forked from EngineX-Cambricon/enginex-mlu370-vllm
141 lines
4.5 KiB
Python
141 lines
4.5 KiB
Python
|
from dataclasses import dataclass
|
|||
|
|
from typing import Dict, List, Optional, Tuple, Type
|
|||
|
|
|
|||
|
|
import openvino as ov
|
|||
|
|
import torch
|
|||
|
|
|
|||
|
|
from vllm.attention.backends.abstract import (AttentionBackend,
|
|||
|
|
AttentionMetadata)
|
|||
|
|
from vllm.attention.backends.utils import CommonAttentionState
|
|||
|
|
from vllm.multimodal import MultiModalPlaceholderMap
|
|||
|
|
|
|||
|
|
|
|||
|
|
def copy_cache_block(src_tensor: ov.Tensor, dst_tensor: ov.Tensor,
|
|||
|
|
src_offset: int, dst_offset: int) -> None:
|
|||
|
|
|
|||
|
|
def create_roi_tensor(
|
|||
|
|
tensor: ov.Tensor,
|
|||
|
|
block_number: int,
|
|||
|
|
) -> ov.Tensor:
|
|||
|
|
roi_begin = ov.runtime.Coordinate([0, 0, 0, 0])
|
|||
|
|
roi_end = ov.runtime.Coordinate(tensor.get_shape())
|
|||
|
|
|
|||
|
|
roi_begin[0] = block_number
|
|||
|
|
roi_end[0] = block_number + 1
|
|||
|
|
|
|||
|
|
if isinstance(tensor, ov.Tensor):
|
|||
|
|
return ov.Tensor(tensor, roi_begin, roi_end)
|
|||
|
|
else:
|
|||
|
|
return ov.RemoteTensor(tensor, roi_begin, roi_end)
|
|||
|
|
|
|||
|
|
src_roi_tensor = \
|
|||
|
|
create_roi_tensor(src_tensor, src_offset)
|
|||
|
|
dst_roi_tensor = \
|
|||
|
|
create_roi_tensor(dst_tensor, dst_offset)
|
|||
|
|
src_roi_tensor.copy_to(dst_roi_tensor)
|
|||
|
|
|
|||
|
|
|
|||
|
|
class OpenVINOAttentionBackend(AttentionBackend):
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_name() -> str:
|
|||
|
|
return "OPENVINO"
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_impl_cls():
|
|||
|
|
# OpenVINO implements PagedAttention as part of the Optimum
|
|||
|
|
# exported model
|
|||
|
|
raise NotImplementedError
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def make_metadata(*args, **kwargs) -> "AttentionMetadata":
|
|||
|
|
raise NotImplementedError
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_state_cls() -> Type["CommonAttentionState"]:
|
|||
|
|
return CommonAttentionState
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def make_openvino_metadata(*args, **kwargs) -> "OpenVINOAttentionMetadata":
|
|||
|
|
return OpenVINOAttentionMetadata(*args, **kwargs)
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_kv_cache_shape(
|
|||
|
|
num_blocks: int,
|
|||
|
|
block_size: int,
|
|||
|
|
num_kv_heads: int,
|
|||
|
|
head_size: int,
|
|||
|
|
) -> Tuple[int, ...]:
|
|||
|
|
return (2, num_blocks, num_kv_heads, block_size, head_size)
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def swap_blocks(
|
|||
|
|
src_tensor: ov.Tensor,
|
|||
|
|
dst_tensor: ov.Tensor,
|
|||
|
|
src_to_dists: List[Tuple[int, int]],
|
|||
|
|
) -> None:
|
|||
|
|
for src, dst in src_to_dists:
|
|||
|
|
copy_cache_block(src_tensor, dst_tensor, src, dst)
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def copy_blocks(
|
|||
|
|
kv_caches: List[Tuple[ov.Tensor, ov.Tensor]],
|
|||
|
|
src_to_dists: List[Tuple[int, int]],
|
|||
|
|
) -> None:
|
|||
|
|
for src, dst in src_to_dists:
|
|||
|
|
for key_cache, value_cache in kv_caches:
|
|||
|
|
copy_cache_block(key_cache, key_cache, src, dst)
|
|||
|
|
copy_cache_block(value_cache, value_cache, src, dst)
|
|||
|
|
|
|||
|
|
|
|||
|
|
@dataclass
|
|||
|
|
class OpenVINOAttentionMetadata:
|
|||
|
|
"""Metadata for OpenVINOAttentionBackend.
|
|||
|
|
|
|||
|
|
Basic terms used below:
|
|||
|
|
- batch_size_in_sequences - total number of sequences to execute
|
|||
|
|
- prompt_lens – per sequence size number of scheduled tokens
|
|||
|
|
- batch_size_in_tokens = sum(prompt_lens)
|
|||
|
|
- max_context_len = max(context_lens)
|
|||
|
|
- max_num_blocks = div_up(max_context_len / BLOCK_SIZE)
|
|||
|
|
- num_blocks – total number of blocks in block_indices
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
# Describes past KV cache size for each sequence within a batch
|
|||
|
|
# Shape: [batch_size_in_sequences]
|
|||
|
|
# Type: i32
|
|||
|
|
past_lens: torch.Tensor
|
|||
|
|
|
|||
|
|
# Describes start indices of input / speculative tokens from
|
|||
|
|
# current sequences within a batch sequence
|
|||
|
|
# Shape: [batch_size_in_sequences + 1]
|
|||
|
|
# Type: i32
|
|||
|
|
subsequence_begins: torch.Tensor
|
|||
|
|
|
|||
|
|
# Describes block tables for each sequence within a batch -
|
|||
|
|
# indices along 0th dimension in key_cache and value_cache inputs
|
|||
|
|
# Shape: [num_blocks]
|
|||
|
|
# Type: i32
|
|||
|
|
block_indices: torch.Tensor
|
|||
|
|
|
|||
|
|
# Describes block tables for each sequence within a batch -
|
|||
|
|
# for i-th element, it is an index in block_indices with the
|
|||
|
|
# first block belonging to i-th sequence
|
|||
|
|
# Shape: [batch_size_in_sequences + 1]
|
|||
|
|
# Type: i32
|
|||
|
|
block_indices_begins: torch.Tensor
|
|||
|
|
|
|||
|
|
# Describes max context length
|
|||
|
|
# Shape: scalar
|
|||
|
|
# Type: i32
|
|||
|
|
max_context_len: torch.Tensor
|
|||
|
|
|
|||
|
|
# The index maps that relate multi-modal embeddings to the corresponding
|
|||
|
|
# placeholders.
|
|||
|
|
#
|
|||
|
|
# N.B. These aren't really related to attention and don't belong on this
|
|||
|
|
# type -- this is just a temporary solution to make them available to
|
|||
|
|
# `model_executable`.
|
|||
|
|
multi_modal_placeholder_index_maps: Optional[Dict[
|
|||
|
|
str, MultiModalPlaceholderMap.IndexMap]]
|