED_TOL = 1e-4
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+MTEB_EMBED_TOL = 0.02
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# See #19344
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MTEB_RERANK_TASKS = ["NFCorpus"]
@@ -175,6 +175,7 @@ def mteb_test_embed_models(hf_runner,
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=None,
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+ enforce_eager=True,
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**vllm_extra_kwargs) as vllm_model:
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model_config = vllm_model.llm.llm_engine.model_config
@@ -198,6 +199,7 @@ def mteb_test_embed_models(hf_runner,
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st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
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st_dtype = next(hf_model.model.parameters()).dtype
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+ print("Model:", model_info.name)
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print("VLLM:", vllm_dtype, vllm_main_score)
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print("SentenceTransformers:", st_dtype, st_main_score)
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print("Difference:", st_main_score - vllm_main_score)
@@ -286,6 +288,7 @@ def mteb_test_rerank_models(hf_runner,
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runner="pooling",
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max_model_len=None,
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max_num_seqs=8,
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+ enforce_eager=True,
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**vllm_extra_kwargs) as vllm_model:
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model_config = vllm_model.llm.llm_engine.model_config
@@ -304,6 +307,7 @@ def mteb_test_rerank_models(hf_runner,
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st_main_score, st_dtype = mteb_test_rerank_models_hf(
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hf_runner, model_info.name, hf_model_callback)
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+ print("Model:", model_info.name)
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print("VLLM:", vllm_dtype, vllm_main_score)
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print("SentenceTransformers:", st_dtype, st_main_score)
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print("Difference:", st_main_score - vllm_main_score)