131 lines
4.0 KiB
Python
131 lines
4.0 KiB
Python
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from dataclasses import dataclass
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from typing import List, Tuple, Type
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import openvino as ov
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import torch
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from vllm.attention.backends.abstract import (AttentionBackend,
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AttentionMetadata)
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from vllm.attention.backends.utils import CommonAttentionState
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def copy_cache_block(src_tensor: ov.Tensor, dst_tensor: ov.Tensor,
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src_offset: int, dst_offset: int) -> None:
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def create_roi_tensor(
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tensor: ov.Tensor,
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block_number: int,
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) -> ov.Tensor:
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roi_begin = ov.runtime.Coordinate([0, 0, 0, 0])
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roi_end = ov.runtime.Coordinate(tensor.get_shape())
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roi_begin[0] = block_number
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roi_end[0] = block_number + 1
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if isinstance(tensor, ov.Tensor):
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return ov.Tensor(tensor, roi_begin, roi_end)
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else:
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return ov.RemoteTensor(tensor, roi_begin, roi_end)
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src_roi_tensor = \
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create_roi_tensor(src_tensor, src_offset)
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dst_roi_tensor = \
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create_roi_tensor(dst_tensor, dst_offset)
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src_roi_tensor.copy_to(dst_roi_tensor)
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class OpenVINOAttentionBackend(AttentionBackend):
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@staticmethod
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def get_name() -> str:
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return "openvino"
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@staticmethod
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def get_impl_cls():
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# OpenVINO implements PagedAttention as part of the Optimum
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# exported model
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raise NotImplementedError
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@staticmethod
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def make_metadata(*args, **kwargs) -> "AttentionMetadata":
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raise NotImplementedError
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@staticmethod
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def get_state_cls() -> Type["CommonAttentionState"]:
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return CommonAttentionState
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@staticmethod
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def make_openvino_metadata(*args, **kwargs) -> "OpenVINOAttentionMetadata":
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return OpenVINOAttentionMetadata(*args, **kwargs)
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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return (2, num_blocks, num_kv_heads, block_size, head_size)
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@staticmethod
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def swap_blocks(
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src_tensor: ov.Tensor,
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dst_tensor: ov.Tensor,
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src_to_dists: List[Tuple[int, int]],
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) -> None:
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for src, dst in src_to_dists:
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copy_cache_block(src_tensor, dst_tensor, src, dst)
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@staticmethod
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def copy_blocks(
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kv_caches: List[Tuple[ov.Tensor, ov.Tensor]],
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src_to_dists: List[Tuple[int, int]],
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) -> None:
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for src, dst in src_to_dists:
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for key_cache, value_cache in kv_caches:
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copy_cache_block(key_cache, key_cache, src, dst)
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copy_cache_block(value_cache, value_cache, src, dst)
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@dataclass
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class OpenVINOAttentionMetadata:
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"""Metadata for OpenVINOAttentionBackend.
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Basic terms used below:
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- batch_size_in_sequences - total number of sequences to execute
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- prompt_lens – per sequence size number of scheduled tokens
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- batch_size_in_tokens = sum(prompt_lens)
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- max_context_len = max(context_lens)
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- max_num_blocks = div_up(max_context_len / BLOCK_SIZE)
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- num_blocks – total number of blocks in block_indices
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"""
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# Describes past KV cache size for each sequence within a batch
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# Shape: [batch_size_in_sequences]
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# Type: i32
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past_lens: torch.Tensor
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# Describes start indices of input / speculative tokens from
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# current sequences within a batch sequence
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# Shape: [batch_size_in_sequences + 1]
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# Type: i32
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subsequence_begins: torch.Tensor
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# Describes block tables for each sequence within a batch -
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# indices along 0th dimension in key_cache and value_cache inputs
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# Shape: [num_blocks]
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# Type: i32
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block_indices: torch.Tensor
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# Describes block tables for each sequence within a batch -
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# for i-th element, it is an index in block_indices with the
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# first block belonging to i-th sequence
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# Shape: [batch_size_in_sequences + 1]
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# Type: i32
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block_indices_begins: torch.Tensor
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# Describes max context length
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# Shape: scalar
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# Type: i32
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max_context_len: torch.Tensor
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