Support `BERTModel` (first `encoder-only` embedding model) (#9056)

Signed-off-by: Max de Bayser <maxdebayser@gmail.com>
Signed-off-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Andrew Feldman <afeldman@neuralmagic.com>
Co-authored-by: afeldman-nm <156691304+afeldman-nm@users.noreply.github.com>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: laishzh <laishengzhang@gmail.com>
Co-authored-by: Max de Bayser <maxdebayser@gmail.com>
Co-authored-by: Max de Bayser <mbayser@br.ibm.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>

tests/models/embedding/language/test_embedding.py

@@ -6,21 +6,31 @@ import pytest
 
 from ..utils import check_embeddings_close
 
+# Model, Guard
 MODELS = [
     "intfloat/e5-mistral-7b-instruct",
+    "BAAI/bge-base-en-v1.5",
     "BAAI/bge-multilingual-gemma2",
 ]
 
+ENCODER_ONLY = [
+    "BAAI/bge-base-en-v1.5",
+]
+
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 def test_models(
+    monkeypatch,
     hf_runner,
     vllm_runner,
     example_prompts,
-    model: str,
+    model,
     dtype: str,
 ) -> None:
+    if model in ENCODER_ONLY:
+        monkeypatch.setenv("VLLM_ATTENTION_BACKEND", "XFORMERS")
+
     # The example_prompts has ending "\n", for example:
     # "Write a short story about a robot that dreams for the first time.\n"
     # sentence_transformers will strip the input texts, see:
@@ -33,7 +43,7 @@ def test_models(
                    is_sentence_transformer=True) as hf_model:
         hf_outputs = hf_model.encode(example_prompts)
 
-    with vllm_runner(model, dtype=dtype) as vllm_model:
+    with vllm_runner(model, dtype=dtype, max_model_len=None) as vllm_model:
         vllm_outputs = vllm_model.encode(example_prompts)
 
     check_embeddings_close(

vllm/attention/backends/abstract.py

@@ -15,8 +15,11 @@ if TYPE_CHECKING:
 
 class AttentionType(Enum):
     DECODER = auto()  # Decoder attention between previous layer Q/K/V
-    ENCODER = auto()  # Encoder attention between previous layer Q/K/V
-    ENCODER_DECODER = auto()  # Attention between dec. Q and enc. K/V
+    ENCODER = auto(
+    )  # Encoder attention between previous layer Q/K/V for encoder-decoder
+    ENCODER_ONLY = auto()  # Encoder attention between previous layer Q/K/V
+    ENCODER_DECODER = auto(
+    )  # Attention between dec. Q and enc. K/V for encoder-decoder
 
 
 class AttentionBackend(ABC):

vllm/attention/backends/xformers.py

@@ -287,13 +287,15 @@ def _get_attn_bias(
     * Appropriate attention bias value given the attention type
     '''
 
-    if attn_type == AttentionType.DECODER:
+    if (attn_type == AttentionType.DECODER
+            or attn_type == AttentionType.ENCODER_ONLY):
         return attn_metadata.attn_bias
     elif attn_type == AttentionType.ENCODER:
         return attn_metadata.encoder_attn_bias
-    else:
-        # attn_type == AttentionType.ENCODER_DECODER
+    elif attn_type == AttentionType.ENCODER_DECODER:
         return attn_metadata.cross_attn_bias
+    else:
+        raise AttributeError(f"Invalid attention type {str(attn_type)}")
 
 
 def _set_attn_bias(
@@ -313,7 +315,8 @@ def _set_attn_bias(
                  encoder/decoder cross-attention
     '''
 
-    if attn_type == AttentionType.DECODER:
+    if (attn_type == AttentionType.DECODER
+            or attn_type == AttentionType.ENCODER_ONLY):
         attn_metadata.attn_bias = attn_bias
     elif attn_type == AttentionType.ENCODER:
         attn_metadata.encoder_attn_bias = attn_bias
@@ -371,6 +374,12 @@ def _get_seq_len_block_table_args(
         # No block tables associated with encoder attention
         return (attn_metadata.encoder_seq_lens_tensor,
                 attn_metadata.max_encoder_seq_len, None)
+    elif attn_type == AttentionType.ENCODER_ONLY:
+        assert is_prompt, "Should not have decode for encoder only model."
+
+        # No block tables associated with encoder attention
+        return (attn_metadata.seq_lens_tensor,
+                attn_metadata.max_prefill_seq_len, None)
     else:
         raise AttributeError(f"Invalid attention type {str(attn_type)}")
 
@@ -479,7 +488,10 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
             * ENCODER: no KV caching; pass encoder sequence
                 attributes (encoder_seq_lens/encoder_seq_lens_tensor/
                 max_encoder_seq_len) to kernel, in lieu of decoder
-                sequence attributes (seq_lens/seq_lens_tensor/max_seq_len)
+                sequence attributes (seq_lens/seq_lens_tensor/max_seq_len).
+                Used for encoder branch of encoder-decoder models.
+            * ENCODER_ONLY: no kv_caching, uses the normal attention 
+                attributes (seq_lens/seq_lens_tensor/max_seq_len).
             * ENCODER_DECODER: cross-attention behavior;
                 use cross-attention block table for caching KVs derived
                 from encoder hidden states; since KV sequence lengths
@@ -509,6 +521,7 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
                 and (not attn_metadata.is_all_encoder_attn_metadata_set)):
             raise AttributeError("Encoder attention requires setting "
                                  "encoder metadata attributes.")
+
         elif (attn_type == AttentionType.ENCODER_DECODER
               and (not attn_metadata.is_all_cross_attn_metadata_set)):
             raise AttributeError("Encoder/decoder cross-attention "
@@ -609,6 +622,8 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
                 assert out.shape == output[:num_prefill_tokens].shape
                 output[:num_prefill_tokens] = out
             else:
+                assert attn_type != AttentionType.ENCODER_ONLY, (
+                    "Encoder-only models should not have prefix attention.")
 
                 assert prefill_meta.query_start_loc is not None
                 assert prefill_meta.max_query_len is not None
@@ -638,6 +653,8 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
                 output[:num_prefill_tokens] = out
 
         if decode_meta := attn_metadata.decode_metadata:
+            assert attn_type != AttentionType.ENCODER_ONLY, (
+                "Encoder-only models should not have decode metadata.")
 
             (
                 seq_lens_arg,
@@ -703,36 +720,60 @@ class XFormersImpl(AttentionImpl[XFormersMetadata]):
                           None, :].expand(value.shape[0], self.num_kv_heads,
                                           self.num_queries_per_kv,
                                           value.shape[-1])
+
         # Set attention bias if not provided. This typically happens at
         # the very attention layer of every iteration.
         # FIXME(woosuk): This is a hack.
         attn_bias = _get_attn_bias(attn_metadata, attn_type)
         if attn_bias is None:
             if self.alibi_slopes is None:
+
+                # Cross attention block of decoder branch of encoder-decoder
+                # model uses seq_lens for dec / encoder_seq_lens for enc
                 if (attn_type == AttentionType.ENCODER_DECODER):
                     assert attn_metadata.seq_lens is not None
                     assert attn_metadata.encoder_seq_lens is not None
 
-                    # Default enc/dec cross-attention mask is non-causal
+                    # Cross-attention mask is non-causal
                     attn_bias = BlockDiagonalMask.from_seqlens(
                         attn_metadata.seq_lens, attn_metadata.encoder_seq_lens)
+
+                # Encoder branch of encoder-decoder model uses
+                # attn_metadata.encoder_seq_lens
                 elif attn_type == AttentionType.ENCODER:
+
                     assert attn_metadata.encoder_seq_lens is not None
 
-                    # Default encoder self-attention mask is non-causal
+                    # Encoder self-attention mask is non-causal
                     attn_bias = BlockDiagonalMask.from_seqlens(
                         attn_metadata.encoder_seq_lens)
-                else:
+
+                # Self-attention block of encoder-only model just
+                # uses the seq_lens directly.
+                elif attn_type == AttentionType.ENCODER_ONLY:
                     assert attn_metadata.seq_lens is not None
 
-                    # Default decoder self-attention mask is causal
+                    # Encoder self-attention mask is non-causal
+                    attn_bias = BlockDiagonalMask.from_seqlens(
+                        attn_metadata.seq_lens)
+
+                # Self-attention block of decoder branch just
+                # uses the seq_lens directly
+                elif attn_type == AttentionType.DECODER:
+                    assert attn_metadata.seq_lens is not None
+
+                    # Decoder self-attention mask is causal
                     attn_bias = BlockDiagonalCausalMask.from_seqlens(
                         attn_metadata.seq_lens)
+                else:
+                    raise ValueError("Unknown AttentionType: %s", attn_type)
+
                 if self.sliding_window is not None:
                     attn_bias = attn_bias.make_local_attention(
                         self.sliding_window)
                 attn_bias = [attn_bias]
             else:
+                assert attn_type == AttentionType.DECODER
                 assert attn_metadata.seq_lens is not None
                 attn_bias = _make_alibi_bias(self.alibi_slopes,
                                              self.num_kv_heads, query.dtype,

vllm/model_executor/layers/pooler.py

@@ -12,6 +12,7 @@ class PoolingType(IntEnum):
     """Enumeration for different types of pooling methods."""
     LAST = 0
     ALL = 1
+    CLS = 2
 
 
 class Pooler(nn.Module):
@@ -23,12 +24,13 @@ class Pooler(nn.Module):
     3. Returns structured results as `PoolerOutput`.
 
     Attributes:
-        pooling_type: The type of pooling to use (LAST, AVERAGE, MAX).
+        pooling_type: The type of pooling to use (LAST, ALL, CLS).
         normalize: Whether to normalize the pooled data.
     """
 
     def __init__(self, pooling_type: PoolingType, normalize: bool):
         super().__init__()
+
         self.pooling_type = pooling_type
         self.normalize = normalize
 
@@ -38,10 +40,16 @@ class Pooler(nn.Module):
         pooling_metadata: PoolingMetadata,
     ) -> PoolerOutput:
         """Pools specific information from hidden states based on metadata."""
+
         prompt_lens = PoolingTensors.from_pooling_metadata(
             pooling_metadata, hidden_states.device).prompt_lens
 
-        if self.pooling_type == PoolingType.LAST:
+        if self.pooling_type is PoolingType.CLS:
+            first_token_flat_indices = torch.zeros_like(prompt_lens)
+            first_token_flat_indices[1:] += torch.cumsum(prompt_lens,
+                                                         dim=0)[:-1]
+            pooled_data = hidden_states[first_token_flat_indices]
+        elif self.pooling_type == PoolingType.LAST:
             last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
             pooled_data = hidden_states[last_token_flat_indices]
         elif self.pooling_type == PoolingType.ALL:

vllm/model_executor/models/bert.py

+from typing import Iterable, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import BertConfig
+
+from vllm.attention import Attention, AttentionMetadata, AttentionType
+from vllm.attention.backends.xformers import XFormersImpl
+from vllm.config import CacheConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.pooler import Pooler, PoolingType
+from vllm.model_executor.layers.quantization.base_config 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
+
+
+class BertEmbedding(nn.Module):
+
+    def __init__(self, config: BertConfig):
+
+        super().__init__()
+        self.size = config.hidden_size
+        self.word_embeddings = VocabParallelEmbedding(config.vocab_size,
+                                                      config.hidden_size)
+        self.position_embeddings = VocabParallelEmbedding(
+            config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = VocabParallelEmbedding(
+            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.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type != "absolute":
+            raise ValueError("Only 'absolute' position_embedding_type" +
+                             " is supported")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        input_shape = input_ids.size()
+
+        # Input embeddings.
+        inputs_embeds = self.word_embeddings(input_ids)
+
+        # Position embeddings.
+        position_embeddings = self.position_embeddings(position_ids)
+
+        # Token type embeddings. (TODO: move off hotpath?)
+        token_type_embeddings = self.token_type_embeddings(
+            torch.zeros(input_shape,
+                        dtype=torch.long,
+                        device=inputs_embeds.device))
+
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+class BertEncoder(nn.Module):
+
+    def __init__(self,
+                 config: BertConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.layer = nn.ModuleList([
+            BertLayer(config=config,
+                      cache_config=cache_config,
+                      quant_config=quant_config,
+                      prefix=f"{prefix}.layer.{layer_idx}")
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        for i in range(len(self.layer)):
+            layer = self.layer[i]
+            hidden_states = layer(hidden_states, kv_caches[i], attn_metadata)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+
+    def __init__(self,
+                 config: BertConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        self.attention = BertAttention(
+            hidden_size=config.hidden_size,
+            num_attention_heads=config.num_attention_heads,
+            layer_norm_eps=config.layer_norm_eps,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.attention")
+
+        self.intermediate = BertIntermediate(
+            hidden_size=config.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            prefix=f"{prefix}.intermediate")
+
+        self.output = BertOutput(hidden_size=config.hidden_size,
+                                 intermediate_size=config.intermediate_size,
+                                 layer_norm_eps=config.layer_norm_eps,
+                                 quant_config=quant_config,
+                                 prefix=f"{prefix}.output")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: Optional[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ):
+        attn_output = self.attention(hidden_states, kv_cache, attn_metadata)
+        intermediate_output = self.intermediate(attn_output)
+        output = self.output(intermediate_output, attn_output)
+        return output
+
+
+class BertAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        layer_norm_eps: float,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.self = BertSelfAttention(hidden_size=hidden_size,
+                                      num_attention_heads=num_attention_heads,
+                                      cache_config=cache_config,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.output")
+
+        self.output = BertSelfOutput(hidden_size=hidden_size,
+                                     layer_norm_eps=layer_norm_eps,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.output")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        self_output = self.self(hidden_states, kv_cache, attn_metadata)
+        return self.output(self_output, hidden_states)
+
+
+class BertSelfAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = self.total_num_heads
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj")
+
+        self.attn = Attention(num_heads=self.num_heads,
+                              head_size=self.head_dim,
+                              scale=self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config,
+                              prefix=f"{prefix}.attn")
+
+        if not isinstance(self.attn.impl, XFormersImpl):
+            raise ValueError(
+                "Encoder-only models currently require XFORMERS attention "
+                "backend. Set VLLM_ATTENTION_BACKEND=XFORMERS to use BERT.")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        output = self.attn(q,
+                           k,
+                           v,
+                           kv_cache,
+                           attn_metadata,
+                           attn_type=AttentionType.ENCODER_ONLY)
+        return output
+
+
+class BertSelfOutput(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 layer_norm_eps: float,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.dense = RowParallelLinear(input_size=hidden_size,
+                                       output_size=hidden_size,
+                                       bias=True,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.dense")
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor,
+                input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertIntermediate(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 hidden_act: str,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.dense = ColumnParallelLinear(input_size=hidden_size,
+                                          output_size=intermediate_size,
+                                          bias=True,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.dense")
+        self.intermediate_act_fn = get_act_fn(hidden_act)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 layer_norm_eps: float,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        self.dense = RowParallelLinear(input_size=intermediate_size,
+                                       output_size=hidden_size,
+                                       bias=True,
+                                       quant_config=quant_config,
+                                       prefix=f"{prefix}.dense")
+
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor,
+                input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.dense(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertModel(nn.Module):
+
+    def __init__(self,
+                 config: BertConfig,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = ""):
+        super().__init__()
+        self.embeddings = BertEmbedding(config)
+        self.encoder = BertEncoder(config,
+                                   cache_config,
+                                   quant_config,
+                                   prefix=f"{prefix}.encoder")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is not None:
+            hidden_states = inputs_embeds
+        else:
+            hidden_states = self.embeddings(input_ids=input_ids,
+                                            position_ids=position_ids)
+
+        return self.encoder(hidden_states, kv_caches, attn_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "query", "q"),
+            ("qkv_proj", "key", "k"),
+            ("qkv_proj", "value", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "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:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                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)
+
+
+class BertEmbeddingModel(nn.Module):
+    """A model that uses Bert to provide embedding functionalities.
+
+   This class encapsulates the BertModel and provides an interface for
+   embedding operations and customized pooling functions.
+
+   Attributes:
+       model: An instance of BertModel used for forward operations.
+       _pooler: An instance of Pooler used for pooling operations.
+   """
+
+    def __init__(
+        self,
+        config: BertConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.model = BertModel(config, cache_config, quant_config)
+        self._pooler = Pooler(pooling_type=PoolingType.CLS, normalize=True)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.model(input_ids=input_ids,
+                          position_ids=positions,
+                          kv_caches=kv_caches,
+                          inputs_embeds=inputs_embeds,
+                          intermediate_tensors=intermediate_tensors,
+                          attn_metadata=attn_metadata)
+
+    def pooler(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Optional[PoolerOutput]:
+        return self._pooler(hidden_states, pooling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        self.model.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -87,6 +87,7 @@ _TEXT_GENERATION_MODELS = {
 
 _EMBEDDING_MODELS = {
     # [Text-only]
+    "BertModel": ("bert", "BertEmbeddingModel"),
     "Gemma2Model": ("gemma2", "Gemma2EmbeddingModel"),
     "MistralModel": ("llama", "LlamaEmbeddingModel"),
     "Qwen2ForRewardModel": ("qwen2_rm", "Qwen2ForRewardModel"),