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[Model] Enable optional prefix when loading embedding models (#10639)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung 2024-11-26 02:14:33 +08:00 committed by GitHub
parent b1d920531f
commit cf73f0c95e
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5 changed files with 20 additions and 13 deletions
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9
vllm/model_executor/models/bert.py
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@ -14,18 +14,17 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.pooler import (CrossEncodingPooler, Pooler,
PoolingType)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import SupportsCrossEncoding
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from vllm.transformers_utils.config import (
get_cross_encoder_activation_function)
from .utils import maybe_prefix
from .interfaces import SupportsCrossEncoding
from .utils import WeightsMapper, maybe_prefix
class BertEmbedding(nn.Module):
@ -442,6 +441,8 @@ class BertEmbeddingModel(nn.Module):
return self._pooler(hidden_states, pooling_metadata)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
weights = hf_to_vllm_mapper.apply(weights)
self.model.load_weights(weights)
def _build_model(self,

4
vllm/model_executor/models/gemma2.py
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@ -42,7 +42,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, extract_layer_index,
from .utils import (AutoWeightsLoader, WeightsMapper, extract_layer_index,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@ -511,4 +511,6 @@ class Gemma2EmbeddingModel(nn.Module, SupportsPP):
return self._pooler(hidden_states, pooling_metadata)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
weights = hf_to_vllm_mapper.apply(weights)
self.model.load_weights(weights)

5
vllm/model_executor/models/llama.py
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@ -53,7 +53,8 @@ from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors, PoolerOutput
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@ -689,6 +690,8 @@ class LlamaEmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
return self._pooler(hidden_states, pooling_metadata)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
weights = hf_to_vllm_mapper.apply(weights)
self.model.load_weights(weights)
def load_kv_cache_scales(self, quantization_param_path: str) -> None:

12
vllm/model_executor/models/qwen2.py
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@ -50,7 +50,8 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
@ -585,8 +586,7 @@ class Qwen2EmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
) -> Optional[PoolerOutput]:
return self._pooler(hidden_states, pooling_metadata)
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
loader = AutoWeightsLoader(self,
ignore_unexpected_prefixes=["lm_head."])
return loader.load_weights(weights)
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
weights = hf_to_vllm_mapper.apply(weights)
self.model.load_weights(weights)

3
vllm/model_executor/models/roberta.py
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@ -11,13 +11,14 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.bert import BertEmbeddingModel, BertModel
from vllm.model_executor.models.interfaces import SupportsCrossEncoding
from vllm.model_executor.models.utils import maybe_prefix
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from vllm.transformers_utils.config import (
get_cross_encoder_activation_function)
from .interfaces import SupportsCrossEncoding
class RobertaEmbedding(nn.Module):