83 lines
2.7 KiB
Python
83 lines
2.7 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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import torch
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from vllm.pooling_params import PoolingParams
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from vllm.utils.platform_utils import is_pin_memory_available
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pin_memory = is_pin_memory_available()
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@dataclass
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class PoolingCursor:
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index: list[int]
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first_token_indices_gpu: torch.Tensor
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last_token_indices_gpu: torch.Tensor
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prompt_lens_cpu: torch.Tensor
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num_scheduled_tokens_cpu: torch.Tensor
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def __getitem__(self, indices: slice):
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return PoolingCursor(
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index=self.index[indices],
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first_token_indices_gpu=self.first_token_indices_gpu[indices],
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last_token_indices_gpu=self.last_token_indices_gpu[indices],
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prompt_lens_cpu=self.prompt_lens_cpu[indices],
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num_scheduled_tokens_cpu=self.num_scheduled_tokens_cpu[indices],
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)
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def is_partial_prefill(self):
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return not torch.all(self.prompt_lens_cpu == self.num_scheduled_tokens_cpu)
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@dataclass
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class PoolingMetadata:
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"""Tensors for pooling."""
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prompt_lens: torch.Tensor # CPU Tensor
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prompt_token_ids: torch.Tensor | None
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pooling_params: list[PoolingParams]
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pooling_cursor: PoolingCursor | None = None
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def __getitem__(self, indices: slice):
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return PoolingMetadata(
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prompt_lens=self.prompt_lens[indices],
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prompt_token_ids=None
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if self.prompt_token_ids is None
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else self.prompt_token_ids[indices],
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pooling_params=self.pooling_params[indices],
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pooling_cursor=None
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if self.pooling_cursor is None
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else self.pooling_cursor[indices],
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)
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def build_pooling_cursor(
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self, num_scheduled_tokens: list[int], device: torch.device
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):
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self.pooling_cursor = build_pooling_cursor(
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num_scheduled_tokens, self.prompt_lens, device
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)
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def build_pooling_cursor(
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num_scheduled_tokens: list[int], prompt_lens: torch.Tensor, device: torch.device
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):
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assert len(prompt_lens) == len(num_scheduled_tokens)
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n_seq = len(num_scheduled_tokens)
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index = list(range(n_seq))
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num_scheduled_tokens = torch.tensor(num_scheduled_tokens, device="cpu")
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cumsum = torch.zeros(
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n_seq + 1, dtype=torch.int64, pin_memory=pin_memory, device="cpu"
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)
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torch.cumsum(num_scheduled_tokens, dim=0, out=cumsum[1:])
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cumsum = cumsum.to(device, non_blocking=True)
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return PoolingCursor(
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index=index,
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first_token_indices_gpu=cumsum[:n_seq],
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last_token_indices_gpu=cumsum[1:] - 1,
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prompt_lens_cpu=prompt_lens,
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num_scheduled_tokens_cpu=num_scheduled_tokens,
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)
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