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[Model] Systematic support for fp32 head, pooling models part (#23810)

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Signed-off-by: wang.yuqi <noooop@126.com>
This commit is contained in:
wang.yuqi
2025-09-09 22:29:50 +08:00
committed by GitHub
parent a55cf41a09
commit 19332c0479
14 changed files with 166 additions and 61 deletions
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37
tests/models/language/pooling/mteb_utils.py
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@@ -9,6 +9,7 @@ import mteb
import numpy as np
import pytest
import requests
import torch
from tests.models.utils import (EmbedModelInfo, RerankModelInfo,
check_embeddings_close)
@@ -165,16 +166,19 @@ def mteb_test_embed_models(hf_runner,
vllm_extra_kwargs=None,
hf_model_callback=None,
atol=MTEB_EMBED_TOL):
# A model family has many models with the same architecture,
# and we don't need to test each one.
if not model_info.enable_test:
# A model family has many models with the same architecture,
# and we don't need to test each one.
pytest.skip("Skipping test.")
example_prompts = ["The chef prepared a delicious meal."]
# Test embed_dims, isnan and whether to use normalize
example_prompts = ["The chef prepared a delicious meal." * 1000]
# Allow vllm to test using the given dtype, such as float32
vllm_extra_kwargs = vllm_extra_kwargs or {}
vllm_extra_kwargs["dtype"] = model_info.dtype
# Allow vllm to test using hf_overrides
if model_info.hf_overrides is not None:
vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
@@ -186,21 +190,32 @@ def mteb_test_embed_models(hf_runner,
model_config = vllm_model.llm.llm_engine.model_config
# Confirm whether vllm is using the correct architecture
if model_info.architecture:
assert model_info.architecture in model_config.architectures
# Confirm whether vllm uses the correct default_pooling_type, which
# relates to whether chunked prefill and prefix caching are enabled
assert (model_config._model_info.default_pooling_type ==
model_info.default_pooling_type)
vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
MTEB_EMBED_TASKS)
vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
vllm_outputs = vllm_model.embed(example_prompts)
# Test embed_dims, isnan and whether to use normalize
vllm_outputs = vllm_model.embed(example_prompts,
truncate_prompt_tokens=-1)
assert not torch.any(torch.isnan(torch.tensor(vllm_outputs)))
# Accelerate mteb test by setting
# SentenceTransformers mteb score to a constant
if model_info.mteb_score is None:
with hf_runner(model_info.name,
is_sentence_transformer=True,
dtype="float32") as hf_model:
# e.g. setting default parameters for the encode method of hf_runner
if hf_model_callback is not None:
hf_model_callback(hf_model)
@@ -299,14 +314,16 @@ def mteb_test_rerank_models(hf_runner,
hf_model_callback=None,
vllm_mteb_encoder=VllmMtebEncoder,
atol=MTEB_RERANK_TOL):
# A model family has many models with the same architecture,
# and we don't need to test each one.
if not model_info.enable_test:
# A model family has many models with the same architecture,
# and we don't need to test each one.
pytest.skip("Skipping test.")
# Allow vllm to test using the given dtype, such as float32
vllm_extra_kwargs = vllm_extra_kwargs or {}
vllm_extra_kwargs["dtype"] = model_info.dtype
# Allow vllm to test using hf_overrides
if model_info.hf_overrides is not None:
vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
@@ -319,9 +336,15 @@ def mteb_test_rerank_models(hf_runner,
model_config = vllm_model.llm.llm_engine.model_config
# Confirm whether vllm is using the correct architecture
if model_info.architecture:
assert (model_info.architecture in model_config.architectures)
# Score API is only enabled for num_labels == 1
assert model_config.hf_config.num_labels == 1
# Confirm whether vllm uses the correct default_pooling_type, which
# relates to whether chunked prefill and prefix caching are enabled
assert (model_config._model_info.default_pooling_type ==
model_info.default_pooling_type)
@@ -330,6 +353,8 @@ def mteb_test_rerank_models(hf_runner,
languages=MTEB_RERANK_LANGS)
vllm_dtype = model_config.dtype
# Accelerate mteb test by setting
# SentenceTransformers mteb score to a constant
if model_info.mteb_score is None:
st_main_score, st_dtype = mteb_test_rerank_models_hf(
hf_runner, model_info.name, hf_model_callback)