biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Model] Re-add the implicit conversion feature for as_seq_cls_model (#21103)

Browse Source 
Signed-off-by: wang.yuqi <noooop@126.com>
This commit is contained in:
wang.yuqi
2025-07-18 15:15:07 +08:00
committed by GitHub
parent ba2dfbb0c2
commit ca4eb82bcb
11 changed files with 165 additions and 75 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
vllm/model_executor/models/adapters.py
View File

@@ -331,13 +331,13 @@ def load_weights_using_from_2_way_softmax(
false_id = tokenizer.convert_tokens_to_ids(tokens[0])
true_id = tokenizer.convert_tokens_to_ids(tokens[1])
weight = model.lm_head.weight.data[[true_id]].to(
score_weight = model.lm_head.weight.data[[true_id]].to(
torch.float32) - model.lm_head.weight.data[[false_id]].to(
torch.float32)
param = model.score.weight
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, weight)
weight_loader(param, score_weight)
del model.lm_head
loaded_weights.add("score.weight")
@@ -350,6 +350,8 @@ def load_weights_no_post_processing(model,
torch.Tensor]]):
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead)
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader)
from vllm.model_executor.models.utils import AutoWeightsLoader
model_config = model.vllm_config.model_config
@@ -357,8 +359,6 @@ def load_weights_no_post_processing(model,
tokens = cast(list[int], tokens)
assert len(tokens) > 0
device = model.score.weight.device
if model.config.tie_word_embeddings:
model.lm_head = model.model.embed_tokens
else:
@@ -376,8 +376,11 @@ def load_weights_no_post_processing(model,
trust_remote_code=model_config.trust_remote_code)
token_ids = [tokenizer.convert_tokens_to_ids(t) for t in tokens]
score_weight = model.lm_head.weight.data[token_ids].to(device)
model.score.weight.data.copy_(score_weight)
score_weight = model.lm_head.weight.data[token_ids]
param = model.score.weight
weight_loader = getattr(param, "weight_loader", default_weight_loader)
weight_loader(param, score_weight)
del model.lm_head
loaded_weights.add("score.weight")

4
vllm/model_executor/models/gemma.py
View File

@@ -43,7 +43,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .adapters import as_seq_cls_model
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
@@ -426,6 +425,3 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)
GemmaForSequenceClassification = as_seq_cls_model(GemmaForCausalLM)

4
vllm/model_executor/models/llama.py
View File

@@ -49,7 +49,6 @@ from vllm.model_executor.model_loader.weight_utils import (
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .adapters import as_seq_cls_model
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
is_pp_missing_parameter,
@@ -646,6 +645,3 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
name = name.replace(item, mapping[item])
return name, loaded_weight
LlamaForSequenceClassification = as_seq_cls_model(LlamaForCausalLM)

4
vllm/model_executor/models/qwen2.py
View File

@@ -50,7 +50,6 @@ from vllm.model_executor.model_loader.weight_utils import (
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .adapters import as_seq_cls_model
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
is_pp_missing_parameter,
@@ -496,6 +495,3 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)
Qwen2ForSequenceClassification = as_seq_cls_model(Qwen2ForCausalLM)

4
vllm/model_executor/models/qwen3.py
View File

@@ -44,7 +44,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from .adapters import as_seq_cls_model
from .interfaces import SupportsLoRA, SupportsPP
from .qwen2 import Qwen2MLP as Qwen3MLP
from .qwen2 import Qwen2Model
@@ -320,6 +319,3 @@ class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)
Qwen3ForSequenceClassification = as_seq_cls_model(Qwen3ForCausalLM)

37
vllm/model_executor/models/registry.py
View File

@@ -12,7 +12,7 @@ import sys
import tempfile
from abc import ABC, abstractmethod
from collections.abc import Set
from dataclasses import dataclass, field
from dataclasses import asdict, dataclass, field
from functools import lru_cache
from typing import Callable, Optional, TypeVar, Union
@@ -181,10 +181,6 @@ _CROSS_ENCODER_MODELS = {
"ModernBertForSequenceClassification": ("modernbert",
"ModernBertForSequenceClassification"),
# [Auto-converted (see adapters.py)]
"GemmaForSequenceClassification": ("gemma", "GemmaForSequenceClassification"), # noqa: E501
"Qwen2ForSequenceClassification": ("qwen2", "Qwen2ForSequenceClassification"), # noqa: E501
"Qwen3ForSequenceClassification": ("qwen3", "Qwen3ForSequenceClassification"), # noqa: E501
"LlamaForSequenceClassification": ("llama", "LlamaForSequenceClassification"), # noqa: E501
"JinaVLForRanking": ("jina_vl", "JinaVLForSequenceClassification"), # noqa: E501,
}
@@ -462,10 +458,26 @@ class _ModelRegistry:
return _try_load_model_cls(model_arch, self.models[model_arch])
def _try_inspect_model_cls(self, model_arch: str) -> Optional[_ModelInfo]:
if model_arch not in self.models:
return None
if model_arch in self.models:
return _try_inspect_model_cls(model_arch, self.models[model_arch])
return _try_inspect_model_cls(model_arch, self.models[model_arch])
if model_arch.endswith("ForSequenceClassification"):
causal_lm_arch = model_arch.replace("ForSequenceClassification",
"ForCausalLM")
if causal_lm_arch not in self.models:
return None
info = _try_inspect_model_cls(causal_lm_arch,
self.models[causal_lm_arch])
info = _ModelInfo(**dict(
asdict(info), **{
"architecture": model_arch,
"supports_cross_encoding": True
}))
return info
return None
def _normalize_archs(
self,
@@ -480,6 +492,15 @@ class _ModelRegistry:
normalized_arch = list(
filter(lambda model: model in self.models, architectures))
# try automatic conversion in adapters.py
for arch in architectures:
if not arch.endswith("ForSequenceClassification"):
continue
causal_lm_arch = arch.replace("ForSequenceClassification",
"ForCausalLM")
if causal_lm_arch in self.models:
normalized_arch.append(arch)
# make sure Transformers backend is put at the last as a fallback
if len(normalized_arch) != len(architectures):
normalized_arch.append("TransformersForCausalLM")