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[Model] Add AutoWeightsLoader support for BERT, RoBERTa (#20534)

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Signed-off-by: Jennifer He <islandhe@gmail.com>
Signed-off-by: <islandhe@gmail.com>
Signed-off-by: Jen H <islandhe@gmail.com>
This commit is contained in:
Jennifer He
2025-07-15 01:34:24 -04:00
committed by GitHub
parent 91b3d190ae
commit 85bd6599e4
2 changed files with 59 additions and 100 deletions
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73
vllm/model_executor/models/bert.py
View File

@@ -22,12 +22,11 @@ from vllm.model_executor.layers.pooler import (ClassifierPooler, Pooler,
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.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from .interfaces import SupportsCrossEncoding, SupportsQuant, SupportsV0Only
from .utils import WeightsMapper, maybe_prefix
from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
class BertEmbedding(nn.Module):
@@ -44,9 +43,11 @@ class BertEmbedding(nn.Module):
config.type_vocab_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.position_ids = nn.Parameter(
torch.empty((1, config.max_position_embeddings)), )
self.register_buffer(
"position_ids",
torch.arange(config.max_position_embeddings).unsqueeze(0),
)
self.position_embedding_type = config.position_embedding_type
if self.position_embedding_type != "absolute":
raise ValueError("Only 'absolute' position_embedding_type" +
@@ -358,31 +359,32 @@ class BertModel(nn.Module, SupportsQuant):
("qkv_proj", "value", "v"),
]
loaded_stacked_params = []
other_weights = []
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if self.pooler is None and "pooler" in name:
continue
for (param_name, weight_name, shard_id) in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
if name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param, loaded_weight, shard_id)
loaded_stacked_params.append(name)
break
else:
# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loaded_params.add(name)
if name in params_dict:
other_weights.append((name, loaded_weight))
loader = AutoWeightsLoader(
self,
skip_prefixes=(["pooler."] if self.pooler is None else []),
)
loaded_params = loader.load_weights(other_weights)
loaded_params.update(loaded_stacked_params)
return loaded_params
@@ -396,7 +398,6 @@ class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
model: An instance of BertModel used for forward operations.
_pooler: An instance of Pooler used for pooling operations.
"""
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -425,10 +426,15 @@ class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
return self._pooler(hidden_states, pooling_metadata)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
weights = self.hf_to_vllm_mapper.apply(weights)
weights = ((name, data) for name, data in weights
if not name.startswith("lm_head."))
self.model.load_weights(weights)
weights_list = list(weights)
has_model_prefix = any(
name.startswith("model.") for name, _ in weights_list)
if not has_model_prefix:
mapper = WeightsMapper(orig_to_new_prefix={"": "model."})
loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
return loader.load_weights(weights_list, mapper=mapper)
def _build_model(self,
vllm_config: VllmConfig,
@@ -470,26 +476,9 @@ class BertForSequenceClassification(nn.Module, SupportsV0Only,
self.classifier, self.bert.pooler)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
self_weights = []
def weight_filter():
for name, weight in weights:
if name.startswith("bert."):
yield (name[len("bert."):], weight)
else:
self_weights.append((name, weight))
self.bert.load_weights(weight_filter())
params_dict = dict(self.named_parameters())
for name, loaded_weight in self_weights:
if name.startswith("classifier"):
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loader = AutoWeightsLoader(self)
loaded_params = loader.load_weights(weights)
return loaded_params
def pooler(
self,

74
vllm/model_executor/models/roberta.py
View File

@@ -1,7 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import itertools
from collections.abc import Iterable
from typing import Optional, Union
@@ -13,9 +12,9 @@ from vllm.config import VllmConfig
from vllm.model_executor.layers.pooler import ClassifierPooler
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.utils import WeightsMapper, maybe_prefix
from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
maybe_prefix)
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
@@ -39,8 +38,10 @@ class RobertaEmbedding(nn.Module):
config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.position_ids = nn.Parameter(
torch.empty((1, config.max_position_embeddings)), )
self.register_buffer(
"position_ids",
torch.arange(config.max_position_embeddings).unsqueeze(0),
)
self.position_embedding_type = config.position_embedding_type
if self.position_embedding_type != "absolute":
@@ -136,16 +137,20 @@ class RobertaEmbeddingModel(BertEmbeddingModel):
embedding_class=RobertaEmbedding)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
weights = self.hf_to_vllm_mapper.apply(weights)
# Separate weights in "roberta"-prefixed and all else (not in memory).
# For use with models like FacebookAI/roberta-base.
bert_weights, task_weights = roberta_task_weights_filter(weights)
loaded = self.model.load_weights(bert_weights)
if not len(loaded):
# Fix for models like `sentence-transformers/stsb-roberta-base-v2`
# which use the same architecture, but have no "roberta" prefix.
loaded = self.model.load_weights(task_weights)
assert len(loaded), "Unable to load RobertaEmbeddingModel"
weights_list = list(weights)
has_roberta_prefix = any(
name.startswith("roberta.") for name, _ in weights_list)
if has_roberta_prefix:
# For models with the `roberta.` prefix e.g.
# `FacebookAI/roberta-base`
mapper = WeightsMapper(orig_to_new_prefix={"roberta.": "model."})
else:
# For models without the `roberta.` prefix e.g.
# `sentence-transformers/stsb-roberta-base-v2`
mapper = WeightsMapper(orig_to_new_prefix={"": "model."})
loader = AutoWeightsLoader(self, skip_prefixes=["lm_head."])
return loader.load_weights(weights_list, mapper=mapper)
class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding,
@@ -187,19 +192,8 @@ class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding,
self.classifier)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
bert_weights, task_weights = roberta_task_weights_filter(weights)
bert_weights = self.jina_to_vllm_mapper.apply(bert_weights)
self.roberta.load_weights(bert_weights)
params_dict = dict(self.named_parameters())
for name, loaded_weight in task_weights:
if name.startswith("classifier"):
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
loader = AutoWeightsLoader(self)
return loader.load_weights(weights, mapper=self.jina_to_vllm_mapper)
def pooler(
self,
@@ -245,27 +239,3 @@ def create_position_ids_from_input_ids(input_ids,
past_key_values_length) * mask
return incremental_indices.long() + padding_idx
def roberta_task_weights_filter(
all_weights: Iterable[tuple[str, torch.Tensor]]
) -> tuple[Iterable[tuple[str, torch.Tensor]], Iterable[tuple[str,
torch.Tensor]]]:
"""
Separate task-specific weights that are applied on top
of the encoder-decoder bert base.
To do so, return two generators over the original iterator.
Also, remove the "roberta." prefix to make it loadable
from vanilla BertModel.
"""
# Copy of a lazy iterator without in-memory overhead so both
# iterators can be iterated upon independently.
all_weights1, all_weights2 = itertools.tee(all_weights)
def encoder_decoder_weights():
for name, weight in all_weights1:
if name.startswith("roberta."):
yield (name[len("roberta."):], weight)
return encoder_decoder_weights(), ((n, w) for n, w in all_weights2
if not n.startswith("roberta."))