744 lines
24 KiB
Python
744 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import ABC, abstractmethod
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from collections.abc import Mapping, Set
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from dataclasses import dataclass
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from enum import IntEnum
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from itertools import groupby
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from typing import Callable, Optional, TypeVar, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import PretrainedConfig
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from vllm.config import ModelConfig, PoolerConfig
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from vllm.model_executor.pooling_metadata import ( # noqa: E501
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PoolingMetadata as V0PoolingMetadata)
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from vllm.model_executor.pooling_metadata import PoolingTensors
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from vllm.pooling_params import PoolingParams
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from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
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from vllm.tasks import PoolingTask
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from vllm.utils import resolve_obj_by_qualname
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from vllm.v1.pool.metadata import PoolingMetadata as V1PoolingMetadata
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PoolingMetadata = Union[V0PoolingMetadata, V1PoolingMetadata]
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PoolingFn = Callable[
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[Union[torch.Tensor, list[torch.Tensor]], PoolingMetadata],
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Union[torch.Tensor, list[torch.Tensor]]]
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ClassifierFn = Callable[[torch.Tensor], torch.Tensor]
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class PoolingType(IntEnum):
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"""Enumeration for different types of pooling methods."""
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LAST = 0
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ALL = 1
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CLS = 2
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STEP = 3
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MEAN = 4
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@dataclass(frozen=True)
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class ResolvedPoolingConfig:
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pooling_type: PoolingType
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task: PoolingTask
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@classmethod
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def from_config(
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cls,
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task: PoolingTask,
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pooler_config: PoolerConfig,
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) -> "ResolvedPoolingConfig":
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assert pooler_config.pooling_type is not None
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return cls(task=task,
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pooling_type=PoolingType[pooler_config.pooling_type])
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@dataclass(frozen=True)
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class PoolingParamsUpdate:
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requires_token_ids: bool = False
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"""Set this flag to enable `get_prompt_token_ids` for your pooler."""
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def apply(self, params: PoolingParams) -> None:
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params.requires_token_ids = self.requires_token_ids
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class Pooler(nn.Module, ABC):
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"""The interface required for all poolers used in pooling models in vLLM."""
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@staticmethod
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def for_encode(pooler_config: PoolerConfig):
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if pooler_config.pooling_type == "STEP":
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return StepPooler()
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resolved_config = ResolvedPoolingConfig(task="encode",
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pooling_type=PoolingType.ALL)
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return SimplePooler.from_config(resolved_config)
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@staticmethod
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def for_embed(pooler_config: PoolerConfig):
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resolved_config = ResolvedPoolingConfig.from_config(
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task="embed",
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pooler_config=pooler_config,
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)
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return SimplePooler.from_config(resolved_config)
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@staticmethod
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def for_classify(
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pooler_config: PoolerConfig,
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classifier: Optional[ClassifierFn],
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):
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resolved_config = ResolvedPoolingConfig.from_config(
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task="classify",
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pooler_config=pooler_config,
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)
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pooling = PoolingMethod.from_pooling_type(resolved_config.pooling_type)
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return ClassifierPooler(
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pooling=pooling,
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classifier=classifier,
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)
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@abstractmethod
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def get_supported_tasks(self) -> Set[PoolingTask]:
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"""Determine which pooling tasks are supported."""
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raise NotImplementedError
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def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
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"""
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Construct the updated pooling parameters to use for a supported task.
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"""
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return PoolingParamsUpdate()
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@abstractmethod
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def forward(
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self,
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hidden_states: Union[list[torch.Tensor], torch.Tensor],
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pooling_metadata: PoolingMetadata,
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) -> PoolerOutput:
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raise NotImplementedError
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def get_prompt_lens(
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hidden_states: Union[torch.Tensor, list[torch.Tensor]],
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pooling_metadata: PoolingMetadata,
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) -> torch.Tensor:
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if isinstance(pooling_metadata, V1PoolingMetadata):
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return pooling_metadata.prompt_lens
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return PoolingTensors.from_pooling_metadata(
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pooling_metadata, hidden_states[0].device).prompt_lens
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def get_prompt_token_ids(
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pooling_metadata: PoolingMetadata) -> list[torch.Tensor]:
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if isinstance(pooling_metadata, V1PoolingMetadata):
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assert pooling_metadata.prompt_token_ids is not None, (
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"Please set `requires_token_ids=True` in `get_pooling_updates`")
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return [
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pooling_metadata.prompt_token_ids[i, :num]
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for i, num in enumerate(pooling_metadata.prompt_lens)
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]
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return [
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torch.tensor(seq_data_i.prompt_token_ids)
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for seq_data_i in pooling_metadata.seq_data.values()
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]
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def get_pooling_params(
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pooling_metadata: PoolingMetadata) -> list[PoolingParams]:
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if isinstance(pooling_metadata, V0PoolingMetadata):
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pooling_params = [p for _, p in pooling_metadata.seq_groups]
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else:
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pooling_params = pooling_metadata.pooling_params
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return pooling_params
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def get_tasks(pooling_metadata: PoolingMetadata) -> list[PoolingTask]:
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pooling_params = get_pooling_params(pooling_metadata)
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tasks: list[PoolingTask] = [
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task for pooling_param in pooling_params
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if (task := pooling_param.task) is not None
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]
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assert len(pooling_params) == len(tasks)
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return tasks
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def get_classification_activation_function(config: PretrainedConfig):
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# Implement alignment with transformers ForSequenceClassificationLoss
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# https://github.com/huggingface/transformers/blob/57bb6db6ee4cfaccc45b8d474dfad5a17811ca60/src/transformers/loss/loss_utils.py#L92
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problem_type = getattr(config, "problem_type", "")
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if problem_type == "regression":
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return PoolerIdentity()
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if problem_type == "single_label_classification":
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return PoolerClassify()
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if problem_type == "multi_label_classification":
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return PoolerMultiLabelClassify()
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return PoolerClassify()
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def get_cross_encoder_activation_function(config: PretrainedConfig):
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function_name: Optional[str] = None
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if (hasattr(config, "sentence_transformers")
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and "activation_fn" in config.sentence_transformers):
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function_name = config.sentence_transformers["activation_fn"]
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elif (hasattr(config, "sbert_ce_default_activation_function")
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and config.sbert_ce_default_activation_function is not None):
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function_name = config.sbert_ce_default_activation_function
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if function_name is not None:
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assert function_name.startswith("torch.nn.modules."), (
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"Loading of activation functions is restricted to "
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"torch.nn.modules for security reasons")
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fn = resolve_obj_by_qualname(function_name)()
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return PoolerActivation.wraps(fn)
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return PoolerScore()
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def build_output(
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all_data: Union[torch.Tensor, list[torch.Tensor]], ) -> PoolerOutput:
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all_outputs = [PoolingSequenceGroupOutput(data) for data in all_data]
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return PoolerOutput(outputs=all_outputs)
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class PoolingMethod(nn.Module, ABC):
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@staticmethod
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def from_pooling_type(pooling_type: PoolingType) -> "PoolingMethod":
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if pooling_type == PoolingType.LAST:
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return LastPool()
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if pooling_type == PoolingType.ALL:
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return AllPool()
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if pooling_type == PoolingType.CLS:
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return CLSPool()
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if pooling_type == PoolingType.MEAN:
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return MeanPool()
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raise NotImplementedError(f"Unsupported method: {pooling_type}")
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@abstractmethod
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def get_supported_tasks(self) -> Set[PoolingTask]:
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raise NotImplementedError
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def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
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return PoolingParamsUpdate()
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@abstractmethod
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def forward_one(
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self,
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hidden_states: torch.Tensor,
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prompt_len: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""
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Note:
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`prompt_len=None` means `prompt_len=len(hidden_states)`.
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"""
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raise NotImplementedError
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@abstractmethod
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def forward_all(
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self,
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hidden_states: torch.Tensor,
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prompt_lens: torch.Tensor,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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raise NotImplementedError
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def forward(
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self,
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hidden_states: Union[torch.Tensor, list[torch.Tensor]],
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pooling_metadata: PoolingMetadata,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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prompt_lens = get_prompt_lens(hidden_states, pooling_metadata)
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if isinstance(hidden_states, list):
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return [
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self.forward_one(h, prompt_len)
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for h, prompt_len in zip(hidden_states, prompt_lens)
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]
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return self.forward_all(hidden_states, prompt_lens)
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class CLSPool(PoolingMethod):
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def get_supported_tasks(self) -> Set[PoolingTask]:
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return {"encode", "embed", "classify", "score"}
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def forward_one(
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self,
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hidden_states: torch.Tensor,
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prompt_len: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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assert prompt_len is None or prompt_len == hidden_states.shape[0], \
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"partial prefill not supported with CLS pooling"
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return hidden_states[0]
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def forward_all(
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self,
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hidden_states: torch.Tensor,
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prompt_lens: torch.Tensor,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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first_token_flat_indices = torch.zeros_like(prompt_lens)
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first_token_flat_indices[1:] += torch.cumsum(prompt_lens, dim=0)[:-1]
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return hidden_states[first_token_flat_indices]
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class LastPool(PoolingMethod):
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def get_supported_tasks(self) -> Set[PoolingTask]:
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return {"encode", "embed", "classify", "score"}
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def forward_one(
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self,
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hidden_states: torch.Tensor,
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prompt_len: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return hidden_states[-1]
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def forward_all(
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self,
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hidden_states: torch.Tensor,
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prompt_lens: torch.Tensor,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
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return hidden_states[last_token_flat_indices]
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class AllPool(PoolingMethod):
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def get_supported_tasks(self) -> Set[PoolingTask]:
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return {"encode"}
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def forward_one(
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self,
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hidden_states: torch.Tensor,
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prompt_len: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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assert prompt_len is None or prompt_len == hidden_states.shape[0], \
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"partial prefill not supported with ALL pooling"
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return hidden_states
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def forward_all(
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self,
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hidden_states: torch.Tensor,
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prompt_lens: torch.Tensor,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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return list(hidden_states.split_with_sizes(prompt_lens.tolist()))
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class MeanPool(PoolingMethod):
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def get_supported_tasks(self) -> Set[PoolingTask]:
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return {"encode", "embed", "classify", "score"}
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def forward_one(
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self,
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hidden_states: torch.Tensor,
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prompt_len: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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assert prompt_len is None or prompt_len == hidden_states.shape[0], \
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"partial prefill not supported with MEAN pooling"
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return hidden_states.mean(dim=0, dtype=torch.float32)
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def forward_all(
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self,
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hidden_states: torch.Tensor,
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prompt_lens: torch.Tensor,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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# Use float32 for torch.cumsum in MeanPool,
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# otherwise precision will be lost significantly.
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cumsum = torch.cumsum(hidden_states, dim=0, dtype=torch.float32)
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start_indices = torch.cat([
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torch.tensor([0], device=hidden_states.device),
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torch.cumsum(prompt_lens[:-1], dim=0)
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])
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end_indices = torch.cumsum(prompt_lens, dim=0)
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return (cumsum[end_indices - 1] - cumsum[start_indices] +
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hidden_states[start_indices]) / prompt_lens.unsqueeze(1)
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_T = TypeVar("_T", torch.Tensor, list[torch.Tensor])
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class BasePoolerActivation(nn.Module, ABC):
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@abstractmethod
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def forward(self, pooled_data: _T) -> _T:
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# shape:
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# classify (& score) -> (batch_size, num_classes)
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# embed -> (batch_size, embedding_dim) or list(embedding_dim)
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# (batch_size, dimensions) or list(dimensions) if using MRL
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raise NotImplementedError
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class PoolerActivation(BasePoolerActivation):
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@staticmethod
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def wraps(module: nn.Module):
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if isinstance(module, nn.Identity):
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return PoolerIdentity()
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if isinstance(module, (nn.Sigmoid, nn.Softmax)):
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return PoolerClassify()
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return LambdaPoolerActivation(module)
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@abstractmethod
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError
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def forward(self, pooled_data: _T) -> _T:
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if isinstance(pooled_data, list):
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return [self.forward_chunk(data) for data in pooled_data]
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return self.forward_chunk(pooled_data)
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class PoolerIdentity(PoolerActivation):
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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return pooled_data
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class PoolerNormalize(PoolerActivation):
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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x = F.normalize(pooled_data.float(), p=2, dim=-1)
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return x.to(pooled_data.dtype)
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class PoolerMultiLabelClassify(PoolerActivation):
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
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class PoolerClassify(PoolerActivation):
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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num_labels = pooled_data.shape[-1]
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if num_labels < 2:
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return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
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return F.softmax(pooled_data.float(), dim=-1).to(pooled_data.dtype)
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class PoolerScore(PoolerActivation):
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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num_labels = pooled_data.shape[-1]
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if num_labels < 2:
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return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
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return pooled_data
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class LambdaPoolerActivation(PoolerActivation):
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def __init__(self, fn: Callable[[torch.Tensor], torch.Tensor]):
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super().__init__()
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self.fn = fn
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def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
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return self.fn(pooled_data)
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class PoolerHead(nn.Module):
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def __init__(self, activation: PoolerActivation) -> None:
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super().__init__()
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self.activation = activation
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def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
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pooling_metadata: PoolingMetadata):
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return self.activation(pooled_data)
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class EmbeddingPoolerHead(PoolerHead):
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def __init__(self) -> None:
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super().__init__(activation=PoolerNormalize())
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def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
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pooling_metadata: PoolingMetadata):
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pooling_params = get_pooling_params(pooling_metadata)
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# for matryoshka representation
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dimensions_list = [
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pooling_param.dimensions for pooling_param in pooling_params
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]
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if any(d is not None for d in dimensions_list):
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# change the output dimension
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assert len(pooled_data) == len(dimensions_list)
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if len(set(dimensions_list)) == 1 and not isinstance(
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pooled_data, list):
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# if all dimensions are the same
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d = dimensions_list[0]
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pooled_data = pooled_data[..., :d]
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else:
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pooled_data = [
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vecs if d is None else vecs[..., :d]
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for vecs, d in zip(pooled_data, dimensions_list)
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]
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# for normalize
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flags = [p.normalize for p in pooling_params]
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if len(set(flags)) == 1:
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if flags[0]:
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pooled_data = self.activation(pooled_data)
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else:
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pooled_data = [
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self.activation(vecs) if f else vecs
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for vecs, f in zip(pooled_data, flags)
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]
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return pooled_data
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class RewardPoolerHead(PoolerHead):
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def __init__(self) -> None:
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super().__init__(activation=PoolerClassify())
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def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
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pooling_metadata: PoolingMetadata):
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pooling_params = get_pooling_params(pooling_metadata)
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# for softmax
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flags = [p.softmax for p in pooling_params]
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if len(set(flags)) == 1:
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if flags[0]:
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pooled_data = self.activation(pooled_data)
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else:
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pooled_data = [
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self.activation(vecs) if f else vecs
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for vecs, f in zip(pooled_data, flags)
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]
|
|
|
|
return pooled_data
|
|
|
|
|
|
class SimplePooler(Pooler):
|
|
"""A layer that pools specific information from hidden states.
|
|
|
|
This layer does the following:
|
|
1. Extracts specific tokens or aggregates data based on pooling method.
|
|
2. Normalizes output if specified.
|
|
3. Returns structured results as `PoolerOutput`.
|
|
"""
|
|
|
|
@classmethod
|
|
def from_config(
|
|
cls,
|
|
pooler_config: ResolvedPoolingConfig,
|
|
) -> "SimplePooler":
|
|
pooling = PoolingMethod.from_pooling_type(pooler_config.pooling_type)
|
|
if pooler_config.task == "embed":
|
|
head = EmbeddingPoolerHead()
|
|
elif pooler_config.task == "encode":
|
|
head = RewardPoolerHead()
|
|
else:
|
|
raise NotImplementedError(f"Unknown task: {pooler_config.task}")
|
|
return cls(pooling, head)
|
|
|
|
def __init__(self, pooling: PoolingMethod, head: PoolerHead) -> None:
|
|
super().__init__()
|
|
|
|
self.pooling = pooling
|
|
self.head = head
|
|
|
|
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 forward(
|
|
self,
|
|
hidden_states: Union[torch.Tensor, list[torch.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
pooled_data = self.pooling(hidden_states, pooling_metadata)
|
|
pooled_data = self.head(pooled_data, pooling_metadata)
|
|
return build_output(pooled_data)
|
|
|
|
|
|
class StepPooler(Pooler):
|
|
|
|
def __init__(self, ) -> None:
|
|
super().__init__()
|
|
|
|
self.pooling = AllPool()
|
|
self.head = RewardPoolerHead()
|
|
|
|
def extract_states(
|
|
self,
|
|
hidden_states: Union[torch.Tensor, list[torch.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> Union[list[torch.Tensor], torch.Tensor]:
|
|
pooled_data_lst = self.pooling(hidden_states, pooling_metadata)
|
|
prompt_token_ids = get_prompt_token_ids(pooling_metadata)
|
|
|
|
pooled_data = list[torch.Tensor]()
|
|
|
|
pooling_params = get_pooling_params(pooling_metadata)
|
|
|
|
for data, token_id, pooling_param in zip(pooled_data_lst,
|
|
prompt_token_ids,
|
|
pooling_params):
|
|
step_tag_id = pooling_param.step_tag_id
|
|
returned_token_ids = pooling_param.returned_token_ids
|
|
|
|
if returned_token_ids is not None and len(returned_token_ids) > 0:
|
|
data = data[:, returned_token_ids]
|
|
|
|
if step_tag_id is not None:
|
|
data = data[token_id == step_tag_id]
|
|
pooled_data.append(data)
|
|
|
|
return pooled_data
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"encode"}
|
|
|
|
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
|
return PoolingParamsUpdate(requires_token_ids=True)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[torch.Tensor, list[torch.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
pooled_data = self.extract_states(hidden_states, pooling_metadata)
|
|
pooled_data = self.head(pooled_data, pooling_metadata)
|
|
return build_output(pooled_data)
|
|
|
|
|
|
class ClassifierPooler(Pooler):
|
|
"""A pooling layer for classification tasks.
|
|
|
|
This layer does the following:
|
|
1. Applies a classification layer to the hidden states.
|
|
2. Optionally applies a pooler layer.
|
|
3. Applies an activation function to the output.
|
|
"""
|
|
|
|
@staticmethod
|
|
def act_fn_for_seq_cls(config: ModelConfig):
|
|
return get_classification_activation_function(config.hf_config)
|
|
|
|
@staticmethod
|
|
def act_fn_for_cross_encoder(config: ModelConfig):
|
|
return get_cross_encoder_activation_function(config.hf_config)
|
|
|
|
def __init__(
|
|
self,
|
|
pooling: PoolingFn,
|
|
classifier: Optional[ClassifierFn],
|
|
act_fn: Optional[PoolerActivation] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.pooling = pooling
|
|
self.classifier = classifier
|
|
self.act_fn = act_fn or PoolerClassify()
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return {"classify", "score"}
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[torch.Tensor, list[torch.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
pooled_data = self.pooling(hidden_states, pooling_metadata)
|
|
|
|
if self.classifier is not None:
|
|
# apply classifier once on the full batch if possible
|
|
if isinstance(pooled_data, torch.Tensor):
|
|
pooled_data = self.classifier(pooled_data)
|
|
elif len({data.shape for data in pooled_data}) <= 1:
|
|
pooled_data = self.classifier(torch.stack(pooled_data))
|
|
else:
|
|
pooled_data = [self.classifier(data) for data in pooled_data]
|
|
|
|
pooling_params = get_pooling_params(pooling_metadata)
|
|
flags = [p.activation for p in pooling_params]
|
|
|
|
if len(set(flags)) == 1:
|
|
scores = self.act_fn(pooled_data) if flags[0] else pooled_data
|
|
else:
|
|
scores = [
|
|
self.act_fn(vecs) if f else vecs
|
|
for vecs, f in zip(pooled_data, flags)
|
|
]
|
|
|
|
return build_output(scores)
|
|
|
|
|
|
class DispatchPooler(Pooler):
|
|
"""Dispatches calls to a sub-pooler based on the pooling task."""
|
|
|
|
def __init__(self, poolers_by_task: Mapping[PoolingTask, Pooler]) -> None:
|
|
super().__init__()
|
|
|
|
for task, pooler in poolers_by_task.items():
|
|
if task not in pooler.get_supported_tasks():
|
|
raise ValueError(
|
|
f"{pooler=} does not support {task=}. "
|
|
f"Supported tasks: {pooler.get_supported_tasks()}")
|
|
|
|
self.poolers_by_task = poolers_by_task
|
|
|
|
def get_supported_tasks(self) -> Set[PoolingTask]:
|
|
return set(self.poolers_by_task)
|
|
|
|
def get_pooling_updates(self, task: PoolingTask) -> PoolingParamsUpdate:
|
|
return self.poolers_by_task[task].get_pooling_updates(task)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Union[torch.Tensor, list[torch.Tensor]],
|
|
pooling_metadata: PoolingMetadata,
|
|
) -> PoolerOutput:
|
|
poolers_by_task = self.poolers_by_task
|
|
|
|
if isinstance(hidden_states, list):
|
|
hidden_states_lst = hidden_states
|
|
else:
|
|
prompt_lens = get_prompt_lens(hidden_states, pooling_metadata)
|
|
hidden_states_lst = list(hidden_states.split(prompt_lens.tolist()))
|
|
|
|
outputs = list[PoolingSequenceGroupOutput]()
|
|
offset = 0
|
|
for task, group in groupby(get_tasks(pooling_metadata)):
|
|
if not (pooler := poolers_by_task.get(task)):
|
|
raise ValueError(
|
|
f"Unsupported task: {task} "
|
|
f"Supported tasks: {self.get_supported_tasks()}")
|
|
|
|
num_items = len(list(group))
|
|
group_output: PoolerOutput = pooler(
|
|
hidden_states_lst[offset:offset + num_items],
|
|
pooling_metadata[offset:offset + num_items],
|
|
)
|
|
|
|
outputs.extend(group_output.outputs)
|
|
offset += num_items
|
|
|
|
return PoolerOutput(outputs)
|