xinyun/vllm
1
0
Fork 0
mirror of https://git.datalinker.icu/vllm-project/vllm.git synced 2025-12-09 04:54:56 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Bugfix] Fix encoder-only model support for transformers backend (#28021)

Browse Source 
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Isotr0py 2025-11-05 14:24:41 +08:00 committed by GitHub
parent 428bc7bf1c
commit 0ff05e3770
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 16 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
tests/models/registry.py
View File

@ -899,27 +899,27 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
_TRANSFORMERS_BACKEND_MODELS = {
"TransformersEmbeddingModel": _HfExamplesInfo(
"BAAI/bge-base-en-v1.5", min_transformers_version="5.0.0"
"BAAI/bge-base-en-v1.5", min_transformers_version="5.0.0.dev"
),
"TransformersForSequenceClassification": _HfExamplesInfo(
"papluca/xlm-roberta-base-language-detection",
min_transformers_version="5.0.0",
min_transformers_version="5.0.0.dev",
),
"TransformersForCausalLM": _HfExamplesInfo(
"hmellor/Ilama-3.2-1B", trust_remote_code=True
),
"TransformersMultiModalForCausalLM": _HfExamplesInfo("BAAI/Emu3-Chat-hf"),
"TransformersMoEForCausalLM": _HfExamplesInfo(
"allenai/OLMoE-1B-7B-0924", min_transformers_version="5.0.0"
"allenai/OLMoE-1B-7B-0924", min_transformers_version="5.0.0.dev"
),
"TransformersMultiModalMoEForCausalLM": _HfExamplesInfo(
"Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="5.0.0"
"Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="5.0.0.dev"
),
"TransformersMoEEmbeddingModel": _HfExamplesInfo(
"Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0"
"Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0.dev"
),
"TransformersMoEForSequenceClassification": _HfExamplesInfo(
"Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0"
"Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0.dev"
),
"TransformersMultiModalEmbeddingModel": _HfExamplesInfo("google/gemma-3-4b-it"),
"TransformersMultiModalForSequenceClassification": _HfExamplesInfo(

2
tests/models/test_transformers.py
View File

@ -82,7 +82,7 @@ def test_models(
from packaging.version import Version
installed = Version(transformers.__version__)
required = Version("5.0.0")
required = Version("5.0.0.dev")
if model == "allenai/OLMoE-1B-7B-0924" and installed < required:
pytest.skip(
"MoE models with the Transformers backend require "

10
vllm/model_executor/models/transformers/base.py
View File

@ -28,6 +28,7 @@ from transformers import AutoModel
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
from vllm.attention import Attention, AttentionType
from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
from vllm.config.utils import getattr_iter
from vllm.distributed import get_pp_group, get_tp_group
from vllm.distributed.utils import get_pp_indices
@ -317,7 +318,7 @@ class Base(nn.Module, VllmModel, SupportsQuant, SupportsLoRA, SupportsPP):
# vLLM does not support encoder-decoder models, so if any encoder layer is
# found in a text only model, we assume the whole model is an encoder model
if has_encoder(self.model) and not is_multimodal(self.config):
self.check_version("4.57.0.dev0", "encoder models support")
self.check_version("5.0.0.dev0", "encoder models support")
attn_type = AttentionType.ENCODER_ONLY
else:
attn_type = AttentionType.DECODER
@ -336,7 +337,12 @@ class Base(nn.Module, VllmModel, SupportsQuant, SupportsLoRA, SupportsPP):
):
per_layer_sliding_window = self.config.sliding_window
attention_instances[i] = Attention(
attn_cls = (
EncoderOnlyAttention
if attn_type == AttentionType.ENCODER_ONLY
else Attention
)
attention_instances[i] = attn_cls(
num_heads=num_heads,
head_size=head_size,
# NOTE: We use Llama scale as default, if it's set by

2
vllm/model_executor/models/transformers/moe.py
View File

@ -115,7 +115,7 @@ direct_register_custom_op(
class MoEMixin(MixtureOfExperts):
def __init__(self, *, vllm_config: "VllmConfig", prefix: str = ""):
self.check_version("4.57.0.dev0", "MoE models support")
self.check_version("5.0.0.dev0", "MoE models support")
# Skip MixtureOfExperts.__init__ and call the next class in MRO
super(MixtureOfExperts, self).__init__(vllm_config=vllm_config, prefix=prefix)