323 lines
11 KiB
Python
323 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from enum import IntEnum
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from typing import List, Optional, 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 typing_extensions import assert_never
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from vllm.config import PoolerConfig
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from vllm.model_executor.pooling_metadata import (PoolingMetadata,
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PoolingTensors)
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from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
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from vllm.transformers_utils.config import (
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get_cross_encoder_activation_function)
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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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class SimplePooler(nn.Module):
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"""A layer that pools specific information from hidden states.
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This layer does the following:
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1. Extracts specific tokens or aggregates data based on pooling method.
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2. Normalizes output if specified.
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3. Returns structured results as `PoolerOutput`.
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Attributes:
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pooling_type: The type of pooling to use.
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normalize: Whether to normalize the pooled data.
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"""
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@staticmethod
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def from_pooling_type(
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pooling_type: PoolingType,
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*,
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normalize: bool,
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softmax: bool,
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step_tag_id: Optional[int] = None,
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returned_token_ids: Optional[List[int]] = None,
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) -> "SimplePooler":
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if pooling_type == PoolingType.LAST:
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assert step_tag_id is None and returned_token_ids is None
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return LastPool(normalize=normalize, softmax=softmax)
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if pooling_type == PoolingType.ALL:
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assert step_tag_id is None and returned_token_ids is None
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return AllPool(normalize=normalize, softmax=softmax)
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if pooling_type == PoolingType.CLS:
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assert step_tag_id is None and returned_token_ids is None
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return CLSPool(normalize=normalize, softmax=softmax)
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if pooling_type == PoolingType.MEAN:
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assert step_tag_id is None and returned_token_ids is None
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return MeanPool(normalize=normalize, softmax=softmax)
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if pooling_type == PoolingType.STEP:
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return StepPool(normalize=normalize,
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softmax=softmax,
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step_tag_id=step_tag_id,
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returned_token_ids=returned_token_ids)
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assert_never(pooling_type)
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def __init__(self, *, normalize: bool, softmax: bool) -> None:
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super().__init__()
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self.head = PoolerHead(normalize=normalize, softmax=softmax)
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def get_prompt_lens(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> torch.Tensor:
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return PoolingTensors.from_pooling_metadata(
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pooling_metadata, hidden_states.device).prompt_lens
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def extract_states(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> Union[list[torch.Tensor], torch.Tensor]:
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raise NotImplementedError
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def build_output(self, data: torch.Tensor) -> PoolingSequenceGroupOutput:
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return PoolingSequenceGroupOutput(data)
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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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) -> PoolerOutput:
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pooled_data = self.extract_states(hidden_states, pooling_metadata)
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pooled_data = self.head(pooled_data)
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pooled_outputs = [self.build_output(data) for data in pooled_data]
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return PoolerOutput(outputs=pooled_outputs)
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class CLSPool(SimplePooler):
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def extract_states(
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self,
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hidden_states: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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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(SimplePooler):
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def extract_states(
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self,
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hidden_states: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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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(SimplePooler):
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def extract_states(
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self,
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hidden_states: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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offset = 0
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pooled_data = list[torch.Tensor]()
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for prompt_len in prompt_lens:
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pooled_data.append(hidden_states[offset:offset + prompt_len])
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offset += prompt_len
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return pooled_data
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class MeanPool(SimplePooler):
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def extract_states(
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self,
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hidden_states: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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cumsum = torch.cumsum(hidden_states, dim=0)
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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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class StepPool(SimplePooler):
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def __init__(
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self,
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*,
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normalize: bool,
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softmax: bool,
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step_tag_id: Optional[int] = None,
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returned_token_ids: Optional[List[int]] = None,
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):
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super().__init__(normalize=normalize, softmax=softmax)
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self.step_tag_id = step_tag_id
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self.returned_token_ids = returned_token_ids
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def extract_states(
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self,
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hidden_states: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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returned_token_ids = self.returned_token_ids
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if returned_token_ids is not None and len(returned_token_ids) > 0:
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hidden_states = hidden_states[:, returned_token_ids]
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step_tag_id = self.step_tag_id
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offset = 0
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pooled_data = list[torch.Tensor]()
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for prompt_len, seq_data_i in zip(prompt_lens,
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pooling_metadata.seq_data.values()):
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pooled_data_i = hidden_states[offset:offset + prompt_len]
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if step_tag_id is not None:
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token_ids = torch.tensor(seq_data_i.prompt_token_ids)
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pooled_data_i = pooled_data_i[token_ids == step_tag_id]
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offset += prompt_len
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pooled_data.append(pooled_data_i)
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return pooled_data
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class PoolerHead(nn.Module):
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def __init__(self, *, normalize: bool, softmax: bool) -> None:
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super().__init__()
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self.normalize = normalize
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self.softmax = softmax
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def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor]):
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if self.normalize:
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if isinstance(pooled_data, list):
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pooled_data = [
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F.normalize(data, p=2, dim=1) for data in pooled_data
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]
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else:
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pooled_data = F.normalize(pooled_data, p=2, dim=1)
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if self.softmax:
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if isinstance(pooled_data, list):
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pooled_data = [F.softmax(data, dim=-1) for data in pooled_data]
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else:
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pooled_data = F.softmax(pooled_data, dim=-1)
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return pooled_data
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class Pooler(nn.Module):
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@classmethod
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def from_config_with_defaults(
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cls,
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pooler_config: PoolerConfig,
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pooling_type: PoolingType,
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normalize: bool,
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softmax: bool,
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step_tag_id: Optional[int] = None,
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returned_token_ids: Optional[List[int]] = None,
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) -> SimplePooler:
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return SimplePooler.from_pooling_type(
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pooling_type=PoolingType[pooler_config.pooling_type]
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if pooler_config.pooling_type is not None else pooling_type,
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normalize=pooler_config.normalize
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if pooler_config.normalize is not None else normalize,
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softmax=pooler_config.softmax
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if pooler_config.softmax is not None else softmax,
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step_tag_id=pooler_config.step_tag_id
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if pooler_config.step_tag_id is not None else step_tag_id,
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returned_token_ids=pooler_config.returned_token_ids
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if pooler_config.returned_token_ids is not None else
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returned_token_ids,
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)
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class CrossEncodingPooler(nn.Module):
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"""A layer that pools specific information from hidden states.
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This layer does the following:
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1. Extracts specific tokens or aggregates data based on pooling method.
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2. Normalizes output if specified.
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3. Returns structured results as `PoolerOutput`.
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Attributes:
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pooling_type: The type of pooling to use.
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normalize: Whether to normalize the pooled data.
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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classifier: nn.Module,
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pooler: Optional[nn.Module] = None,
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):
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super().__init__()
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self.classifier = classifier
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self.pooler = pooler
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self.default_activation_function = \
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get_cross_encoder_activation_function(config)
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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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) -> PoolerOutput:
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"""Pools sentence pair scores from the hidden_states."""
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prompt_lens = PoolingTensors.from_pooling_metadata(
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pooling_metadata, hidden_states.device).prompt_lens
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offset = 0
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pooled_data_lst = []
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for prompt_len in prompt_lens:
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pooled_data_i = hidden_states[offset:offset + prompt_len]
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if self.pooler is not None:
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final_shape_tensor = self.pooler(pooled_data_i)
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else:
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final_shape_tensor = self.classifier(pooled_data_i)
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pooled_data_lst.append(final_shape_tensor)
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offset += prompt_len
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pooled_output = torch.stack(pooled_data_lst)
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if self.pooler is not None:
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# apply classifier once on the full batch if possible
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pooled_output = self.classifier(pooled_output)
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scores = self.default_activation_function(pooled_output).squeeze(-1)
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pooled_outputs = [PoolingSequenceGroupOutput(data) for data in scores]
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return PoolerOutput(outputs=pooled_outputs)
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