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80 lines
2.3 KiB
Python
80 lines
2.3 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 dataclasses import dataclass
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from typing import Any
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import torch
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from vllm.pooling_params import PoolingParams
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from vllm.utils import is_pin_memory_available
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class PoolingMetadata:
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"""Metadata for pooling operations in the Pooler layer.
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This class holds the necessary information for pooling operations,
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providing context for how to perform pooling and other related operations.
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Attributes:
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seq_groups: List of (seq_ids, pooling_params).
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seq_data: A mapping of sequence ID to additional sequence data.
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prompt_lens: List of the lengths of each prompt.
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"""
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def __init__(
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self,
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seq_groups: list[tuple[list[int], PoolingParams]],
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seq_data: dict[int, Any], # Specific data related to sequences
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prompt_lens: list[int],
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) -> None:
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self.seq_groups = seq_groups
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self.seq_data = seq_data
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self.prompt_lens = prompt_lens
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def __repr__(self) -> str:
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return ("PoolingMetadata("
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f"seq_groups={self.seq_groups}, "
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f"seq_data={self.seq_data}, "
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f"prompt_lens={self.prompt_lens})")
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def __getitem__(self, indices: slice):
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return PoolingMetadata(
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seq_groups=self.seq_groups[indices],
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seq_data=dict(list(self.seq_data.items())[indices]),
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prompt_lens=self.prompt_lens[indices],
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)
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@dataclass
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class PoolingTensors:
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"""Tensors for pooling."""
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prompt_lens: torch.Tensor
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@classmethod
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def from_pooling_metadata(
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cls,
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pooling_metadata: "PoolingMetadata",
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device: torch.device,
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) -> "PoolingTensors":
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"""
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Create PoolingTensors from PoolingMetadata.
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Args:
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pooling_metadata: PoolingMetadata instance to convert.
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device: Device to store the tensors.
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"""
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# Convert prompt lengths to tensor
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pin_memory = is_pin_memory_available()
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prompt_lens_t = torch.tensor(
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pooling_metadata.prompt_lens,
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device="cpu",
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dtype=torch.long,
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pin_memory=pin_memory,
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)
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return cls(prompt_lens=prompt_lens_t.to(device=device,
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non_blocking=True), )
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