wehub-resource-sync
263 lines
9.1 KiB
Python
263 lines
9.1 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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import json
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import logging
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from collections import defaultdict
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from collections.abc import Iterable
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import torch
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from safetensors.torch import load as safetensors_load
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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.transformers_utils.repo_utils import get_hf_file_bytes
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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 .interfaces_base import VllmModelForPooling
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from .qwen3 import Qwen3ForCausalLM, Qwen3Model
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from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
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logger = logging.getLogger(__name__)
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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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# jina-embeddings-v5-text-small wraps Qwen3-0.6B-Base with four task-specific
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# LoRA adapters. This implementation merges the selected adapter into the base
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# weights at load time to avoid any runtime dependency on peft.
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#
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# Task selection:
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# Pass --hf-overrides '{"jina_task": "retrieval"}' to select one of:
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# retrieval (default), text-matching, classification, clustering.
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_DEFAULT_TASK = "retrieval"
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_SUPPORTED_TASKS = {"retrieval", "text-matching", "classification", "clustering"}
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def _load_adapter(
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model: str,
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task: str,
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revision: str | None,
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) -> tuple[dict, dict[str, torch.Tensor]] | None:
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"""Load adapter config and weights from a local path or HF repo.
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Returns (adapter_config, adapter_weights) or None if not found.
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"""
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config_bytes = get_hf_file_bytes(
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f"adapters/{task}/adapter_config.json",
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model,
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revision,
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)
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if config_bytes is None:
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return None
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adapter_config = json.loads(config_bytes)
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weights_bytes = get_hf_file_bytes(
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f"adapters/{task}/adapter_model.safetensors",
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model,
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revision,
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)
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if weights_bytes is None:
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return None
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adapter_weights = safetensors_load(weights_bytes)
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return adapter_config, adapter_weights
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def _build_lora_pairs(adapter_weights: dict) -> dict:
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"""Group raw adapter tensors into {base_key: {"A": tensor, "B": tensor}} pairs.
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Transforms adapter keys like:
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base_model.model.layers.0.self_attn.q_proj.lora_A.weight
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Into base keys like:
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layers.0.self_attn.q_proj.weight
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"""
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lora_pairs = defaultdict(dict)
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for key, tensor in adapter_weights.items():
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clean_key = key
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if clean_key.startswith("base_model.model."):
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clean_key = clean_key[len("base_model.model.") :]
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if ".lora_A." in clean_key:
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base_key = clean_key.split(".lora_A.")[0] + ".weight"
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lora_pairs[base_key]["A"] = tensor
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elif ".lora_B." in clean_key:
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base_key = clean_key.split(".lora_B.")[0] + ".weight"
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lora_pairs[base_key]["B"] = tensor
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return dict(lora_pairs)
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class JinaEmbeddingsV5Model(Qwen3ForCausalLM, VllmModelForPooling):
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"""Jina Embeddings V5 with task-specific LoRA adapters merged at load time.
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Extends Qwen3ForCausalLM (the underlying architecture) and declares itself
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as a pooling model so that as_embedding_model() does not wrap it.
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"""
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is_pooling_model = True
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hf_to_vllm_mapper = Qwen3ForCausalLM.hf_to_vllm_mapper | WeightsMapper(
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orig_to_new_prefix={"": "model."}
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)
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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self._model_name = vllm_config.model_config.model
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self._revision = vllm_config.model_config.revision
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self._task = getattr(
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vllm_config.model_config.hf_config, "jina_task", _DEFAULT_TASK
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)
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if self._task not in _SUPPORTED_TASKS:
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logger.warning(
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"Unknown jina_task=%r. Falling back to %r.",
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self._task,
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_DEFAULT_TASK,
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)
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self._task = _DEFAULT_TASK
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pooler_config = vllm_config.model_config.pooler_config
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assert pooler_config is not None
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self.pooler = DispatchPooler.for_embedding(pooler_config)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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lora_pairs: dict = {}
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scaling = 1.0
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result = _load_adapter(self._model_name, self._task, self._revision)
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if result is None:
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logger.warning(
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"No adapter found for task %r in %r. Loading raw base weights.",
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self._task,
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self._model_name,
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)
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else:
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adapter_config, adapter_weights = result
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scaling = adapter_config["lora_alpha"] / adapter_config["r"]
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lora_pairs = _build_lora_pairs(adapter_weights)
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logger.info(
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"Loaded %d adapter tensors for task %r (scaling=%.4f, %d LoRA pairs)",
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len(adapter_weights),
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self._task,
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scaling,
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len(lora_pairs),
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)
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def _merge_weights(
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weights: Iterable[tuple[str, torch.Tensor]],
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) -> Iterable[tuple[str, torch.Tensor]]:
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for name, tensor in weights:
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clean_name = name
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if clean_name.startswith("model."):
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clean_name = clean_name[len("model.") :]
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if clean_name in lora_pairs:
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pair = lora_pairs[clean_name]
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if "A" in pair and "B" in pair:
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lora_A = pair["A"].to(device=tensor.device, dtype=tensor.dtype)
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lora_B = pair["B"].to(device=tensor.device, dtype=tensor.dtype)
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tensor = tensor + (lora_B @ lora_A) * scaling
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yield name, tensor
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loader = AutoWeightsLoader(self, ignore_unexpected_prefixes=["lm_head."])
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weights = _merge_weights(weights)
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return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
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