111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from https://huggingface.co/jinaai/jina-reranker-v3/blob/main/modeling.py
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from collections.abc import Iterable
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import torch
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from torch import nn
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from vllm.config import VllmConfig
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import PoolingTask
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from vllm.v1.pool.metadata import PoolingMetadata
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from ..layers.pooler import DispatchPooler
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from ..layers.pooler.tokwise import (
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StepPool,
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TokenPooler,
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TokenPoolingMethodOutputItem,
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)
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from .interfaces import SupportsLateInteraction
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from .qwen3 import Qwen3Model
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from .utils import AutoWeightsLoader, maybe_prefix
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class JinaForRanking(nn.Module, SupportsLateInteraction):
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is_pooling_model = True
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.projector_dim: int = config.embedding_size
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self.vllm_config = vllm_config
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self.quant_config = quant_config
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self.model = Qwen3Model(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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self.projector = nn.Sequential(
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nn.Linear(config.hidden_size, config.hidden_size // 2, bias=False),
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nn.ReLU(),
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nn.Linear(config.hidden_size // 2, self.projector_dim, bias=False),
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)
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self.pooler = DispatchPooler(
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{
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"token_embed": TokenPooler(
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pooling=JinaForRankingPool(self.projector),
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)
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}
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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hidden_states = self.model(
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input_ids, positions, intermediate_tensors, inputs_embeds
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)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self, skip_prefixes=(["lm_head."]))
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return loader.load_weights(weights)
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class JinaForRankingPool(StepPool):
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def __init__(self, projector: nn.Sequential):
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super().__init__()
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self.doc_token_id = 151670
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self.query_token_id = 151671
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self.projector = projector
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def get_supported_tasks(self) -> set[PoolingTask]:
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return {"token_embed"}
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def forward(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> list[TokenPoolingMethodOutputItem]:
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pooled_data_lst = super().forward(hidden_states, pooling_metadata)
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prompt_token_ids = pooling_metadata.get_prompt_token_ids()
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embeds_list = list[torch.Tensor | None]()
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for data, token_ids in zip(pooled_data_lst, prompt_token_ids):
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# for unfinished chunked prefill
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if data is None:
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embeds_list.append(None)
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else:
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docs_indexes = torch.where(torch.eq(token_ids, self.doc_token_id))[0]
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query_indexes = torch.where(torch.eq(token_ids, self.query_token_id))[0]
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# The JinaForRanking model concatenates docs first, then query.
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# Let's stay consistent with this novel design.
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indexes = torch.cat([docs_indexes, query_indexes])
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embeds = self.projector(data[indexes])
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embeds_list.append(embeds)
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return embeds_list
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