bert.py

# 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 import Attention, AttentionType
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.model_executor.pooling_metadata import PoolingMetadata
from vllm.sequence import IntermediateTensors
from vllm.tasks import PoolingTask

from .interfaces import SupportsCrossEncoding, SupportsQuant
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 = 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",
                              attn_type=AttentionType.ENCODER_ONLY)

    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
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


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


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,
                default_pooling_type=PoolingType.CLS,
            ),
        })


# 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(input_ids.shape,
                          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


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)

        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)