vllm/vllm/model_executor/models/modernbert.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 ModernBertConfig

from vllm.attention import Attention, AttentionType
from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.linear import (QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.pooler import (ClassifierPooler,
                                               DispatchPooler, Pooler,
                                               PoolingMethod,
                                               PoolingParamsUpdate,
                                               PoolingType)
from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
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
from vllm.tasks import PoolingTask

from .interfaces import SupportsCrossEncoding, default_pooling_type
from .utils import WeightsMapper, maybe_prefix


class ModernBertEmbeddings(nn.Module):

    def __init__(self, config: ModernBertConfig):

        super().__init__()
        self.config = config
        self.tok_embeddings = VocabParallelEmbedding(config.vocab_size,
                                                     config.hidden_size)
        self.norm = nn.LayerNorm(config.hidden_size,
                                 eps=config.layer_norm_eps,
                                 bias=config.norm_bias)

    def forward(
        self,
        input_ids: torch.Tensor,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if inputs_embeds is not None:
            return self.norm(inputs_embeds)
        else:
            inputs_embeds = self.tok_embeddings(input_ids)
            embeddings = self.norm(inputs_embeds)
            return embeddings


class ModernBertRotaryEmbedding(RotaryEmbedding):

    def __init__(self, config: ModernBertConfig, head_size: int, dim: int,
                 base: float):
        super().__init__(
            head_size=head_size,
            rotary_dim=dim,
            max_position_embeddings=config.max_position_embeddings,
            base=base,
            is_neox_style=True,
            dtype=torch.float16)
        self.config = config


class ModernBertAttention(nn.Module):

    def __init__(self,
                 config: ModernBertConfig,
                 layer_id: Optional[int] = None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.layer_id = layer_id
        self.deterministic_flash_attn = config.deterministic_flash_attn
        self.num_heads = config.num_attention_heads
        assert self.num_heads % tp_size == 0
        self.head_dim = config.hidden_size // config.num_attention_heads
        self.all_head_size = self.head_dim * self.num_heads
        self.scaling = self.head_dim**-0.5
        self.Wqkv = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            self.num_heads,
            bias=config.attention_bias,
        )

        sliding_window = None
        if layer_id % config.global_attn_every_n_layers != 0:
            sliding_window = config.local_attention // 2
            rope_theta = config.local_rope_theta if config.local_rope_theta \
                    is not None else config.global_rope_theta
        else:
            rope_theta = config.global_rope_theta

        self.rotary_emb = ModernBertRotaryEmbedding(config=config,
                                                    head_size=self.head_dim,
                                                    dim=self.head_dim,
                                                    base=rope_theta)
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              prefix=f"{layer_id}.attn",
                              attn_type=AttentionType.ENCODER_ONLY,
                              per_layer_sliding_window=sliding_window)
        self.Wo = RowParallelLinear(config.hidden_size,
                                    config.hidden_size,
                                    bias=config.attention_bias)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.Wqkv(hidden_states)
        q, k, v = qkv.split([self.all_head_size] * 3, dim=-1)
        q, k = self.rotary_emb(position_ids, q, k)
        attn_outputs = self.attn(q, k, v)
        hidden_states = attn_outputs
        hidden_states, _ = self.Wo(hidden_states)
        return hidden_states


class ModernBertMLP(nn.Module):

    def __init__(self, config: ModernBertConfig):
        super().__init__()
        self.config = config
        self.Wi = nn.Linear(config.hidden_size,
                            int(config.intermediate_size) * 2,
                            bias=config.mlp_bias)
        self.act = nn.GELU()
        self.Wo = RowParallelLinear(config.intermediate_size,
                                    config.hidden_size,
                                    bias=config.mlp_bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
        return self.Wo(self.act(input) * gate)[0]


class ModernBertLayer(nn.Module):

    def __init__(self,
                 config: ModernBertConfig,
                 prefix: str = "",
                 layer_id: Optional[int] = None):
        super().__init__()
        self.config = config
        if layer_id == 0:
            self.attn_norm = nn.Identity()
        else:
            self.attn_norm = nn.LayerNorm(config.hidden_size,
                                          eps=config.norm_eps,
                                          bias=config.norm_bias)
        self.attn = ModernBertAttention(config=config, layer_id=layer_id)
        self.mlp_norm = nn.LayerNorm(config.hidden_size,
                                     eps=config.norm_eps,
                                     bias=config.norm_bias)
        self.mlp = ModernBertMLP(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
    ) -> torch.Tensor:
        attn_outputs = self.attn(hidden_states=self.attn_norm(hidden_states),
                                 position_ids=position_ids)
        hidden_states = hidden_states + attn_outputs
        mlp_output = self.mlp(self.mlp_norm(hidden_states))
        hidden_states = hidden_states + mlp_output
        return hidden_states


class ModernBertEncoderLayer(nn.Module):

    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        self.layers = nn.ModuleList([
            ModernBertLayer(config=config, layer_id=layer_id)
            for layer_id in range(config.num_hidden_layers)
        ])

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
    ) -> torch.Tensor:
        for i, layer in enumerate(self.layers):
            hidden_states = layer(hidden_states, position_ids)
        return hidden_states


@support_torch_compile
@default_pooling_type("CLS")
class ModernBertModel(nn.Module):
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={"layers.": "encoder_layer.layers."})

    def __init__(
        self,
        vllm_config: VllmConfig,
        prefix: str = "",
    ):
        super().__init__()
        config = vllm_config.model_config.hf_config
        self.config = config
        self.embeddings = ModernBertEmbeddings(config)
        self.encoder_layer = ModernBertEncoderLayer(vllm_config)
        self.final_norm = nn.LayerNorm(config.hidden_size,
                                       eps=config.norm_eps,
                                       bias=config.norm_bias)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        weights = self.hf_to_vllm_mapper.apply(weights)
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            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)
        return loaded_params

    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,
                                            inputs_embeds=inputs_embeds)

        outputs = self.encoder_layer(
            hidden_states=hidden_states,
            position_ids=positions,
        )
        norm_outputs = self.final_norm(outputs)
        return norm_outputs


class ModernBertPooler(Pooler):

    def __init__(self, config: ModernBertConfig):
        super().__init__()

        pooling_type = PoolingType[config.classifier_pooling.upper()]
        self.pooling = PoolingMethod.from_pooling_type(pooling_type)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size,
                               config.classifier_bias)
        self.act = nn.GELU()
        self.norm = nn.LayerNorm(config.hidden_size,
                                 eps=config.norm_eps,
                                 bias=config.norm_bias)

    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 = pooled_output.to(self.dense.weight.dtype)
        return self.norm(self.act(self.dense(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


@default_pooling_type("CLS")
class ModernBertForSequenceClassification(nn.Module, SupportsCrossEncoding):

    is_pooling_model = True

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        self.config = config
        self.model = ModernBertModel(vllm_config=vllm_config,
                                     prefix=maybe_prefix(prefix, "modernbert"))
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.pooling = ModernBertPooler(config)

        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.pooling,
                classifier=self.classifier,
                act_fn=ClassifierPooler.act_fn_for_seq_cls(
                    vllm_config.model_config),
            ),
            "score":
            ClassifierPooler(
                pooling=self.pooling,
                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]]):

        self_weights = []

        def weight_filter():
            for name, weight in weights:
                if name.startswith("model."):
                    yield name[len("model."):], weight
                else:
                    self_weights.append((name, weight))

        self.model.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)
            if name.startswith("head"):
                param = params_dict["pooling." + name[len("head") + 1:]]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)

    def forward(
        self,
        input_ids: Optional[torch.LongTensor],
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return self.model(
            input_ids=input_ids,
            inputs_embeds=inputs_embeds,
            positions=positions,
        )