474 lines
17 KiB
Python
474 lines
17 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 enum import IntEnum
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from typing import 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 typing_extensions import assert_never
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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.sequence import PoolerOutput, PoolingSequenceGroupOutput
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from vllm.transformers_utils.config import (
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get_classification_activation_function,
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get_cross_encoder_activation_function)
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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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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: 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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assert isinstance(hidden_states, 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: 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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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: Union[torch.Tensor, list[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, pooling_metadata)
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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: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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if isinstance(hidden_states, list):
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result = []
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for req_state, prompt_len in zip(hidden_states, prompt_lens):
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assert prompt_len == req_state.shape[0], \
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"partial prefill not supported with CLS pooling"
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result.append(req_state[0])
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return result
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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: 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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if isinstance(hidden_states, list):
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return [h[-1] for h in hidden_states]
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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: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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if isinstance(hidden_states, list):
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for req_state, prompt_len in zip(hidden_states, prompt_lens):
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assert prompt_len == req_state.shape[0], \
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"partial prefill not supported with ALL pooling"
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return hidden_states
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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: 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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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if isinstance(hidden_states, list):
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result = []
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for req_state, prompt_len in zip(hidden_states, prompt_lens):
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assert prompt_len == req_state.shape[0], \
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"partial prefill not supported with mean pooling"
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result.append(torch.mean(req_state, dim=0,
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dtype=torch.float32))
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return result
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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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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 get_prompt_token_ids(
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self,
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pooling_metadata: PoolingMetadata,
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) -> list[torch.Tensor]:
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if isinstance(pooling_metadata, V1PoolingMetadata):
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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 extract_states(
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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 = self.get_prompt_lens(hidden_states, pooling_metadata)
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prompt_token_ids = self.get_prompt_token_ids(pooling_metadata)
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pooled_data_lst = list[torch.Tensor]()
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if isinstance(hidden_states, list):
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for req_state, prompt_len in zip(hidden_states, prompt_lens):
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assert prompt_len == req_state.shape[0], \
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"partial prefill not supported with step pooling"
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pooled_data_lst = hidden_states
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else:
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offset = 0
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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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offset += prompt_len
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pooled_data_lst.append(pooled_data_i)
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pooled_data = list[torch.Tensor]()
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returned_token_ids = self.returned_token_ids
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step_tag_id = self.step_tag_id
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for data, token_id in zip(pooled_data_lst, prompt_token_ids):
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if returned_token_ids is not None and len(returned_token_ids) > 0:
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data = data[:, returned_token_ids]
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if step_tag_id is not None:
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data = data[token_id == step_tag_id]
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pooled_data.append(data)
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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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pooling_metadata: PoolingMetadata):
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# Using float32 in PoolerHead
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if isinstance(pooled_data, list):
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for i in range(len(pooled_data)):
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pooled_data[i] = pooled_data[i].to(torch.float32)
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else:
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pooled_data = pooled_data.to(torch.float32)
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# for matryoshka representation
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if isinstance(pooling_metadata, V0PoolingMetadata):
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dimensions_list = [
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pooling_param.dimensions
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for _, pooling_param in pooling_metadata.seq_groups
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]
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else:
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assert isinstance(pooled_data, list)
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dimensions_list = [
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pooling_param.dimensions
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for pooling_param in pooling_metadata.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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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 = [
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F.softmax(data, dim=-1)
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if data.shape[-1] >= 2 else F.sigmoid(data)
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for data in pooled_data
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]
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else:
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if pooled_data.shape[-1] >= 2:
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pooled_data = F.softmax(pooled_data, dim=-1)
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else:
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pooled_data = F.sigmoid(pooled_data)
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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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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 ClassifierPooler(nn.Module):
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"""A pooling layer for classification tasks.
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This layer does the following:
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1. Applies a classification layer to the hidden states.
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2. Optionally applies a pooler layer.
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3. Applies an activation function to the output. In the case of
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classification models it is either sigmoid or softmax. In the
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case of scoring models, the same behavior is configuration
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dependent, as in the sentence-transformers library.
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"""
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def __init__(
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self,
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config: ModelConfig,
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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.classification_act_fn = get_classification_activation_function(
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config.hf_config)
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self.cross_encoder_act_fn = get_cross_encoder_activation_function(
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config.hf_config)
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def _get_act_fn(self, use_cross_encoder: bool):
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return (self.cross_encoder_act_fn
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if use_cross_encoder else self.classification_act_fn)
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def get_prompt_lens(
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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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) -> torch.Tensor:
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if isinstance(pooling_metadata, V1PoolingMetadata):
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return pooling_metadata.prompt_lens
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assert isinstance(hidden_states, 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 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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) -> PoolerOutput:
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"""Pools sentence pair scores from the hidden_states."""
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prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)
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pooled_data = list[torch.Tensor]()
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if isinstance(hidden_states, list):
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for req_state, prompt_len in zip(hidden_states, prompt_lens):
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assert prompt_len == req_state.shape[0], \
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"partial prefill not supported with classifier"
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pooled_data = hidden_states
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else:
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offset = 0
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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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offset += prompt_len
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pooled_data.append(pooled_data_i)
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pooled_data_lst = []
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for pooled_data_i in pooled_data:
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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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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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if isinstance(pooling_metadata, V0PoolingMetadata):
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use_cross_encoder_list = [
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pooling_param.use_cross_encoder
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for _, pooling_param in pooling_metadata.seq_groups
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]
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else:
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use_cross_encoder_list = [
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pooling_param.use_cross_encoder
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for pooling_param in pooling_metadata.pooling_params
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]
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# shape of scores: (batch_size, num_labels)
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if all(use_cross_encoder == use_cross_encoder_list[0]
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for use_cross_encoder in use_cross_encoder_list):
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act_fn = self._get_act_fn(use_cross_encoder_list[0])
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scores = act_fn(pooled_output)
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else:
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scores = torch.stack([
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self._get_act_fn(use_cross_encoder)(vecs)
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for use_cross_encoder, vecs in zip(use_cross_encoder_list,
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pooled_output)
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])
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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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