init

vllm/model_executor/models/bert.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable, Set
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import BertConfig
+
+from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, PoolerConfig, VllmConfig
+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 (ClassifierPooler,
+                                               DispatchPooler, Pooler,
+                                               PoolingMethod,
+                                               PoolingParamsUpdate,
+                                               PoolingType)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.sequence import IntermediateTensors
+from vllm.tasks import PoolingTask
+from vllm.v1.pool.metadata import PoolingMetadata
+
+from .interfaces import SupportsCrossEncoding, SupportsQuant
+from .interfaces_base import default_pooling_type
+from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
+
+
+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.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" +
+                             " is supported")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+    ) -> torch.Tensor:
+
+        token_type_ids = _decode_token_type_ids(input_ids)
+
+        inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings + position_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        return embeddings
+
+
+class BertPooler(Pooler):
+
+    def __init__(self, config: BertConfig):
+        super().__init__()
+
+        self.pooling = PoolingMethod.from_pooling_type(PoolingType.CLS)
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def get_supported_tasks(self) -> Set[PoolingTask]:
+        return self.pooling.get_supported_tasks()
+
+    def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
+        return self.pooling.get_pooling_updates(task)
+
+    def _head(self, pooled_output: torch.Tensor):
+        pooled_output = self.dense(pooled_output)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+    def forward(
+        self,
+        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        pooled_output = self.pooling(hidden_states, pooling_metadata)
+
+        if isinstance(pooled_output, list):
+            pooled_output = [self._head(output) for output in pooled_output]
+        else:
+            pooled_output = self._head(pooled_output)
+
+        return pooled_output
+
+
+class BertEncoder(nn.Module):
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        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,
+    ) -> torch.Tensor:
+        for layer in self.layer:
+            hidden_states = layer(hidden_states)
+        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):
+        attn_output = self.attention(hidden_states)
+        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,
+    ) -> torch.Tensor:
+        self_output = self.self(hidden_states)
+        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 = EncoderOnlyAttention(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")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> 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)
+        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
+
+
+@support_torch_compile
+@default_pooling_type("CLS")
+class BertModel(nn.Module, SupportsQuant):
+
+    is_pooling_model = True
+
+    packed_modules_mapping = {"qkv_proj": ["query", "key", "value"]}
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        embedding_class: type[nn.Module] = BertEmbedding,
+    ) -> None:
+        super().__init__()
+
+        self.config = vllm_config.model_config.hf_config
+        self.embeddings = embedding_class(self.config)
+        self.encoder = BertEncoder(vllm_config=vllm_config,
+                                   prefix=f"{prefix}.encoder")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        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=positions)
+        return self.encoder(hidden_states)
+
+    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"),
+        ]
+
+        loaded_stacked_params = []
+        other_weights = []
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            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)
+                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:
+                if name in params_dict:
+                    other_weights.append((name, loaded_weight))
+
+        return other_weights, loaded_stacked_params
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        other_weights, loaded_stacked_params = self._load_weights(weights)
+
+        loader = AutoWeightsLoader(self, skip_prefixes=["pooler."])
+        loaded_params = loader.load_weights(other_weights)
+        loaded_params.update(loaded_stacked_params)
+        return loaded_params
+
+
+@default_pooling_type("ALL")
+class BertPoolingModel(BertModel):
+
+    is_pooling_model = True
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        embedding_class: type[nn.Module] = BertEmbedding,
+    ) -> None:
+        super().__init__(
+            vllm_config=vllm_config,
+            prefix=prefix,
+            embedding_class=embedding_class,
+        )
+
+        config = vllm_config.model_config.hf_config
+        self.pooler = BertPooler(config)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        other_weights, loaded_stacked_params = self._load_weights(weights)
+
+        loader = AutoWeightsLoader(self)
+        loaded_params = loader.load_weights(other_weights)
+        loaded_params.update(loaded_stacked_params)
+        return loaded_params
+
+
+@default_pooling_type("CLS")
+class BertEmbeddingModel(nn.Module, SupportsQuant):
+    """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.
+    """
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.model = self._build_model(vllm_config=vllm_config,
+                                       prefix=maybe_prefix(prefix, "model"))
+        self.pooler = self._build_pooler(pooler_config)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.model(input_ids=input_ids,
+                          positions=positions,
+                          inputs_embeds=inputs_embeds,
+                          intermediate_tensors=intermediate_tensors)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        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,
+                     prefix: str = "") -> BertModel:
+        return BertModel(vllm_config=vllm_config,
+                         prefix=prefix,
+                         embedding_class=BertEmbedding)
+
+    def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
+        return DispatchPooler({
+            "encode": Pooler.for_encode(pooler_config),
+            "embed": Pooler.for_embed(pooler_config),
+        })
+
+
+# Here we encode the token type ids together with the input ids.
+# Since we use int 32 for the input IDs and the vocabulary size
+# is way lower than 2**31, there is room to encode additional
+# bits. At the same time, for cross-encoder use cases, the
+# token type ids are only 0 or 1, requiring only 1 bit.
+# This means that we can store the token type ids in the 31st
+# bit. We void the 32nd bit because that would produce a negative
+# number, which could be used to signal other things.
+#
+# The reason for all of this is that all the tensors that are
+# passed as input to the forward function of a module marked
+# with @support_torch_compile have to be persistent. So to
+# avoid adding more persistent tensors in the model runner, we
+# encode more information in the same persistent tensor.
+#
+# Since the *ForClassification module is outside of the BertModel
+# which is compiled, we can do the encoding here and then separate
+# the information again in the Embedding  layer. Since with bit masks
+# we can do this entirely with torch operations and without branching,
+# it works with torch compile.
+
+TOKEN_TYPE_SHIFT = 30
+
+
+def _encode_token_type_ids(input_ids: torch.Tensor,
+                           token_type_ids: torch.Tensor) -> None:
+    # input_ids can be padded to the right
+    input_ids[:token_type_ids.shape[0]].bitwise_or_(
+        token_type_ids << TOKEN_TYPE_SHIFT)
+
+
+def _decode_token_type_ids(input_ids: torch.Tensor) -> torch.Tensor:
+
+    ids_mask = torch.ones_like(input_ids,
+                               dtype=torch.int32,
+                               device=input_ids.device) << TOKEN_TYPE_SHIFT
+    tokens_mask = ids_mask.bitwise_not()
+
+    token_type_ids = input_ids.bitwise_and(ids_mask) >> TOKEN_TYPE_SHIFT
+
+    input_ids.bitwise_and_(tokens_mask)
+
+    return token_type_ids
+
+
+@default_pooling_type("CLS")
+class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
+                                    SupportsQuant):
+    """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.
+   """
+
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        self.num_labels = config.num_labels
+        self.bert = BertPoolingModel(vllm_config=vllm_config,
+                                     prefix=maybe_prefix(prefix, "bert"),
+                                     embedding_class=BertEmbedding)
+        self.classifier = nn.Linear(config.hidden_size,
+                                    config.num_labels,
+                                    dtype=vllm_config.model_config.head_dtype)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode":
+            Pooler.for_encode(pooler_config),
+            "classify":
+            ClassifierPooler(
+                pooling=self.bert.pooler,
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_seq_cls(
+                    vllm_config.model_config),
+            ),
+            "score":
+            ClassifierPooler(
+                pooling=self.bert.pooler,
+                classifier=self.classifier,
+                act_fn=ClassifierPooler.act_fn_for_cross_encoder(
+                    vllm_config.model_config),
+            ),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        loaded_params = loader.load_weights(weights)
+        return loaded_params
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        if token_type_ids is not None:
+            assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
+            assert input_ids is not None
+            _encode_token_type_ids(input_ids, token_type_ids)
+
+        return self.bert(input_ids=input_ids,
+                         positions=positions,
+                         inputs_embeds=inputs_embeds,
+                         intermediate_tensors=intermediate_tensors)
+
+
+@default_pooling_type("ALL")
+class BertForTokenClassification(nn.Module):
+    is_pooling_model = True
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.head_dtype = vllm_config.model_config.head_dtype
+        self.num_labels = config.num_labels
+        self.bert = BertModel(vllm_config=vllm_config,
+                              prefix=maybe_prefix(prefix, "bert"),
+                              embedding_class=BertEmbedding)
+        self.classifier = nn.Linear(config.hidden_size,
+                                    config.num_labels,
+                                    dtype=self.head_dtype)
+
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode":
+            Pooler.for_encode(pooler_config),
+        })
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        loader = AutoWeightsLoader(self)
+        loaded_params = loader.load_weights(weights)
+        return loaded_params
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        if token_type_ids is not None:
+            assert self.bert.config.vocab_size < (1 << TOKEN_TYPE_SHIFT)
+            assert input_ids is not None
+            _encode_token_type_ids(input_ids, token_type_ids)
+
+        hidden_states = self.bert(input_ids=input_ids,
+                                  positions=positions,
+                                  inputs_embeds=inputs_embeds,
+                                  intermediate_tensors=intermediate_tensors)
+
+        hidden_states = hidden_states.to(self.head_dtype)
+        return self.classifier(hidden_states)